Examples

All the current data and python files for the examples in the main episimmer repository exists here. Do note that due to the large number of files, we have limited each file’s length to 200 lines.

Warning

This page is created only for reference to the examples. Please do not copy them and try running them in your simulation until you are sure of what you are doing. Copying them may lead to errors due to the files being incomplete. Go to the examples/ directory in the Episimmer repository to get the complete files.

Basic_Disease_Models

The examples in this directory shows the basic models that the user can implement. Episimmer currently provides two kinds of disease models namely, Stochastic and Scheduled models. Both these modelling types are variants of the well known Compartmental Models that exist today. Some of them implemented here are the SIR, SEYAR, SIRID models. For further details on these models refer to Tutorial 2.1.

To run an example :

            cd examples/Basic_Disease_Models
            python ../../episimmer/main.py Stochastic_SIR

---

Example_1

This example uses the Stochastic model comprising of the states- Susceptible, Exposed, Asymptomatic, Symptomatic and Recovered.

It represents a university environment where the agents like students, teachers, visitors, etc. receive or contribute to infection through interactions and events laid out across different campus locations.

Interactions as well as events have been defined for the weekdays and weekends from Monday through Sunday.

Episimmer uses this example to study disease spread using a real-life scenario (university).

The specificity of this example comes from defining interactions between agents as well as the events that each agent was part of for every single day throughout the week.

It shows how the probability of getting infected by a single interaction is also dependent on additional parameters such as time of contact and intensity of interaction (these parameters are passed by the user). This is how infection spread can be influenced by introducing factors that are characteristic to the way the agents of the population interact with one another.

This introduces more flexibily in terms of implementation and the university can use this opportunity to make better decisions in the fight against COVID or any other disease/infection breakout.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import math

import episimmer.model as model


#User defined functions
#This function is user defined, based on the parameters the user has inputed in agents file and interaction/contact file
#This function represents the probability of getting infected during a single interaction/contact
def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):

	p_inf_symp,p_inf_asymp=p_infected_states_list[0],p_infected_states_list[1]
	#EXAMPLE 1
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_asymp
	else:
		return 0

	#Example 2
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval'])*float(c_dict['Intensity']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval'])*float(c_dict['Intensity']))*p_inf_asymp
	else:
		return 0

def event_contribute_fn(agent,event_info,location,current_time_step):
	#Example 1
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.3
	else:
		return 0

	#Example 2
	susceptibility=1
	if agent.info['HLA Type']=='A':
		susceptibility=0.9

	if agent.state=='Symptomatic':
		return math.tanh(float(event_info['Time Interval']))*(1-location.info['Ventilation'])*susceptibility
	elif agent.state=='Asymptomatic':
		return 0.3*math.tanh(float(event_info['Time Interval']))*(1-location.info['Ventilation'])*susceptibility

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	p=math.tanh(ambient_infection*0.3)
	return p




class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0.01,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection(probability_of_infection_fn, [0.3,0.1]))
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.2))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.2))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.csv

Agent Index,Type,Residence,HLA Type
0,Staff,Dorm B,C
1,Student,Teacher Dorm,C
2,Visitor,Dorm B,A
3,Student,Teacher Dorm,C
4,Visitor,Dorm A,A
5,Administration,Dorm A,B
6,Teacher,Teacher Dorm,C
7,Teacher,Teacher Dorm,C
8,Visitor,Dorm B,C
9,Visitor,Dorm B,C
10,Visitor,Outside,C
11,Administration,Dorm A,C
12,Student,Outside,A
13,Visitor,Outside,B
14,Administration,Dorm A,B
15,Student,Outside,C
16,Student,Teacher Dorm,A
17,Student,Dorm A,C
18,Teacher,Teacher Dorm,A
19,Visitor,Dorm A,B
20,Student,Dorm A,A
21,Administration,Outside,C
22,Visitor,Dorm B,A
23,Staff,Dorm A,B
24,Visitor,Dorm B,A
25,Staff,Teacher Dorm,C
26,Teacher,Teacher Dorm,B
27,Student,Teacher Dorm,A
28,Teacher,Outside,C
29,Administration,Outside,A
30,Administration,Dorm B,A
31,Teacher,Dorm B,A
32,Teacher,Outside,A
33,Teacher,Dorm A,C
34,Staff,Outside,B
35,Student,Dorm B,C
36,Visitor,Outside,C
37,Staff,Dorm A,C
38,Staff,Teacher Dorm,B
39,Student,Dorm A,C
40,Administration,Outside,C
41,Teacher,Teacher Dorm,A
42,Visitor,Dorm A,C
43,Administration,Teacher Dorm,C
44,Administration,Dorm A,B
45,Administration,Dorm B,C
46,Student,Dorm A,B
47,Visitor,Dorm B,B
48,Staff,Dorm A,A
49,Student,Outside,B
50,Visitor,Dorm B,C
51,Student,Teacher Dorm,B
52,Teacher,Teacher Dorm,B
53,Student,Outside,A
54,Administration,Dorm A,A
55,Teacher,Outside,C
56,Teacher,Dorm A,A
57,Visitor,Dorm A,A
58,Visitor,Dorm A,B
59,Staff,Dorm A,A
60,Staff,Dorm A,A
61,Teacher,Teacher Dorm,B
62,Teacher,Dorm B,C
63,Staff,Dorm A,B
64,Visitor,Dorm B,C
65,Staff,Outside,C
66,Administration,Dorm A,B
67,Administration,Outside,A
68,Teacher,Teacher Dorm,B
69,Student,Outside,A
70,Staff,Dorm B,C
71,Visitor,Outside,A
72,Staff,Dorm B,A
73,Student,Outside,B
74,Administration,Outside,C
75,Teacher,Dorm B,A
76,Administration,Outside,A
77,Teacher,Teacher Dorm,C
78,Teacher,Teacher Dorm,B
79,Staff,Outside,A
80,Student,Dorm A,C
81,Visitor,Outside,B
82,Administration,Outside,C
83,Teacher,Dorm B,C
84,Staff,Teacher Dorm,C
85,Student,Dorm B,B
86,Teacher,Dorm B,C
87,Teacher,Dorm A,A
88,Staff,Teacher Dorm,B
89,Visitor,Dorm B,C
90,Administration,Teacher Dorm,B
91,Visitor,Dorm B,C
92,Student,Teacher Dorm,C
93,Visitor,Teacher Dorm,B
94,Administration,Outside,B
95,Administration,Teacher Dorm,C
96,Administration,Dorm B,A
97,Staff,Dorm B,C
98,Visitor,Teacher Dorm,B
99,Teacher,Dorm B,B

agents.txt

100
Agent Index:Type:Residence:HLA Type
0:Visitor:Outside:B
1:Visitor:Teacher Dorm:C
2:Visitor:Outside:C
3:Staff:Teacher Dorm:A
4:Student:Dorm A:A
5:Visitor:Dorm A:B
6:Administration:Teacher Dorm:C
7:Staff:Teacher Dorm:C
8:Staff:Outside:A
9:Teacher:Dorm B:B
10:Visitor:Teacher Dorm:C
11:Administration:Outside:B
12:Visitor:Outside:C
13:Staff:Outside:B
14:Staff:Dorm A:B
15:Visitor:Teacher Dorm:B
16:Staff:Outside:A
17:Staff:Dorm A:B
18:Teacher:Dorm B:C
19:Staff:Dorm A:A
20:Visitor:Outside:B
21:Student:Teacher Dorm:B
22:Teacher:Teacher Dorm:C
23:Staff:Dorm A:C
24:Visitor:Dorm B:B
25:Visitor:Outside:C
26:Visitor:Outside:B
27:Staff:Outside:C
28:Teacher:Dorm A:C
29:Student:Dorm A:B
30:Staff:Dorm A:A
31:Student:Teacher Dorm:C
32:Administration:Teacher Dorm:A
33:Teacher:Dorm A:A
34:Administration:Dorm A:C
35:Teacher:Dorm B:B
36:Student:Dorm A:A
37:Administration:Dorm A:A
38:Teacher:Outside:B
39:Teacher:Dorm B:B
40:Administration:Teacher Dorm:A
41:Staff:Teacher Dorm:A
42:Teacher:Outside:B
43:Administration:Teacher Dorm:B
44:Teacher:Outside:C
45:Visitor:Outside:A
46:Visitor:Teacher Dorm:A
47:Administration:Teacher Dorm:A
48:Staff:Dorm A:A
49:Student:Outside:B
50:Staff:Teacher Dorm:B
51:Administration:Outside:C
52:Student:Dorm B:C
53:Administration:Dorm A:C
54:Administration:Outside:A
55:Administration:Outside:C
56:Administration:Dorm A:C
57:Visitor:Outside:B
58:Teacher:Outside:A
59:Staff:Outside:A
60:Administration:Teacher Dorm:A
61:Student:Teacher Dorm:A
62:Student:Dorm B:A
63:Teacher:Dorm A:C
64:Staff:Dorm A:A
65:Student:Teacher Dorm:C
66:Teacher:Outside:C
67:Staff:Dorm B:B
68:Administration:Teacher Dorm:B
69:Teacher:Teacher Dorm:C
70:Administration:Teacher Dorm:C
71:Visitor:Outside:B
72:Visitor:Teacher Dorm:C
73:Visitor:Teacher Dorm:C
74:Administration:Dorm A:C
75:Student:Outside:C
76:Visitor:Outside:C
77:Student:Teacher Dorm:A
78:Teacher:Dorm A:B
79:Visitor:Dorm B:C
80:Administration:Outside:C
81:Student:Dorm B:A
82:Visitor:Dorm B:B
83:Administration:Outside:A
84:Student:Teacher Dorm:A
85:Student:Dorm A:C
86:Staff:Dorm B:A
87:Administration:Teacher Dorm:B
88:Administration:Dorm A:A
89:Teacher:Dorm A:C
90:Administration:Dorm A:B
91:Student:Outside:C
92:Student:Dorm A:B
93:Teacher:Teacher Dorm:C
94:Student:Outside:C
95:Administration:Teacher Dorm:C
96:Staff:Outside:B
97:Staff:Dorm A:C
98:Teacher:Outside:A
99:Administration:Dorm A:B

config.txt

Random Seed <3>
Number of worlds <3>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type:Residence:HLA Type>
Agent list filename <agents.csv>
Interaction Info Keys <Agent Index:Interacting Agent Index:Time Interval:Intensity>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index:Type:Ventilation:Roomsize:Capacity>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents:Time Interval>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<monday_events.txt>
<tuesday_events.txt>
<wednesday_events.txt>
<thursday_events.txt>
<friday_events.txt>
<saturday_events.txt>
<sunday_events.txt>

friday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
96,57,5.004142877127,0.42191921620925354
54,27,5.641672551963007,0.7462919078948316
25,8,1.794290991869778,0.8194479950290448
48,12,0.22317783046829742,0.5930024147218874
32,1,2.0332990080122935,0.9049495038090604
79,4,5.349489905495666,0.25834688605446654
16,19,7.482148590293117,0.8597260125619213
1,84,1.643656099054901,0.6492400176694979
91,92,7.539420185381803,0.1572574957436378
0,18,0.412793980070838,0.12359152155948494
27,4,2.1036845816262373,0.7556071327228134
44,93,0.5338106410878762,0.3250995579281485
7,85,2.700768270414009,0.6396363196460858
25,54,7.851908992747192,0.03235332764508547
86,18,4.264343699418963,0.03294294008400289
47,49,2.2001671472930973,0.8547969093028639
88,57,9.21067656780389,0.9149669418045047
91,60,5.954707715564772,0.5719792018299477
70,13,8.590870958001473,0.3467306495161502
39,25,3.0242832181735766,0.4138264196643887
31,32,4.446511361608556,0.3307555630717903
72,16,7.152478159718685,0.4051997279355414
51,56,5.126758363544265,0.03510071951222604
57,69,1.8038913841447546,0.6695529982166318
50,34,6.781050924626727,0.00925049088045582
9,57,2.0483981637076076,0.8150216743517682
2,46,1.9695147002286972,0.8236731954676536
76,10,3.2367032040028865,0.189359530938495
93,40,3.5290802325675985,0.5770031224285761
13,51,8.366286124001043,0.7531077788790762
3,43,4.667816904834296,0.5663977961774596
28,87,4.033038804641689,0.523226854909037
95,61,4.099665005428983,0.382169865819883
11,58,5.760839569829173,0.5331949513335849
18,26,0.09758072728262124,0.7763890421115288
13,8,3.3584379922185317,0.1863867865578278
60,60,7.177857666160996,0.10434104385849963
65,96,1.1312776613884845,0.9674134933532024
6,33,5.839416802331847,0.378146491269558
48,43,7.271644108335251,0.8995373336774743
84,81,0.43165651762144375,0.3493858177036441
17,78,4.34280471192559,0.2029239277672824
58,12,3.017355257636595,0.08158660374123372
23,76,2.964865343386526,0.0690704096811312
48,35,5.776276020339678,0.893577319760597
73,2,1.7577645470923775,0.06759772321371171
37,96,1.933497529060112,0.38664529916662904
55,16,9.506699244782125,0.5224209433841157
21,89,3.9832543315545896,0.21727916670097103
30,43,9.439600407370815,0.4025782968889513
4,94,4.0558134618401365,0.4412080905758925
83,87,0.5577932147347009,0.46357822403968507
64,17,5.698770885091612,0.3799729422257563
82,6,7.314244632540017,0.9379001142886053
51,47,4.972927027027878,0.7498405462605637
46,21,3.7018950508852324,0.5939738834476441
19,24,9.129329003202235,0.3757168235721946
78,68,9.318254737275796,0.30123710713292373
65,48,2.7578489748122936,0.7651692440439245
85,96,4.750884998198361,0.30555530537592257
44,59,3.7588664444315434,0.029012995900403737
42,53,7.965520525420933,0.500322500665823
34,89,8.137472839811998,0.10683445931853242
31,74,7.154376619192167,0.020288324506276934
14,87,5.458581884838476,0.4920008942367613
80,69,8.248995001589027,0.23526849665722827
50,21,2.5306642194391116,0.3794406316118146
61,45,5.298710794959918,0.37907685807994806
85,24,8.321777043104493,0.536579378508093
62,76,7.7095256085339345,0.3829852909795268
97,68,6.720884566803925,0.3390071114792286
60,77,4.533234610599038,0.8113433047634149
26,10,8.06790434062078,0.30299925326446464
47,43,8.69852562934309,0.7895856290622782
78,27,3.6823345284575018,0.6433166905984297
82,95,2.9028972238732917,0.5743434116842108
48,50,1.2885001947678354,0.5845643239525443
24,92,5.10702514748865,0.8295933418361403
4,70,3.697077445626795,0.7706536847343546
19,48,9.705052235091756,0.270603140864078
11,35,1.6398273857230894,0.5619636810964064
64,82,8.60868600219238,0.5136674086504184
24,73,4.747910218034176,0.9698540725625405
77,39,9.537053731100741,0.3184912393594964
26,65,6.623026331783141,0.12464013667280971
18,6,3.118879056544123,0.1687671012080162
25,88,1.661248992411558,0.0027643427331558446
30,16,1.7496507474499778,0.7533736018848727
14,68,4.7877230911941435,0.8567674787579299
90,35,4.017104747627666,0.3475651824665301
50,90,7.885812663252305,0.15224668925837392
88,69,3.527703312016046,0.321127267891277
60,76,4.367915897127837,0.855080625730378
65,1,1.2124336273196634,0.09352156937936174
41,44,2.1441067147164086,0.8308344029216161
75,37,5.23272398959814,0.12124510374511377
87,47,4.538318227134673,0.9102798004594629
65,93,8.049689874685107,0.6156585039063263
27,58,0.3047661250962619,0.7169403094641904
83,16,9.20113436803063,0.07680089025478865
52,32,3.3875605244645546,0.44228488826006074
5,90,7.7095709383023046,0.9799160115631094
52,97,3.6345203156375074,0.7827122469716974
12,58,4.838114696803647,0.05473584699724787
10,72,0.508058948403507,0.9765336364302032
17,50,4.027819358150032,0.24275873502950995
14,97,9.524236590404163,0.11350964370743688
11,77,3.527122966633823,0.5955015777009192
91,45,4.772816223394329,0.7847202464900873
78,67,6.20973216289181,0.8563825326861786
0,64,0.9431439754563342,0.5482371426367199
58,16,6.549678084867544,0.7318436530470492
7,1,8.399829627881095,0.4050885728277188
92,79,2.5015507201515397,0.6365583371748141
88,12,9.988248273349624,0.91778648665744
36,78,3.5159152629245307,0.2713837465739497
51,55,9.375528856307408,0.3026126573046497
92,16,4.027980101435947,0.9242330260173324
3,39,7.560986956014491,0.3032799651405086
69,93,8.318737374058491,0.9468290390539934
56,19,8.769740801633382,0.7910807429615233
3,78,4.85867456393448,0.9293999905478831
43,34,7.62621241841282,0.4909205436131492
38,70,0.8485749761563488,0.8174025274111226
97,21,1.4620852192773992,0.28112583098770594
78,0,3.6170753191985927,0.08955589947833242
4,75,6.741010108321763,0.042069505873642
85,91,9.104591257308506,0.6706786433254306
77,47,7.373105749484914,0.342128451024016
97,25,7.551534579609695,0.8037059903606372
0,65,0.18652005163854013,0.5596295475108785
74,32,3.8660598542307167,0.36416943608251073
32,42,2.979184547336715,0.8546082155458167
58,37,7.356194239909315,0.6748553130671284
6,4,9.441521880256872,0.6546753816907176

friday_contacts.txt

135
Agent Index:Interacting Agent Index:Time Interval:Intensity
10:88:6.688619668606895:0.9667447062422788
9:95:0.9007299229441801:0.29413754832944883
31:65:2.3104869981567724:0.13629109371609527
76:96:3.014786047555358:0.06339801582240967
51:45:3.131052149585477:0.4445434382525858
89:8:1.9427263725803523:0.8808337927621033
10:58:2.495396084901178:0.3683191418222923
21:6:9.164811448710253:0.9149064054528824
20:94:9.073726154746812:0.09172186189747888
11:35:5.811227020170672:0.04277875290311006
81:67:7.382044703884041:0.7562618254548581
35:88:5.427284912563087:0.4919887871599472
64:98:3.2931497408328347:0.20795695942795245
21:60:5.309981205719868:0.011521792726691515
96:55:9.656859364978201:0.3703774321272413
56:15:9.416354213552863:0.6794383500051776
26:84:1.984408867513613:0.29733391878026927
20:92:4.853416282497795:0.7638596564144793
74:36:4.155107158450942:0.3304639571662785
98:80:5.883730603507602:0.7125416288385673
48:58:4.237992321318459:0.2184722842877974
63:11:9.4185033672319:0.6937998477526771
93:84:0.07677001757306234:0.48147174789615876
4:14:3.9106702580659745:0.8198449581478692
20:25:9.759725526106168:0.7121362469200376
8:40:9.859266700927066:0.9002242531813217
7:48:5.9862020774776905:0.2562517464656725
23:54:1.687746631438165:0.5900449712604049
50:10:7.664351665510184:0.34811947944265953
28:46:5.972242241867672:0.8446013225231795
48:84:0.4610657004284924:0.6920856877392875
22:80:5.797512189699676:0.5749543208902808
15:69:0.6755755530815644:0.16837016924547443
89:21:3.7647209748264734:0.022369240867610274
95:99:7.419111728757224:0.6755793454767197
46:1:6.0869705981206765:0.9392659049557766
96:62:0.38005028756432413:0.9685916051859171
75:35:1.5759150731503702:0.7930309701559252
83:18:6.419755471582778:0.8909924833495784
63:5:4.374873831058975:0.14767216650642556
77:36:1.1436741940145367:0.027032871240488587
31:27:0.03543073074623537:0.26791737345013567
93:58:8.600159028547823:0.5089371541057991
37:83:9.525552257972498:0.7630639782440107
12:71:0.1838245228022528:0.16301999982715865
4:28:7.201106455933197:0.639241011778947
39:55:2.1450605439606854:0.0072084624135791975
76:28:0.8317688391569544:0.11354085521055068
92:75:8.891200749890123:0.40037278495757134
63:67:0.6057359831130937:0.49096137048602173
1:78:2.175748319238159:0.1973094235179783
44:90:6.603685950367471:0.07742541911225254
43:48:5.313085158201147:0.0031820058422277198
40:66:5.140130492045794:0.3522022574851984
73:47:7.622359504852094:0.3803414058582355
44:6:9.90535715069833:0.7067631788482184
82:71:8.438586749469684:0.05817509423223455
27:89:6.191647064682342:0.4954437234413782
40:16:2.7203192040973834:0.20918796475319368
72:94:1.0722333108247561:0.29041196859888474
71:3:4.911064356203861:0.40762232519762864
96:13:6.0216102844761465:0.18221116663426262
87:44:8.942071102975323:0.6762865235784432
81:87:5.828205005560443:0.43654291147178037
67:0:9.558161218422207:0.9782982086424898
26:15:2.747686471657702:0.5841505638191209
60:64:3.1877027193758387:0.7852519193645222
28:21:8.89719398196214:0.7796779703774409
37:27:6.148709351415107:0.11498229333292553
11:64:2.588199198086797:0.9232497898570643
94:60:4.584180815888526:0.6663030600822625
96:14:0.50361178946847:0.6276123660867005
60:21:6.304111960148045:0.40129177112848724
72:1:7.946241811259695:0.7384589147638106
16:72:8.280083142609529:0.4050472195277236
9:11:9.83701917013762:0.5098460996889553
72:29:2.7846659029729883:0.040863074669492905
0:87:2.5247982332670693:0.1763267929861304
9:42:4.288106193000074:0.2738502204725093
83:42:0.7506400165327698:0.7642430599819986
12:76:9.9768333694983:0.2717578760733744
20:49:5.381409836273989:0.6346301712804985
16:16:3.6726867565695507:0.5582714052891703
3:85:1.3686976214395152:0.29712675725144
74:15:3.4809262245707284:0.08676173861125958
99:0:6.3566236770104645:0.7076734815700955
92:2:1.2152547236516253:0.06654621024479845
62:39:1.2779870965308293:0.45103199912883984
0:67:0.001817668070662748:0.29057807898622035
68:31:7.533477004492588:0.9192380824068297
12:95:3.6035444861225017:0.6605518577198357
80:59:7.992128919597919:0.4588233013708509
92:9:5.942982815932124:0.8525527661682476
54:49:2.5528781753994645:0.1674153560281869
67:56:2.4068751851152905:0.06936911168175486
99:22:3.5335711374409917:0.39379603080655945
91:95:3.9199908183515664:0.25378177050876816
73:18:3.503121031888783:0.7584130737841819
68:46:1.4189787199600157:0.019877623645405795
44:23:4.212711155988669:0.30143615210535746
47:39:0.5087911598281225:0.5893668330016113
48:92:2.3542849120881657:0.9451616102571446
8:54:6.903186150983789:0.2695255667504325
14:84:9.462171277620177:0.060500538799988846
91:25:3.697664572724438:0.4079539487067759
88:55:7.346264967721757:0.552949376315497
52:88:9.665066927538911:0.7401975800017172
18:7:4.4697307603688285:0.3782737394491341
90:32:2.916400428655871:0.536697768154158
2:74:8.317196407757693:0.2955317179537563
65:73:8.914697419270194:0.8930540458434777
20:21:4.219761483887274:0.2632063077194766
75:97:6.310357238443305:0.14546059647798926
26:69:5.469932282412852:0.1777212237813397
22:31:8.687063973633554:0.02966055949862567
87:78:3.4690077115478637:0.5578236892580205
62:47:7.198318314161049:0.8276001292402286
74:98:2.3265643497552024:0.22498444445538013
51:72:4.797553032172871:0.13299317181491055
36:76:5.369674743302922:0.6308887356663198
81:59:5.147324117443669:0.8496019187336087
55:82:5.933758031306022:0.4066184094819313
54:6:2.448195518938933:0.0869846065164368
31:97:6.658117713557282:0.4701995171802008
44:26:5.384133256347262:0.2881262392733547
66:93:4.77688056130655:0.02994329042677224
91:19:2.565625338887955:0.888807228949255
40:4:4.003688953910921:0.9128964821711357
94:91:7.023901631427076:0.6382076850534217
17:52:9.229885545120888:0.3200856999256
82:5:6.528401052084762:0.747569444766493
20:29:3.094567113936595:0.6996607131893888
85:19:3.4859677673907297:0.4504028250467983
12:62:2.279581910720033:0.31311123094403015
96:66:7.081217549271823:0.7148731929250536

friday_events.txt

10
Location Index:Agents:Time Interval
2:45,66,63,77,71,80,50,14,92,19:60
7:42,17,26,76,4,4,1,42,29,78,2,24,24,48,35,10,52,57,1,94,11:30
9:19,99:10
5:1,74,5,46,18,27,43,60,17,29,70,4,92,94,37,63,96,33,11,81:60
4:43,29,49,74,40,99,3,48,31,96,93,69:10
1:24,57,37,92,20,6,28,94,2,95,88,16,81:45
1:71,65,42,66,49,79,32,50,16,43,55,67,32,92,81,91,87,76,82,4:30
0:68,57,48,79,3,26,63,25,29,21,44,35,37,56,85,55,39,6,40,97:30
9:74,26,10,55,59,41,64,38,46,76,70,33,17,31:60
0:55,16,79,60,40,54:60

generate_agents.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type:Residence:HLA Type'
	info_dict['Type']=['Student','Teacher','Administration','Staff','Visitor']
	info_dict['Residence']=['Dorm A','Dorm B','Outside','Teacher Dorm']
	info_dict['HLA Type']=['A','B','C']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')


write_to_file('agents.txt',100)

generate_agents_csv.py

import random
from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Type','Residence','HLA Type']
    writer = DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()
    for i in range(100):
        info_dict={}
        info_dict['Agent Index']=i
        info_dict['Type']=random.choice(['Student','Teacher','Administration','Staff','Visitor'])
        info_dict['Residence']=random.choice(['Dorm A','Dorm B','Outside','Teacher Dorm'])
        info_dict['HLA Type']=random.choice(['A','B','C'])
        writer.writerow(info_dict)

generate_contacts.py

import random

import numpy as np


def write_to_file(filename,n,no_contacts):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index:Time Interval:Intensity'

	f=open(filename,'w')
	f.write(str(no_contacts)+'\n')
	f.write(header+'\n')

	for i in range(no_contacts):
		agent=random.randint(0,n-1)
		contact_agent=random.randint(0,n-1)
		time=random.random()*10
		intensity=random.random()
		f.write(str(agent)+':'+str(contact_agent)+':'+str(time)+':'+str(intensity)+'\n')


write_to_file('monday_contacts.txt',100,300)
write_to_file('tuesday_contacts.txt',100,100)
write_to_file('wednesday_contacts.txt',100,500)
write_to_file('thursday_contacts.txt',100,200)
write_to_file('friday_contacts.txt',100,135)
write_to_file('saturday_contacts.txt',100,50)
write_to_file('sunday_contacts.txt',100,70)

generate_events.py

import random

import numpy as np


def write_to_file(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents:Time Interval'
	n=random.randint(10,20)
	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		line=str(random.randint(0,no_locations-1))+':'
		for i in range(random.randint(0,20)):
			line+=str(random.randint(0,no_agents-1))+','
		line+=str(random.randint(0,no_agents-1))
		line+=':'+str(random.choice([10,30,45,60]))+'\n'

		f.write(line)

write_to_file('monday_events.txt',10,100)
write_to_file('tuesday_events.txt',10,100)
write_to_file('wednesday_events.txt',10,100)
write_to_file('thursday_events.txt',10,100)
write_to_file('friday_events.txt',10,100)
write_to_file('saturday_events.txt',5,100)
write_to_file('sunday_events.txt',2,100)

generate_interactions.py

import csv
import random


def write_interactions(filename,n,no_contacts):
	with open(filename, 'w', newline='') as file:
		fieldnames = ['Agent Index', 'Interacting Agent Index', 'Time Interval', 'Intensity']
		writer = csv.DictWriter(file, fieldnames=fieldnames)
		writer.writeheader()
		for i in range(no_contacts):
			agent=random.randint(0,n-1)
			contact_agent=random.randint(0,n-1)
			time=random.random()*10
			intensity=random.random()
			writer.writerow({'Agent Index':agent, 'Interacting Agent Index':contact_agent, 'Time Interval':time, 'Intensity':intensity})


write_interactions('monday_contacts.csv',100,300)
write_interactions('tuesday_contacts.csv',100,100)
write_interactions('wednesday_contacts.csv',100,500)
write_interactions('thursday_contacts.csv',100,200)
write_interactions('friday_contacts.csv',100,135)
write_interactions('saturday_contacts.csv',100,50)
write_interactions('sunday_contacts.csv',100,70)

generate_locations.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Type:Ventilation:Roomsize:Capacity'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		f.write(':'+random.choice(['Open Air','Lab','Classroom','Cafeteria','Library']))
		f.write(':'+str(random.random()))
		f.write(':'+str(random.randint(10,20)))
		f.write(':'+str(random.randint(30,40)))
		f.write('\n')


write_to_file('locations.txt',10)

interaction_files_list.txt

<monday_contacts.csv>
<tuesday_contacts.csv>
<wednesday_contacts.csv>
<thursday_contacts.csv>
<friday_contacts.csv>
<saturday_contacts.csv>
<sunday_contacts.csv>

locations.txt

10
Location Index:Type:Ventilation:Roomsize:Capacity
0:Lab:0.17931671057168175:14:33
1:Open Air:0.29934348486313356:17:34
2:Open Air:0.8567996764630795:19:36
3:Lab:0.15045983775394045:13:31
4:Open Air:0.37538590106059433:18:38
5:Library:0.12777319682254162:14:40
6:Open Air:0.5840889324453068:13:34
7:Cafeteria:0.9846490323043516:16:34
8:Classroom:0.7226268679741341:20:33
9:Open Air:0.25595540385626603:12:38

monday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
53,50,6.513987164288512,0.009841241328195971
75,41,9.148192540295673,0.24346079227118766
48,15,7.767801788987083,0.7136052498759755
52,88,1.5827087482234237,0.20764631674966094
91,29,2.7343235816879474,0.14895783199367685
38,8,4.334045729516742,0.6439094471011881
29,63,4.210981817246595,0.2554432621369428
27,36,8.47635840100173,0.3832054559612531
61,26,9.548384089998105,0.6427227428081111
59,75,0.19282581475098315,0.6828452816536151
43,36,2.766678550264544,0.09873500692734027
25,65,3.1770527299851494,0.3435712193957915
8,40,8.102382721566547,0.09587949195147027
94,25,8.241622912147497,0.5730417318280656
59,47,7.699772353241968,0.39279197145264855
89,56,0.5541668883448503,0.06333660535403263
17,97,0.39998863659011175,0.7631024669772002
21,24,9.481067041452134,0.383204002416655
89,60,6.957525839570176,0.30328744337885094
52,38,2.3412395878589987,0.16383796788949623
28,0,6.141151899527708,0.7962272950902385
86,66,0.2603884125232203,0.7358136231991173
91,84,3.6123649897326304,0.7799712779618293
10,8,8.663266709710413,0.12171387351501184
61,29,1.6262042324661152,0.10065614499197328
50,21,4.841685610806538,0.7543211044433643
36,87,3.452267039247221,0.24586831246758334
81,82,8.650432434700082,0.06323310864576226
92,22,5.715589643104055,0.31823996685111133
73,35,6.284157263259199,0.11694633297875623
10,84,8.999192526045253,0.6437786483355039
41,24,3.329425451169008,0.5970435365473865
83,28,6.085727947739475,0.16747815697677937
91,92,2.3851917083174436,0.7073630889864982
43,70,8.455754235213357,0.10182165120349396
5,83,6.174698872178428,0.941209396024936
59,14,2.291049580905622,0.5455844456290104
13,2,9.46944893647284,0.7369595962496633
58,44,3.57900560392034,0.6837125615283298
25,93,5.116785984157063,0.13994535890464188
62,46,7.148574887294899,0.5405338300800007
22,90,1.0063834871966304,0.4877659103098638
99,46,0.8665745698758154,0.10833600628847495
38,43,0.7143250674023671,0.23448581373131705
4,16,8.1451375328301,0.16790513318785627
4,92,4.96476895002348,0.8104031815415705
98,4,5.911646782174485,0.7988305382316447
65,93,2.6847021765065024,0.287871730729368
7,54,9.222677019633089,0.537725039535849
29,68,2.9028843779458393,0.3334779206701901
81,42,9.099960055312769,0.344508935524367
96,3,1.8276294599828247,0.5879862401519191
12,69,7.444276394152981,0.14065410813768553
53,24,3.8426989168155936,0.553110116244658
32,53,2.8024046447519813,0.14749352841808105
31,26,1.8300810567986148,0.2511953179826105
52,63,8.181614960177079,0.5342722919206799
43,79,3.6303269687559436,0.041962863257867467
32,67,6.808129891567074,0.9655803746876441
88,7,6.529327196638947,0.8705529513644207
24,22,2.343935060531636,0.9962011342143507
52,76,9.903506135903946,0.24535465759316943
34,71,3.440764716127096,0.8830014936240537
90,8,4.9723666216465015,0.8660633726701565
2,16,3.981201904138718,0.5462666429394176
51,73,9.173840400289956,0.865539433334778
29,47,3.148978965476934,0.008528755768497653
49,17,4.411053661515005,0.29462045868329656
94,20,2.99799449269492,0.008094047003058158
14,89,6.932501717103873,0.47267759190124015
71,63,2.4410782836620495,0.8450391983408515
65,41,8.452847533443084,0.2344428882511541
93,89,5.446234835932727,0.330607083902015
75,74,7.1764769352007916,0.025050360616053924
4,20,5.261301134184475,0.8635716467281545
50,54,9.317619443580812,0.648110436821533
74,16,0.9372646530940454,0.9950542940364594
87,38,7.183818262108614,0.13361402344750717
14,30,9.169442123777605,0.16886440853265938
88,71,3.3102347294712953,0.5561612966102644
85,33,7.070634425610756,0.2863648970197845
89,46,9.177775831868844,0.07552739773663264
3,94,8.183009216900762,0.4620521108586507
34,24,2.1401199464054477,0.5509220560673987
72,6,1.7324077130068205,0.8059066985821882
79,34,0.44615888882443167,0.36544912453839706
80,10,8.963528390783331,0.6351811175877636
50,9,7.047409633314041,0.41897643552173114
18,41,8.56145461013509,0.43182853230932194
14,83,7.53361659631241,0.217323186288224
86,35,8.89767842506425,0.9536791563015704
82,4,8.12157962871083,0.37018509361329277
97,31,8.648762210396342,0.34765263360451615
7,99,8.776027691582023,0.25878808302839884
18,83,6.269859483863993,0.3125403759007429
44,77,4.464382164004213,0.13901033013312758
15,84,8.98611873951888,0.5071629988483005
71,12,4.984748548530953,0.40055692578644464
55,15,2.5012472920711772,0.5476296836584915
29,20,1.323833021883356,0.28336173169726786
18,29,0.1507888595102047,0.07317326091711773
87,31,5.74536343344707,0.4686744188513029
16,46,3.6391719351673633,0.0794946256761716
47,6,4.142412582685995,0.12319743144062067
44,62,4.543247941899508,0.247526152958766
37,3,8.042882111482527,0.7441702016120487
16,62,5.768476020179256,0.47664663572590715
62,70,1.8980155315166736,0.23647147445571282
22,5,8.046646738605578,0.6345913535526976
2,30,5.274857202949921,0.3008106462729032
25,77,1.2803466196095459,0.36112700934738684
25,88,0.27025905784243576,0.33565355291757737
99,62,8.168023992027726,0.10124829380096845
26,41,1.8811304853591704,0.9029155005956867
26,17,8.589612826175477,0.9213970386212076
77,47,7.831043375818597,0.4247028116988881
35,17,4.152762492180637,0.4745679027732439
71,70,6.160068068019173,0.6521567323522888
10,93,4.425380020352634,0.7054440482566736
16,28,3.311087652028042,0.9166201071677543
85,31,6.45496925102327,0.22055188410179216
59,85,3.2656053850868405,0.4212341556171103
37,1,2.483329632428263,0.2826638966987185
93,70,3.188395899451053,0.21875828829817812
76,73,7.711950568255208,0.5022244747840047
56,50,2.785621560145656,0.9185163236605833
24,30,9.29755591451815,0.4328965887063524
43,79,6.304758039767384,0.8186748853057808
43,42,5.542757737114167,0.19833513133534986
16,68,3.102059433024935,0.11092284566460309
55,22,4.48744700920494,0.8547061529638681
24,45,9.16283607437596,0.5803737376495258
55,49,9.957213997749054,0.18841253768327848
98,92,5.763243882706385,0.9134748899042435
79,45,2.0356604853285454,0.9602500660221783
42,49,5.046691251349574,0.12302780997543783
19,8,8.395827945704132,0.5118220858973469
86,67,3.3253137878628913,0.31971277856462366
88,59,2.9664952022095536,0.19401296630828513
22,68,0.3384038872996886,0.20935264425984557
29,80,6.115713782624931,0.7495259112078474
28,91,9.320314287253098,0.16028074257299518
53,20,0.14522197751418386,0.6035801190491846
9,3,7.584164121549164,0.15831465204436646
31,88,2.730702741829185,0.29430232215069896
52,61,0.15088073664091484,0.8229746138822506
27,9,4.728286397295321,0.2628311922872978
33,86,7.614042631030896,0.6361740132693546
88,96,7.931938114936079,0.8622558475628161
82,59,7.5582925932469145,0.618105304402339
69,92,6.098154553251156,0.8344150383601279
23,50,2.771872594923157,0.5233656747968031
32,79,2.9523425703149506,0.05141448060184706
43,96,5.495483582006947,0.6075736888332405
47,49,8.948174139790332,0.703515789046357
47,82,7.79232272382948,0.28713868588623237
3,55,2.1197005987232687,0.8710215637843753
17,16,7.636187145481843,0.4463501376184047
66,17,5.96216082558347,0.1156295679843291
88,40,3.8833354969600564,0.7827144296503963
96,65,2.7887703424127586,0.00308759494866484
62,32,2.018292224720104,0.5025080299253926
55,54,1.7003344426926126,0.08895997472318506
60,57,9.673556267275426,0.6050642601172523
67,52,4.877761334010176,0.9199827916724133
63,13,1.7776983995884343,0.460048381662998
82,12,2.3935471506408144,0.8012083235883181
66,32,0.11754795261362139,0.37092099189472627
3,11,6.846716842391126,0.5511448261403317
98,78,8.156000887373777,0.07543486878681582
56,15,7.6584173143233,0.11395001805719251
82,67,2.510812196657246,0.2697436040624521
66,88,5.850870985161633,0.5194042053397994
78,22,5.318050188652826,0.3741489531161384
17,17,5.682766690116123,0.20102723484565566
84,58,4.600053324359877,0.8665601602485907
51,83,7.905940738999307,0.23155308931227125
38,92,6.330499459552722,0.3949879347311296
17,51,2.305887472499214,0.8529892916573967
93,49,5.043004483856393,0.6064709004839419
69,95,0.2476376406695313,0.17395004518990098
8,41,2.59984936498891,0.42830518585779387
64,74,7.659173451036309,0.7110193229540483
49,67,4.430934873881354,0.6036641096307729
46,56,8.343715010160377,0.46405645321798394
93,84,8.20735245970513,0.9654588716041783
52,36,4.071777045509133,0.09514425913775437
56,13,9.263991384633917,0.7514734590438714
29,74,6.844402733610884,0.9869675256906077
40,54,4.596949606037163,0.051095680266435295
54,52,9.074772108375173,0.8012517742676243
20,9,5.9595745038121715,0.6852065620945298
25,43,9.578013720201213,0.33143069568800143
53,94,8.883872807719479,0.143434155051809
37,86,1.0007570551447964,0.3335433501456836
90,12,8.867737588204768,0.7987419668276835
76,26,4.114199161021061,0.2052471840848128
84,37,6.40601059783028,0.29110876156895105
71,36,3.164557443444541,0.3836278225803993

monday_contacts.txt

300
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:91:3.901351436822462:0.7339762595213493
78:90:3.5626711544249745:0.6746469145132054
5:85:3.045488921169931:0.039540230198430115
86:81:3.35308756346429:0.3633954339845933
11:89:6.326551736128173:0.6289266025075126
41:89:9.329187350891646:0.6353349553288311
20:71:0.38645434909083676:0.04041798989591305
92:68:5.889425651825421:0.3950396909932594
2:47:6.557852441820289:0.7245690803250271
99:32:8.402723639687704:0.0029308245084296303
3:33:3.5133963523940634:0.5984631970990983
59:72:5.370053020778495:0.531313701010571
94:59:5.361572617932277:0.9886697867090888
21:82:9.786681409791774:0.644945666368522
48:42:5.011982887515333:0.051662026866201693
62:94:3.2808909541391196:0.9379521953670432
70:28:3.856798574421678:0.2557608578207182
6:59:5.678890997712748:0.3530777753051588
37:67:9.131898601767697:0.858499260774226
52:74:9.986369997027031:0.3156120836947629
31:94:1.507816879463777:0.750555105240906
99:93:2.121959362802376:0.09703892296171557
37:94:5.400780286788532:0.4299044759629851
15:81:4.75977897522104:0.3232455011774399
51:63:8.55510185721477:0.31288557896015257
4:59:5.9222428832641425:0.12157485621270103
43:73:6.795602865846028:0.06744185332954145
59:1:2.7021747467696375:0.050538725122540984
60:93:2.943901539081496:0.4982339562937653
3:84:4.133634564366487:0.3075102931608772
73:44:8.134038990032534:0.8418295238039117
17:86:0.542806560857374:0.7588119151895625
85:77:0.2859974668290466:0.7241059456194887
14:16:5.329831905451478:0.9195203828276385
1:90:2.211130035725443:0.23639098557775062
8:70:1.5406578056068143:0.4262130830791522
41:0:1.1579089653984453:0.7851213807119591
50:37:4.484956048115092:0.3060348403762886
89:88:8.043811518556971:0.4727074037001201
58:95:0.8868990689387879:0.3467640517252304
3:86:6.190128446233331:0.8176423375406843
19:22:6.298136491836642:0.5649230836250814
64:54:4.536312531879237:0.7989489195824466
1:61:7.833473806936388:0.650092667048976
43:76:3.44149914401369:0.8746721680294639
26:4:6.373337730771791:0.17592940756741804
60:35:9.934829550819842:0.5345586622312622
16:51:1.5764360886713513:0.6770030520140922
91:75:9.528950938813134:0.6204530683653462
39:42:7.840684207317652:0.6089954987487575
61:65:3.319567445374041:0.5478018921036643
52:46:4.646705033209998:0.537254045209896
95:50:1.7647409730344588:0.02688151759300872
51:35:4.156824965180649:0.7123780511350246
75:78:3.9226537983258227:0.596842377897293
74:58:7.563226282263413:0.8062241896776831
7:84:0.7120251049265447:0.9302238734157992
47:95:8.024189071209314:0.2387216724542346
57:28:6.2859322129180475:0.1556546337774144
70:30:2.530642559415648:0.25633238312842377
88:16:9.06713427487879:0.41516872075378486
32:35:8.09237307848392:0.5857892642742539
17:65:7.8134917233836285:0.28104998118729463
58:79:6.223781312011015:0.012229676686173363
90:90:4.692384410823149:0.46217556562178697
71:62:9.08294852508194:0.7287708959264037
33:20:1.542102652267403:0.8389929454049759
90:74:4.633437833538752:0.65537480210047
97:11:8.325837108492266:0.08894675467827051
10:72:6.557672074227439:0.8651050525879831
2:73:4.872758588552571:0.09357856657833297
57:65:6.828660337659751:0.48495229529925665
95:47:0.7466953354057215:0.6603079823678228
92:49:2.0077342778968554:0.9189494309774255
82:53:0.24061611784117698:0.9198471939191474
64:6:0.5767175723374529:0.5800677289768271
56:50:2.8103728644720705:0.7661027286936849
88:65:5.495737535254017:0.13475930874418607
39:26:4.520441829762211:0.7752928037603906
62:15:2.527874839657712:0.1705345466390138
75:37:6.949647409442933:0.5105509864259511
4:20:2.97399456614634:0.11289483289257596
41:6:4.273511532203043:0.09718585800915025
4:46:7.362236247628521:0.04058096586641702
43:36:8.117474485572878:0.14526881403519276
56:47:3.855901848144012:0.06033216610055803
82:62:7.36834872994402:0.7504447501739113
34:37:6.940059348353467:0.9688323772735252
80:84:1.746381666139043:0.7823637521600265
11:48:7.364223469449296:0.36985754872005294
24:63:8.322818469697424:0.26920941824660416
95:93:8.565298534711246:0.36630815362030034
55:98:8.261077047953284:0.24720488756898507
67:32:8.928969126003851:0.2768960055237545
61:35:8.836832665518946:0.8525592430588332
4:79:9.86527008653734:0.5241984425341051
15:84:7.895868992916919:0.9243365521462359
4:44:8.716228021549393:0.9307245519590631
96:55:6.888764694183994:0.0792443519258833
72:90:7.406214716467515:0.8859191527012792
16:14:4.69851133682884:0.19881286599667536
61:63:3.380324616991465:0.8268422238613041
52:16:0.19216831328206507:0.8647705352734371
45:84:1.2697886559236449:0.6513461320262995
50:83:6.159864427212866:0.23346664207112955
69:30:3.4770888522834897:0.36611239444780763
70:90:3.5286680285590846:0.061422783069107645
93:39:0.9637079127853909:0.807360297039483
59:97:7.043532289504258:0.12207223530271005
43:84:2.227673296719175:0.8300830010185617
47:42:1.833397217400502:0.21499046911985964
76:1:7.8677237896729535:0.1308918842693122
36:55:1.3055580188425742:0.7277279779721338
62:0:0.6371814138898102:0.739520928356319
9:66:3.6931844772562252:0.49433217498267423
5:88:1.486445418424901:0.7994315842241301
67:44:4.389739626245813:0.7501063883906391
87:66:1.5283456139385265:0.933656123136008
23:48:6.137030114523152:0.9235785663451955
55:39:5.593904911357448:0.7918698321362837
6:98:8.573685590443334:0.6643971331641266
58:40:9.370090787366754:0.16062179115642827
9:7:5.99552450577316:0.22398865033159399
78:78:1.6985126182973498:0.23431129397478678
27:36:1.7553777448861463:0.9571656933455291
10:2:3.340403747124703:0.266154426065234
20:32:9.04965093388158:0.7998074320401931
62:43:4.48824832171709:0.19692009534109944
66:68:1.044013500391131:0.17518002535790944
81:91:2.032022113548353:0.590068376259575
79:5:3.6678672963264725:0.8583172648269328
88:25:2.7906016247631857:0.3417441357034283
48:22:8.274301104789616:0.6180500643903922
37:34:4.895008043767715:0.470617322501378
65:98:3.9939963178189073:0.7597707393883737
8:99:4.344354090436447:0.08000834895567921
33:14:2.721637241356134:0.5469317967238905
97:61:4.480139087307686:0.9370228932878472
20:89:5.052620560783816:0.2537641232093184
77:4:7.5548012755079155:0.7647336265540196
34:53:2.3637671040815222:0.8631258102243161
97:97:1.1517561038645152:0.6080198126602016
47:4:4.764917096374596:0.5152616692151232
56:7:0.7673797837521523:0.05295581093999868
12:65:5.820115979848362:0.6738384932399847
22:33:8.160077472113436:0.35575801582167244
0:24:2.5976244236881127:0.07228477664392474
97:67:1.3349001353042622:0.5467070433629635
4:12:5.412935866383392:0.11638905261774857
95:64:4.328183987891099:0.5038839316589997
91:91:1.8006275689774043:0.7069818589813456
33:64:9.979922860196544:0.1730588104192059
82:14:5.625659929969839:0.427124084779392
80:66:0.12556782560516622:0.6318204731180151
84:81:8.965005435252678:0.1329683648749237
7:7:2.7621652564272834:0.22081572672352956
60:97:1.6189519082706272:0.5054371256483898
61:47:1.001552280836816:0.44870613684494454
12:28:1.7394787076567975:0.15123469338547757
92:66:1.9575307012529986:0.5143410029855222
35:60:5.974833467758106:0.18060226652566203
19:47:2.4143782472302853:0.7229634935521948
32:33:4.084222023013261:0.8052825167982911
61:42:3.880567198086904:0.33785485991760156
9:7:4.797609189111243:0.6557842812818759
60:17:6.786175767301016:0.855473866429063
87:80:6.840555606112167:0.10932325733796955
14:84:5.475907747014465:0.23291755605347986
91:29:9.17560043418257:0.17718783157595752
28:78:3.4425085921579193:0.8242337800670188
60:34:7.2394297827991005:0.8755428155410877
54:64:2.2671905891345414:0.7334964590961794
67:84:5.948336450041793:0.3098471787427216
11:56:6.700839762851763:0.3207358083318562
38:77:0.9674523146234348:0.6023850656632548
9:44:4.69793570668195:0.5029890933381677
47:20:5.319830568980307:0.38707577261884796
26:86:4.7817534968627555:0.2976309298220806
41:47:0.20291653431164902:0.4554712242013542
17:64:7.3452775056010955:0.6483550983905965
8:2:2.016021005062094:0.47350242384381125
27:52:5.803404417975604:0.9532995101175942
29:20:9.704176176125863:0.08789736542495086
50:87:3.1885515636171515:0.9367769520102764
93:29:2.0395613845066887:0.4946398943812982
76:97:9.019353389326039:0.1445289893279038
99:46:6.582281848173897:0.5425316978102004
86:6:1.6825724869426617:0.22432772872032958
90:29:7.500546164660645:0.30905885655729015
16:78:2.424664767871816:0.18813093908551015
87:61:3.2116382773342567:0.8869057054380951
27:0:6.159493456473577:0.2276835244003813
90:78:7.329164728333016:0.26167298307249465
76:42:6.930905362346148:0.9096883575644671
46:6:8.782850221647784:0.3169474488876317
62:40:1.370250262759245:0.5553566618394318
81:15:5.902292977530621:0.0556777547804056
32:33:2.3285018362586083:0.22805846655926876

monday_events.txt

17
Location Index:Agents:Time Interval
5:27,68:10
9:34:60
6:12,16,55,23,59,24,96,9,94,63,65,23,13,86,49,98,78,41,89,45:10
4:51,26,83,88,43,59,17,67,21,9,26,54,85:45
2:56,69,37,67,50,3,28:30
0:57,30,66,27,37,58,42,82,47,90,86,89,86,75:30
2:99,59,82,38,38,31,61,23,10,70,62,47,66:10
4:69,24,63,26,87,28,36,91,28,86,80:10
1:49,78,77,56,26,26,92,38,47,64,54,45,46,74,46,8,86,50,55,76:30
2:91,9,88,70,2,19,72,0,43,88,15,26,93,81:60
7:77,62,88,76,28,37,29,92,97,55,2,19:30
5:4:30
8:74,3,31,48,6,32,63,79,99,32,95,1,50,16,29,7,99:10
7:89,75,36,95,17,40,58,64,53,11,69,70,34,21,12,53,26,23,38:45
8:4,8,42,46,82,18,58,38,33,76:45
7:97,93,67,45,10,4,89,55,43,42,86,65,28,77,44,63:60
2:46,18,6,75,17,6,15,69,22,75,58,36:10

saturday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
67,66,9.857117310137038,0.5920888806387263
39,40,2.9606587904019332,0.28799857726311495
19,48,9.933376750841031,0.5563869797724015
47,14,2.707792838677613,0.11910322049310873
97,93,7.330200583028975,0.05479876188162769
36,52,5.8053943720215395,0.5087501005692888
16,29,2.9259700624959297,0.7450077742936179
24,76,6.615452592843672,0.23066023231585764
98,1,0.6129732778252273,0.03079626452546913
52,51,4.991234253861199,0.11029211445761467
49,64,4.281211334785262,0.09369780403627248
76,15,3.110100778578777,0.16882508544803732
4,82,5.018950780821032,0.4528425446162895
25,23,1.523435869691594,0.8698275706188255
69,26,5.982815427997005,0.8195210582557968
6,69,6.002756213947941,0.47565948855915063
51,92,0.9693831534702857,0.8545362381598459
4,54,8.926690297564479,0.19650516555917197
96,37,1.0761711632578574,0.2856646294271543
56,11,6.206036904245246,0.025636240561556556
63,45,9.243970250499935,0.8459046432706034
18,80,7.21308647170134,0.18639074663509203
22,25,8.566766221711488,0.9194158046579632
46,78,9.436676144535742,0.3066038278097547
55,33,0.36786224695660175,0.46598870340260434
80,31,4.295593090845587,0.7887243574197714
74,96,9.079412546074366,0.2008470536485456
93,11,1.811367363309666,0.24854787128323197
17,45,1.3031037142786772,0.736713001093112
54,77,6.689236468914333,0.18914544406995604
48,55,6.561686523837922,0.8415382745176084
95,24,2.1199045466842223,0.3191748832651885
0,43,6.361037571365211,0.054785174673453896
0,86,0.05530006267194132,0.44722966677561227
67,8,5.108232528265227,0.0780720907027247
6,57,9.073223985113284,0.6305218263588744
16,8,6.875884563883529,0.7889755826254277
35,48,3.63455049866231,0.23653026160724466
93,72,2.726782980498262,0.7473684674491506
43,65,0.5285308238378339,0.8079994482515851
30,84,5.435250398044084,0.22794249938945577
68,66,5.812705194521447,0.3987768568223644
37,30,5.462713140719986,0.2651939540942704
17,49,9.554281145866424,0.2854383038592877
3,1,7.129363264331846,0.6332198675617925
34,66,3.7040828036302886,0.15835840507645926
39,53,8.70713280127657,0.7521303893855847
85,42,2.9065301405226784,0.4061131567948121
4,71,7.566520295016451,0.4702871038447024
25,72,3.3524932671715546,0.991241688877458

saturday_contacts.txt

50
Agent Index:Interacting Agent Index:Time Interval:Intensity
78:67:6.687995944426314:0.4033605962521598
60:25:9.766242543855506:0.06792652175390823
18:90:3.76439853374662:0.9833305943472379
4:19:6.506721718246754:0.47110824328789613
73:26:2.5400982744608336:0.40140270897106645
68:57:0.7095173223023121:0.2328466079907241
57:54:1.545474789816078:0.6593172582955799
90:25:7.843174009450287:0.8268544568417224
28:52:1.3914812455190517:0.8024812081343093
9:71:1.5518951054968155:0.9318507773242553
39:28:8.476359135606552:0.7763246795933149
49:50:4.7211099258761555:0.2950133516599971
52:12:1.6886708754449598:0.27197127175998526
19:20:5.6781326401719046:0.9590963962294797
22:77:1.530950907625851:0.3754295473613327
89:88:5.658452775120387:0.0803874959012173
4:12:7.636759575481204:0.2831880696193816
58:57:8.601430915297016:0.5434659477112249
4:32:6.147275128372893:0.856773362268219
75:95:3.3575411423184542:0.46747804925146763
48:73:4.294729247064641:0.050670872480034546
87:31:0.20788603769580694:0.06469788200264015
50:44:2.983096090063986:0.02735893758806829
12:73:3.5332686474645323:0.6308187130898412
59:18:6.507883109382785:0.8679178454556692
90:76:0.6431511136089729:0.38181470878945245
93:55:0.688031974008203:0.02165710753326533
93:54:0.5174288624109147:0.4172063304023521
90:55:2.351244324988293:0.20155062301576254
6:69:3.204281646873234:0.6853704116954752
37:45:4.207020809561934:0.5062926163922263
50:85:3.3126767901315013:0.11439025603353281
18:98:0.5483540344431337:0.5831224256744627
3:20:8.466441890639738:0.15531801936817402
70:84:4.252674278698835:0.1021412687119303
84:96:9.65755023003075:0.9104362499713785
85:30:4.477617210548132:0.2798872009598208
27:81:3.4726517979202596:0.6867846228811487
33:5:9.418615513088076:0.3089650238414625
82:19:5.969411859418942:0.9201045742327656
16:63:4.806229721717362:0.9196419859150736
29:41:7.958618332948535:0.44643319464470743
32:85:7.863072004177757:0.8397192112109673
64:63:2.8034310396456483:0.5005219190796429
22:53:8.636229353574626:0.5573464209207643
20:81:1.3492272749291145:0.5759296391255372
72:1:3.170961659254469:0.864395776604293
51:5:6.983939525485372:0.3998399286929163
6:11:2.828084537368467:0.7569874076404247
84:6:3.225352755378524:0.13692972894786104

saturday_events.txt

10
Location Index:Agents:Time Interval
1:63,91,41,97,7,44,73,88,6,36,22:45
5:70,76,47:60
1:50,91,48,78:10
9:60,55,98,50,0,11,22,13,92,54,33,84,14:60
5:7,76,83,54,90,94,79,2,75,34,7,96,50,10,23,54,94,15,41,87,6:30
2:44,90,20,90,40,99,25,95,5,3,31,31,50,31,4,48,40,80,6:10
1:99,53,83,90,63,45,12,82,28,96,68:10
2:61,99,34,2,53,88,77,16,90,28,43,63,62,88,42:30
3:93,29,68,43,29,0,67,49,9,92:10
6:91,31,55,9,57,71,39,25,76,76,17,6,18,34,27,4,75,4,62,29,6:60

sunday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
88,99,8.486887539457896,0.6304214594796798
22,98,3.2976061937586563,0.781281314136626
75,99,4.430757248996154,0.1192555052707921
1,16,7.048835094335958,0.13157790618351306
64,56,4.655156331391267,0.6998835788082481
52,23,0.40055384199715993,0.75098379425079
57,7,1.5925582330513355,0.18543205571638421
48,53,6.490406524470783,0.2868193033569312
29,51,4.493661262409669,0.5815858845665998
43,75,0.6573777627777944,0.48978674189411686
50,69,1.5519768951778778,0.054714720400359296
83,70,9.975574102080033,0.29285241977157617
50,61,3.8682067678943124,0.4261808134423466
75,33,6.740529736522591,0.2455572388334618
64,22,5.975520317563916,0.339408352827008
12,25,8.123063683302792,0.5784864212678281
26,1,1.4395665451710782,0.9527690571698493
14,60,1.4430820158919855,0.39247712631007536
2,85,3.8006454199690043,0.2921826762943228
67,47,4.673025133711266,0.8086927544736627
90,80,0.6743678721890989,0.8176138774275091
0,56,8.047433934786856,0.2559808833574637
95,81,5.215697774477086,0.9006825144319155
30,35,3.990730095641025,0.5069325770991455
39,65,2.3811238779362887,0.30447260187846537
70,7,7.576691410586301,0.09327402375535143
98,41,2.769033142970916,0.052943430372137
40,9,1.53999525068522,0.3637708934017183
25,26,7.337572932448316,0.9914045891898488
85,24,0.6183737562734581,0.12280394449800236
92,64,2.7937380809337697,0.38492439656092525
54,3,0.358907903107174,0.6108859228588672
88,13,5.20066638591457,0.6428277196598772
58,14,5.132696666075683,0.7770071741679037
96,37,8.951072527951148,0.15909392193657212
19,5,2.667024065530701,0.16760141110060367
38,65,4.776675413849936,0.6358673983241082
33,43,9.388244029403698,0.2677659863381495
74,3,1.920978437228692,0.8181512011809285
4,22,1.1168095443666715,0.49875323614048606
33,78,5.247483529457279,0.1808059413598897
27,55,1.4802711152787895,0.2644792206222256
13,47,9.879205440524398,0.8262467807502241
91,41,6.006774424396097,0.36409489715815657
91,18,6.618327483739249,0.808798335582276
28,70,1.2853107381027506,0.07098388830301883
59,30,7.047303701965818,0.6403449892486467
38,14,6.185026335954712,0.299405530751356
90,4,8.716153438777326,0.8234338404902319
31,81,2.4693304094766266,0.08279562712445887
72,3,5.256897773525066,0.6592241405225215
6,20,6.098476239562825,0.7645209043837615
86,36,0.08937541961729134,0.35642021687049374
75,94,8.854515849420165,0.9606419685383933
54,98,4.638295722864876,0.5073037465495944
98,46,2.1629311222063374,0.27936623481924505
56,65,4.647708874943114,0.3714395293129583
76,77,5.891978882816718,0.22901678369513967
8,24,7.3415194739594805,0.16260056535985934
6,95,7.261061146157749,0.521374883996052
3,57,8.603304779092946,0.5139482482796789
8,9,7.012907842879529,0.4997630558711238
66,69,7.715026519996071,0.8236282668323782
87,9,4.079938354014608,0.4660587190357144
64,48,0.8764088012451265,0.2749338760438347
75,12,7.643239456414168,0.6320420632107933
44,35,8.966995279467467,0.24014856201649082
13,30,7.402636843700636,0.022401560118258
57,73,2.820428173846705,0.943722059836342
61,73,9.41610911071566,0.5783995362749058

sunday_contacts.txt

70
Agent Index:Interacting Agent Index:Time Interval:Intensity
29:74:9.16372608230145:0.796954964751048
6:51:7.883900864399966:0.4086674799106108
80:96:0.14815771583060466:0.9598774863311844
74:97:4.308374781710823:0.5469594486352
51:13:9.042079023826053:0.4951854351944386
80:59:1.194192610139505:0.1806780778265884
29:52:8.298008123594673:0.6055655082754013
11:99:7.398529511198307:0.8423463023583696
94:96:0.8114992155173495:0.6185711886621139
32:66:2.3703816553952883:0.35420962128864475
39:99:3.855243189079789:0.19683280636170353
14:20:6.993801240807475:0.9924238391700014
69:5:5.368207898395178:0.3296239694354882
22:82:5.141505384517688:0.6680939851970867
16:14:4.381681285284316:0.13324126477503573
63:73:1.1377617766820813:0.016765931895245334
53:42:4.331827823657975:0.560247114997433
74:72:8.487044883624892:0.44857507061224955
48:53:8.142210501452041:0.22185756589964178
39:80:0.919320244588725:0.0066855429075109996
3:21:9.289809772081991:0.48205140669390023
68:46:4.536474019847931:0.6855403269937351
58:90:0.8335888281998161:0.5829499953459416
5:56:8.623750119046413:0.6850571684408665
37:46:2.19563994708889:0.32831432045342446
43:16:6.54823708272073:0.7241818732660742
55:95:1.356326584676878:0.8593548177807079
33:51:5.72041717661566:0.524825828129498
56:97:0.05304038966948088:0.9396784738557239
39:25:1.6057401908444169:0.7729036122188875
75:49:5.1930718546095:0.6210496845686397
50:72:6.400159461397456:0.4083531569308648
16:36:3.095902490181952:0.7697749975800223
98:36:8.736666708714022:0.9269722338361613
25:4:0.1996745353487972:0.3912838723392581
54:37:4.379634943637777:0.14566373998141358
92:55:0.019798603565377437:0.7045448754207068
36:91:1.6904254816989417:0.9488514792773272
75:0:9.344337089059099:0.31555189896437286
98:93:5.920573213081997:0.0028759470703639822
73:27:6.323996215279141:0.9268743088920766
10:26:0.3335853333487415:0.22327795239905257
83:6:1.8792327407608012:0.5844054293798739
80:39:8.666561745531057:0.6341570680368916
85:64:8.279808342659074:0.10714932227380469
51:80:2.597488396875214:0.0753952752053868
36:39:5.6836065070360675:0.6191528017058452
72:11:4.7881710302403455:0.7655770299526184
46:90:1.0209935816785076:0.3288165509174008
44:3:3.9645570905388516:0.9433763441413141
20:19:4.608574167399386:0.879494666634502
5:30:3.2439699423921597:0.1099899352310858
82:95:2.2446524075665786:0.3730883570466935
36:47:7.523082303216631:0.46842401692247715
67:80:0.46591410875939654:0.43019339282451874
86:1:4.153321745378494:0.8744248883962594
32:87:3.740524247725736:0.3904419258374895
4:88:7.107645004605604:0.971958195970546
80:46:6.087211809269826:0.5332140334707597
20:16:9.743912977300749:0.06325827742806112
11:68:3.451500943176198:0.1235586691736077
7:86:1.3489846728692534:0.9380271839281606
98:76:2.3319468321605408:0.23635317254322508
59:48:0.5527503420919422:0.9217009076588242
45:94:8.674582793785653:0.6210440386455588
24:38:1.587140836439298:0.7617430249347842
66:36:1.1370077466599082:0.7580769247367519
92:66:5.149056575604316:0.20909269304442213
96:32:7.470321654585279:0.14446721285379327
57:32:5.236089542116535:0.012270130984272543

sunday_events.txt

11
Location Index:Agents:Time Interval
1:0,91,50,25,99,16,48,67,28,96,50,4,80:10
0:74,4,62,93,73,44,56:60
0:7,21,89,8,27,80,40,21,5,85,48,40,47,44,77,28,83:30
0:45,2,58,75,53,99,82,23,42,63,9:45
0:70,79,66,53,46,99,73,66,31,61,49,14,21,54,55,37,57,23,72:45
0:17,13,47,80,92,35,39,66,86,75,83,14,25,97,65,21,33:30
1:53,69,20,0,61,11,55,39,69,35,58:45
1:80,7,55:30
1:49,34,43,54,73,35,38,82,39,50,66,41,90,93,3,96,90,40,83:45
1:8,63,58,69,43,4,78,97,20,14,9,74,37,29,12,58,29,63:45
0:21,39,32,72,31,7,55,40,76,38,75,69:45

thursday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
9,72,2.914227778302505,0.6826991464281399
4,23,1.7626573477813823,0.1867575121778262
96,10,6.188166718934582,0.5343467721947005
0,58,7.089036604140549,0.10775487424959562
9,11,9.67302895627405,0.8980069940395575
93,82,3.5711730462818903,0.8470185341812573
44,20,7.492920372324567,0.8767819235925547
81,59,8.877288545955315,0.5726311240333285
13,58,7.855807841441584,0.943416360883798
55,41,8.615477261049776,0.28432691622917616
3,25,4.451749865199233,0.8913642362995232
11,22,2.9730546580604997,0.29981855155001325
76,71,3.8844120704576577,0.7286341972286404
48,36,1.5232199132644864,0.5145761295979717
98,22,2.718914273776586,0.7076840108883607
26,15,2.1845358786259785,0.7831605938784265
88,80,7.478995841823769,0.6409935415627314
41,71,4.602269400448483,0.9921185937661067
9,72,3.4198736933186646,0.9166824813396153
57,54,5.300520828275738,0.6892351426205671
54,56,5.816201895124036,0.8659727417260997
84,47,4.094474084211179,0.3997958008433463
15,45,7.993018008393561,0.8704458892221738
17,51,6.13897007916667,0.9648374061250926
21,97,1.8012852087460351,0.36901019639081134
69,22,2.416538351050921,0.9864639695301223
74,60,4.362163305391271,0.3712260589076263
94,87,9.872530116893952,0.38401371533485174
72,19,6.7283068678525755,0.32881118325285585
22,29,6.68050847751975,0.9697631261691214
61,46,4.930428144808266,0.4172336148672652
56,7,8.72630136004489,0.21157918158984157
8,23,2.9498170927352128,0.6137918778944923
16,3,6.210064056150158,0.5153153524864713
34,15,1.3792770232659568,0.9517064711431299
67,94,9.48326600196943,0.9458851803129598
39,8,4.438852003317232,0.5900555758968664
88,92,0.024873423364663028,0.024753883423453193
91,41,4.98413218316343,0.847102271146868
46,92,2.681589592840341,0.35983462243833575
33,27,5.404912374265333,0.6721719225515167
26,88,4.635528942368791,0.9482013513281706
83,78,6.094650214477303,0.7149120805237638
8,38,3.792069398035318,0.057350140158731966
69,26,5.28691302773999,0.003072222774669897
70,4,2.9854952898326124,0.23203797932624926
67,69,0.6928829210410559,0.07763575576610571
40,84,2.5102336158353697,0.8960826689678371
33,31,3.977974273644035,0.5884174903951659
11,54,0.08465506592757133,0.6803844913904717
13,81,1.5661540838375687,0.34374535391176464
70,32,4.390982356838365,0.4622252382126143
80,11,6.4996703453849864,0.915809779678598
60,1,2.0111652888340013,0.9496414908132494
72,5,1.3238998665651902,0.49677849650953776
76,76,0.21973026748088764,0.9611826411513842
26,19,6.599848837190305,0.6225975506807934
97,84,7.907613923056919,0.1741259596155258
10,47,7.94232102985182,0.417089659665809
68,95,8.71933405152487,0.4730314036714953
87,7,2.273143230465149,0.941884830527986
78,95,0.7959663612567724,0.7807901682100586
43,64,8.268799497585611,0.9771738202667778
69,39,5.785017466876434,0.7661568652822905
83,23,2.157365752619306,0.6391250192403155
1,39,7.83307894998671,0.7311494293277346
45,70,2.623901876474808,0.2777724331353346
16,60,4.989352872396161,0.10707498471394494
11,17,2.729649547935531,0.8204342446345623
80,77,6.588965105333038,0.2867543980568842
46,98,6.693191757579374,0.40880065076358685
91,65,2.903597713934033,0.3931442047883065
98,47,3.8489534597418995,0.41097128089722157
12,3,6.854179151003045,0.04163972350503187
86,28,2.9960330864729148,0.8641050432288885
72,76,2.990552826012225,0.22519385952105397
80,4,5.530859821727081,0.38228882244996387
95,84,0.7199327596161764,0.036165290628866864
42,47,3.9865585053480834,0.6858625250509734
98,3,2.5758390391803863,0.4244230074059112
17,63,4.812652214934011,0.3686661931997619
14,78,2.6316982476663964,0.05211874976005215
22,94,0.6972111448385587,0.48403632622951254
42,43,9.515787037107701,0.009831527561575903
62,88,0.007127490780595158,0.2768895268006344
67,71,0.6313198403195397,0.041723659955730574
66,25,2.2595588525399313,0.3722392929544631
58,46,0.41879041384945803,0.08989144881754885
58,95,7.9549707494999815,0.2616410486899894
26,90,8.137043823209007,0.5491274799034928
34,25,3.5311620347403028,0.22867874954885714
53,2,3.3618329417577018,0.6602261848682114
48,86,1.9010065638418583,0.5222332913436765
13,69,5.81815018391909,0.9740220343398174
28,24,7.023058277512709,0.7600614904391981
72,63,5.96743005840613,0.7802621400177376
19,14,4.847806924816364,0.0602715099349338
4,90,5.268067740613599,0.10222213350582032
36,27,1.8701202916050896,0.8153445538596785
63,39,3.7330060909108207,0.49651367627585763
98,22,3.562740173389175,0.584405499328503
63,58,2.1213512446978147,0.36973486712719394
44,99,9.011727052172976,0.10133689707492555
17,96,6.0744716926653926,0.7885691635402455
68,5,7.739478589670432,0.44061535046167766
92,84,2.1120488722262367,0.74843764299737
42,47,3.1066401119398446,0.11698435709564625
24,22,2.9126818485457227,0.3062502402593752
70,75,7.2640730025786295,0.629716149201804
31,18,7.330169099939212,0.8053978568791952
93,4,6.636463080011932,0.9417900029895555
39,36,7.258963395972463,0.5027535711183595
67,8,8.3118070759105,0.40722458815879303
58,18,3.6859792198408057,0.02514212638286395
19,67,6.436887485383876,0.5813921401237467
55,20,6.006766646751819,0.766861522605022
56,37,4.369580401099724,0.7545066063955993
76,1,3.090850736286833,0.9860410422879425
20,2,2.2122538751064735,0.3612336038689087
34,12,6.975430024529682,0.624613234003694
32,96,3.050587676311365,0.28375970412714413
75,41,7.85166045809857,0.5441072252205454
40,17,4.941838509462828,0.6923794762394322
96,10,5.122349273134413,0.1602343210968591
22,0,0.29742176804522846,0.07935201348428711
38,75,8.115305318521397,0.1872478777553287
55,58,9.866422364691775,0.799434072835278
7,49,0.1231596065458973,0.006019103130803227
7,66,0.32837469341490233,0.09775416603535003
11,90,1.9215277216415894,0.3608638863999387
95,58,1.6549052827063115,0.7388836894644241
55,89,3.4918943509555342,0.501100680228859
33,47,2.063003195825238,0.6254691951767559
14,17,0.2866509121548366,0.5602100703307697
73,44,8.314534585649568,0.6427245865074744
64,8,6.914977634415856,0.5303422875831179
92,29,4.618361545030978,0.4025247887701302
78,52,8.394118930513107,0.8428192212566906
8,9,7.154540591525763,0.7681419593535012
16,78,5.870375376288676,0.2516987688916821
45,15,7.22290052422495,0.5789335840015365
30,70,0.9304791318675509,0.4358317276440241
49,58,3.6431043294920187,0.04814527874300567
58,7,9.247696072347372,0.6255585831106495
57,37,1.2072493171597976,0.3312372284986912
55,53,5.920168404176785,0.021570225426554734
93,95,9.270889095454812,0.990510265653494
81,21,0.0194679340984949,0.25879309573438447
14,68,7.476177081485364,0.5803638849832051
48,12,5.871094693209179,0.016679144323287654
31,68,4.764475343220857,0.5689332577292193
72,42,5.742546755802016,0.63520652056623
2,77,4.337892096324857,0.6947518771064183
49,70,2.689481916031582,0.2346540171653292
4,73,7.586705427025796,0.9864978247869635
0,55,7.520409724267434,0.5196642784240297
55,29,6.569064747805761,0.8986552476757952
96,39,0.4746965956888305,0.981056242780302
83,27,4.201419237470898,0.11884356937492535
49,43,2.002194361076721,0.21120283217260039
11,18,7.816131132470934,0.8352094974903228
75,78,1.7360727049404534,0.5661042498991689
66,18,4.324707202155118,0.9011730389581124
44,62,5.963336327035632,0.590330831248187
86,5,5.694819517099006,0.9624636894458778
86,32,8.635331937347214,0.42298712237986
59,37,9.119485006460856,0.04608631468556046
65,55,8.96179606900532,0.03603698154347401
53,57,3.169648036370525,0.3497514597548387
33,32,5.0467562135871615,0.6017709305141903
31,84,2.7072533056392634,0.47866019548591976
82,10,5.102039222186429,0.11077315602194338
20,59,0.4262601008151967,0.8410589832717688
77,61,6.402821803711511,0.3226962865941335
72,79,5.309314703913507,0.047961724848451626
0,47,8.776170972386415,0.032613562673203145
73,53,0.4579456252085079,0.22056235957099768
64,43,1.5696490819412223,0.0337971025431677
68,96,5.0651056351305845,0.0023909162306597898
48,77,6.4972649113014285,0.1609880879803035
89,79,5.287643091155157,0.9218511948809394
46,65,9.890000965899008,0.2799321359444805
87,9,7.147425047713284,0.6823879887739884
83,53,8.643393817035033,0.12893600630222457
32,9,4.919093266911232,0.6053801243752489
89,5,0.18834847867871263,0.08013066781696387
3,97,2.8701443642449354,0.40692134454910356
50,62,6.655688252252143,0.5996512305554584
52,18,4.281205400374355,0.6759452861604844
76,71,8.61244360242969,0.5435611879375963
5,17,7.416150191560677,0.6566592232635489
41,7,8.476104785876835,0.7795940299564589
19,20,1.5760014696603353,0.4746578912950198
80,32,4.164773840970143,0.6471675445446147
76,23,1.057749908595711,0.17445219531009748
12,24,6.31441926298651,0.029933610271667965
71,48,0.1112348878803271,0.7676168247170366
5,41,5.523272358997677,0.8850282091920424
76,40,1.2565672000595307,0.5671999232754229

thursday_contacts.txt

200
Agent Index:Interacting Agent Index:Time Interval:Intensity
9:80:7.0382921246333705:0.9556820598269046
44:69:8.573011972540401:0.07924998376906311
10:57:1.5325958641775272:0.3093902519850843
42:10:8.701123888970558:0.962585163449696
92:17:6.157994099854182:0.7080803028223787
30:59:9.725783500251366:0.7314558080633391
75:50:3.8058557518124214:0.3168807779694327
40:62:6.43557676676667:0.9763099081805485
92:27:0.04439101149650737:0.417023535886784
9:96:4.064443587754805:0.3627068026184982
57:97:2.9937333519214326:0.04831281631023343
15:10:5.893399813507008:0.47319793911862373
4:58:2.189181419606845:0.6803283844589387
71:62:1.7525285905496146:0.13896567010484562
79:36:8.506352552863355:0.8691976202423097
76:53:6.137904467285217:0.07106840002404269
84:36:2.560188707141876:0.7771950084819546
52:43:7.668274637265973:0.7312993690534784
54:93:7.939847618469336:0.7776433290453468
81:68:5.384535911579822:0.23525255976832482
93:16:9.920757993127333:0.8934543441881765
1:72:7.758134739731689:0.24703891010135515
94:31:4.736159938253104:0.279716657877369
11:91:6.604975713945529:0.2980626079316899
22:89:7.307471438634465:0.8331314885020186
66:92:5.074020217973926:0.5867164552992925
46:20:7.872054213346798:0.07697026703119414
68:84:0.014172442899552662:0.3614765333867669
82:9:1.0094025461973999:0.7513051533741296
88:35:8.916625728448652:0.13212679832443874
3:61:8.879634677276655:0.16048328962110692
30:63:0.889562352523886:0.14175368432296465
60:41:4.057982037703882:0.9460153048461237
27:8:3.4522820993929315:0.263099965041526
76:33:5.350168457592791:0.4817138552741015
1:85:6.03291472992156:0.7362304926092693
47:46:5.151177915023272:0.524784652285693
43:80:6.3260521909884115:0.20238179959236602
46:6:1.4188793789888487:0.9554001282463949
68:2:8.760839211072776:0.5522656652793739
71:87:9.866051901497453:0.050298054713874385
90:11:5.444448491136552:0.9347109216145608
17:22:3.1981154659531565:0.21362937499069423
55:24:0.31133364718463397:0.9904127541387862
39:36:6.652772458814239:0.32511126196824647
51:17:0.1908445268782133:0.7776073952742899
38:27:4.9287219964583775:0.6766708526045345
43:68:2.345297470120279:0.4888662207402883
52:77:6.2704802068309515:0.9727888002522699
57:65:0.31410385209842073:0.5678620629870902
58:9:4.5043887132533875:0.9529991526026419
32:91:8.856779214806078:0.6205084549405729
36:9:2.99684211320568:0.20084350883099966
52:42:2.234391573182888:0.7936651279874264
66:97:5.497317602861229:0.8037936396717074
43:75:8.631273282189033:0.6467101601379966
91:93:5.899984231792686:0.5589976222351022
89:98:3.4357223028901385:0.28650955658054356
32:69:7.035480596894519:0.715682876100546
36:47:0.702440123848932:0.37014161233455
97:35:7.795073444954294:0.690234242431429
49:26:8.188641044378683:0.2854457891281498
23:48:5.8860192391972745:0.9858892118609247
94:66:4.8331558942120045:0.9392247213322765
1:28:3.940617896767412:0.8119157936104054
94:9:9.511243180423108:0.4866582339311317
16:13:1.3706419546146398:0.4606816198641699
4:94:6.079250764705122:0.664159392552753
8:95:9.367600455819955:0.28978090388795374
87:94:0.009281959133973228:0.9533789826774827
58:89:1.9742256941383884:0.12038943184213302
84:58:8.2927498262796:0.6289705093238441
96:78:6.145813134167143:0.8797391678218863
15:29:7.304166478831061:0.37660257369721994
33:10:8.415998407750381:0.3884970977718254
44:83:1.5842806601754167:0.8979250123521796
0:89:2.301629122139719:0.07010399854914007
56:69:2.2033530452009864:0.0386290184572774
39:21:8.365740266584353:0.8769992573517463
35:8:0.6039725358473025:0.7231100758772858
16:29:1.1869583871524414:0.7934164894804442
13:60:1.1083952560802401:0.8160926988887836
0:76:7.296924179379242:0.04145973049986551
96:10:7.083416807339589:0.31052447378674664
67:95:7.3778033130326035:0.4699849766808416
14:60:6.545948462503307:0.28553558881248886
43:46:9.73653464036593:0.08518557315885533
39:55:8.001834086567442:0.1449534039528918
96:29:3.3347114644129827:0.4751405480035604
1:3:3.9843061077162076:0.2904432377787004
22:86:0.7737914282516112:0.9949485827235354
39:54:9.44229177563296:0.7555177614543236
13:8:2.9093184369975322:0.4074161100925633
90:56:6.3954756331314115:0.3630477505326515
95:59:7.741332185095796:0.0686127194562044
63:6:7.329546037754219:0.047602827898245326
57:55:2.2058024651955788:0.5420130616536876
45:64:7.972444005640122:0.9346612569555631
17:96:0.5028192989338531:0.602861775981457
19:47:1.1593911790613642:0.3294905796595542
5:73:5.053371460984918:0.43453735386468306
47:14:1.671267644260136:0.7617381750767552
5:17:3.8521114646242918:0.9027839801336598
30:95:8.155368057480747:0.3039655272261964
42:0:5.301894475432239:0.12825534758882506
59:93:0.867332721346481:0.8543727044000994
61:24:4.545866060225478:0.29454914795747555
85:51:7.265989073625477:0.9771268329383078
19:63:4.114022700622767:0.40193113111336387
45:47:1.0866634685103327:0.6439994336060687
36:54:3.3080183883197356:0.05478430344551444
27:43:4.491839482154164:0.34138799298187006
96:83:3.2537132537203695:0.4690103280088781
3:17:8.210480957200954:0.6785354918047558
60:0:8.166288756067337:0.009825187110891531
80:57:3.6405957600181194:0.9731578767400095
4:12:5.167714502627886:0.08442010394327959
36:60:1.5436074012458656:0.38231602955762645
13:81:7.487919848355968:0.7720185857814735
86:81:9.832572756034889:0.932466012208083
85:43:2.3720109674290333:0.8201010230106307
32:21:9.953311147163454:0.011674606541613675
41:94:0.5708653782110851:0.8171371735366577
80:62:7.826269089129624:0.8054818446439931
59:42:3.4752456682963286:0.16962944629105137
35:30:8.950010240117711:0.7321501698872532
36:68:8.049175109984219:0.8751769192948862
90:78:8.313662977740753:0.021380973721606078
4:60:0.3863981340857503:0.64324289181507
92:5:1.5821836994859562:0.6832838495910474
92:35:5.759253574243678:0.6722529014859097
63:52:0.3248004646469238:0.889428065406194
76:58:4.657899334607473:0.1269797853566087
20:23:2.030506718629682:0.623571833876037
67:56:5.721392098935411:0.9740022252292175
39:71:2.3800650310864215:0.39527668002658867
57:68:8.743679973789662:0.11559725358582273
9:57:4.426655660307315:0.3831574189336966
90:35:4.500662171278994:0.6457649176495883
11:15:3.610230581472095:0.7749786563954427
78:30:3.537127278785983:0.672334116297062
94:51:8.461216862609769:0.5454202707832448
75:26:7.878760507607612:0.23953114774140905
62:21:8.03728326258731:0.7317362756887453
41:28:9.881900293689839:0.5158710625716576
55:59:5.187576120008606:0.09037421440274218
66:30:0.2822708894510517:0.8239935690953065
14:86:2.4350591158958035:0.0451732234158444
10:13:7.540167443077943:0.9128796041719606
11:11:2.5872234084376977:0.2745810085653637
63:18:5.8349459811306135:0.2628000302629594
8:70:7.389502622983511:0.33781675668735134
94:92:9.716335252977252:0.7560749193156806
46:6:6.697843308826012:0.3071889852788805
14:56:3.0115810056757883:0.019309987557965647
43:59:7.70335287625792:0.07801146422444183
13:19:7.1531177132632635:0.46172693965817924
32:58:0.0947302768753655:0.4435550454724231
65:41:7.846459075221219:0.20486103557339408
79:30:4.855795600531242:0.6324741482930949
94:72:9.192984245755726:0.47308851194268364
1:19:2.5189273919855326:0.5697138720443463
43:89:5.623253504764232:0.3205680634577276
23:49:5.996101075819311:0.3092468469691083
24:77:1.1575274845932815:0.29449933851511156
97:14:1.542998906358647:0.5324160705560573
24:34:2.8547843066335075:0.8823443962826881
88:94:3.3126621067162567:0.36194029301617625
59:89:8.775881914064165:0.8253261132282622
6:80:3.9288893586194327:0.10275972449442927
71:59:1.6479602486244127:0.04174936740649626
55:54:1.5015663207731555:0.06830746730647275
52:2:0.9008968763444658:0.4046997291491362
53:73:1.8688136024961355:0.5830614775602793
36:8:3.9728441806543637:0.135649600130743
64:44:3.9868053958346583:0.12018302290146932
20:81:2.3295936709442913:0.16782594204362378
70:98:2.4625686349731533:0.7447068575679722
83:68:3.0500910480749255:0.18871352909901662
12:36:1.3232525955055607:0.6301253264588437
24:4:8.511899833302598:0.03323414664925628
50:77:7.491775365021729:0.2614312397444818
77:8:8.606105243413465:0.6596871488477454
41:11:3.3624084193448764:0.5842044609543943
98:13:5.435720234282897:0.07937243127913918
21:65:0.5322387729059574:0.38013044360335524
11:81:1.473579773422019:0.4182034100396437
71:6:4.054832359579801:0.994191633021709
91:44:3.9765231844593063:0.6317211275561101
93:14:5.805280716375303:0.662059365871648
86:69:3.1533754603139252:0.6050328383845425
36:22:4.280692953835742:0.7771808522245846
76:78:7.500105794724697:0.5285135775933644
78:73:9.218428037446689:0.6502891286543955
77:24:9.141627497458963:0.6764185110368829
99:20:2.9402718121860625:0.94933964891294
51:60:5.440972736761095:0.08167094302929112
53:46:1.4756695868875869:0.5147498179121255

thursday_events.txt

12
Location Index:Agents:Time Interval
4:77,31,38,72,99:10
5:82,32,83,3,34,99,17,14,16,63,9,71,30,4,55,33,74:10
9:69,63:60
0:1,33:10
2:56,84,16,7,32,61,16,91,4,82,78,62,61,56:30
9:51,93,72,82,73,58,39,80,29,12,42,49,30,47,58,71,20:10
8:44,68,84,13,76,11,20,3,0,46,15:10
2:64,53,99,84,91,79,25,20,4,11,60,53,56,28,77,81,9,8:30
3:13,99,8,72,12,60,21,51,23,21,40,7,60,15,54,93,62,25:60
9:57,86,23,14,99,44,86,56,38,57,6,1,71:30
6:56,53,13,83,76:30
4:48,44,36,38,69,60,52,96,56:60

tuesday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
66,0,1.8172369246500197,0.006012368176620986
74,92,4.063473307316592,0.44764113765276414
10,28,2.5078058170016004,0.9556666937503718
28,6,5.0663925880424,0.030157397136076347
14,34,0.9023473288248696,0.40762532229169424
82,17,4.409706397800109,0.8581145623573369
43,2,4.156581552776811,0.2765556802005179
62,27,1.2914385251065508,0.009513514017428504
39,15,4.862643162759489,0.3406184083074584
37,61,6.003686928238557,0.7615036747316247
26,25,0.75888883318599,0.7458770745925443
31,38,6.903844182398593,0.9278421364738013
78,11,3.956008580201522,0.6503839273684523
90,48,6.769446426997564,0.10888629056775989
85,91,0.7232151612815041,0.5467854056064144
48,61,2.2328510614263974,0.44718384957966695
78,96,0.7063783545238811,0.8896588296914104
6,48,9.293321418971457,0.8244605077577327
59,60,1.346910814314961,0.1075512883150157
91,18,4.159645334446736,0.9035452588829113
49,29,6.086884750360499,0.3383082213819574
49,65,6.530330988330888,0.6544708521532662
22,21,2.359281478670309,0.6932914366816318
36,11,0.6214541407681629,0.277228132146625
68,35,1.384050140411951,0.9858517993039427
27,3,8.941691191907072,0.8676675804245452
21,20,1.805147298856169,0.8465608015601254
84,86,2.4225104203737757,0.43038948235552
26,63,6.483320436344188,0.6967437250929029
29,96,0.8799624974435949,0.49727731725706925
14,27,9.114149895145916,0.3607316314698501
56,94,3.436609544657556,0.03583350304170885
92,83,7.224940852275099,0.796582912719368
36,14,1.9585817639216085,0.5523849902001531
31,13,4.484949871338362,0.26768807176332765
23,83,0.015340968768530194,0.10036970882600071
56,88,9.644090451403999,0.4768964287882195
49,31,7.09441961853263,0.06924208901127449
93,71,1.9071767620603308,0.1034237044014571
47,60,7.971199482192748,0.18391554416077116
26,87,4.1078464708973215,0.27716258035987584
4,84,1.187407367890705,0.591748889516374
40,49,1.5983463783361906,0.7862069123228235
35,37,9.477098230814514,0.6785201377250942
55,46,4.4392432669147865,0.8755928204327293
78,45,3.267941991588039,0.8380363457231991
54,81,7.164718663466019,0.1620363873309948
5,92,5.380116701363553,0.20282298378753127
52,93,8.344482466024907,0.8636854730280744
62,23,6.701569229007836,0.5040441233693438
73,98,8.118088212815017,0.28909432113979583
58,27,1.8661553338044412,0.950297468551335
66,1,6.492082007243933,0.46419110664867524
39,48,5.019406802332588,0.8247073034288753
14,50,1.4376340264666165,0.5119841394352085
99,26,1.8385790775577826,0.3252535154166645
56,24,3.81520574287756,0.9493833892557094
12,85,4.590511092408569,0.8830225997371698
86,49,2.256171992717796,0.23012674462476623
88,49,4.176669994920607,0.29142550506828013
78,12,7.834379855411703,0.7857791594264615
32,25,5.98743219341273,0.6017363643492531
22,64,6.920059245930357,0.40418357976938335
61,67,7.614317747527228,0.1194139638185292
49,48,5.287355461807298,0.6925352138956495
92,33,1.0903989744713527,0.7393398513964201
67,19,3.4874417270160754,0.7594694251871744
19,47,0.16504452846147655,0.6950781665506448
23,53,3.9361428532544496,0.2553940611426446
23,35,3.5450049204356415,0.24938808055450234
90,68,8.123188697688832,0.5077132957074547
45,38,8.489594860937716,0.8518278364060441
31,98,0.7355476804641636,0.4276085779375872
94,67,6.91245915282636,0.2578313271797378
69,66,1.4941880378682837,0.48289983567784056
30,14,8.822005482549379,0.6672125252010723
94,45,6.8878032268713305,0.4353270629542777
92,56,9.772394909616143,0.6595689986069757
98,37,7.6484985184170124,0.3917151822749264
70,59,3.702798635248433,0.061744383601446184
24,86,0.9885849505381816,0.46973558683739525
20,2,0.04912805020394151,0.6576301400716553
90,77,7.622215907800679,0.45040666331175994
63,97,2.5762955502800877,0.06689564467549869
40,22,3.184498179260392,0.4423936808955783
23,85,3.913729685022269,0.9982090099271226
19,45,2.038237218553247,0.8103181976684201
60,1,9.762520396551164,0.5486821038416273
0,29,7.192738832418542,0.6131583807313137
84,17,8.749307528435297,0.5225577012900724
83,48,7.086116913452992,0.9900778680704334
74,40,2.2821555707866126,0.35018334993217504
3,13,0.36995864315547355,0.11846637314579411
50,78,3.941694960962491,0.6034833071010233
37,56,1.2302019767572725,0.4601094370224549
25,82,8.558629082547203,0.18125777696742207
56,50,5.666529201636958,0.34450397914279596
46,23,6.008069289249349,0.8728988105709846
2,52,1.7097510664836613,0.13851074831486443
7,78,5.960466354795643,0.397621705031562

tuesday_contacts.txt

100
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:45:6.213436231456636:0.3731609728338897
89:81:4.579771459232793:0.11434815810612176
0:16:3.242934650368129:0.3141918044148243
15:82:7.975466857130672:0.6128656081719708
12:29:1.371000381482833:0.3302304647764601
14:7:7.821227878776517:0.02845995074557328
85:83:6.395632457075665:0.7751395265422735
76:14:3.5755744413957458:0.5574299399414683
5:62:9.58634391552728:0.7052359820907551
33:45:0.7076539116973712:0.29372417115115146
46:38:6.129146387692819:0.2609382873034033
25:66:7.924473535575229:0.5101208649146122
25:30:2.2435787778590233:0.34060744129158593
34:60:4.927953728053525:0.6700587895933668
47:5:2.684771776287241:0.2574115092392507
89:58:9.625734441309854:0.5968114997896796
3:61:5.220803442701763:0.8620230034643891
29:91:2.4686173240322318:0.6230867759670403
2:95:1.5754431658870482:0.6105753814387738
99:4:7.691047725707579:0.3413757611023611
78:93:2.9318192055348415:0.4648065055530436
51:24:8.197221547880316:0.9718876331351113
92:27:9.198396797255626:0.16543165396821713
60:99:1.9852917319237706:0.6754749253049644
47:60:2.926189356051805:0.9833964216145247
62:53:8.777231457375235:0.7284431458587418
7:93:9.19597804582149:0.36088453244829266
18:16:6.90568418912085:0.47964664098450793
39:75:8.829446299574954:0.8566338306237319
79:27:0.30209164979904113:0.9162862348077758
41:47:2.8150099179100363:0.2202297658034008
34:87:5.121262434689546:0.34522516985978635
52:69:8.376611116331295:0.9721430380048826
31:7:8.568390078953934:0.2609747271152305
90:39:3.3323074765201985:0.3738473348752833
8:48:9.61161080916259:0.11255940822682353
29:42:1.046490193853703:0.9703762943992277
8:52:1.3703006046548538:0.06626361915074641
75:91:1.4840408750275702:0.6152611616346282
37:26:7.825349435322081:0.8239794472248464
22:84:4.940400200673159:0.7619456896388551
59:90:5.784623371260322:0.10666187082198009
21:86:0.3471548050285156:0.6173979174975259
42:90:8.178704998755766:0.4609972690917916
29:95:4.8406036225093:0.09106867309864752
53:58:2.9237402820948066:0.8880798506295625
64:21:9.633445470500215:0.3795317805234453
62:36:1.3372576343073461:0.9333367878739499
40:29:6.759730185924982:0.40384662674309035
20:56:0.9484204437096111:0.2700243168712728
91:13:5.593847444401174:0.14251730023029452
8:38:0.8118455773477451:0.15534571880671544
9:58:1.9613155867738086:0.9964979966329512
28:44:8.039543224362301:0.92149645738936
88:93:6.612640065946129:0.7475311567942813
40:19:7.620144079622059:0.3182416084415045
57:79:4.092781780687574:0.7267655904498955
11:49:5.677218509537134:0.620710266950606
87:9:0.5998969632207896:0.9462038643902423
79:52:0.3517405280543384:0.4570039635821589
98:32:3.0513911846409467:0.11192808504123763
48:26:2.144916542978387:0.7649703267181862
19:60:2.5378878697564122:0.5125815380642406
82:21:6.355984386247059:0.7538837278153913
76:82:9.092557531602177:0.74210496101346
18:7:9.409297527034369:0.9147893913873082
19:22:1.2673823917478577:0.3677686846010142
6:64:4.108843727499912:0.9996670729666973
59:39:3.968592643367236:0.6153826071593608
31:3:4.102429735302982:0.8091549649634372
75:67:6.408176908148027:0.4632439420199308
34:51:3.29285916399488:0.07362754325129339
96:44:0.8606828956909396:0.6380526760438952
32:55:7.632667913997516:0.8151815459035505
92:96:9.988069711163364:0.4201982040837776
76:55:2.7540455819170298:0.2728983302205812
71:69:0.2404635910591657:0.7019088464354047
83:7:3.7833428461767804:0.5059053649402946
54:81:3.347201881226524:0.6078784506485931
78:11:2.377145199017725:0.7250049576896354
28:25:0.6506573295260609:0.36397213695897845
71:55:5.388066989872032:0.7128044897824171
92:36:3.198366280780307:0.22223364625950692
87:38:1.1459230613858307:0.7737585259260796
30:20:4.088682224830098:0.9133713693060721
61:96:0.8000325343560455:0.9509401081347084
64:96:4.276249476121806:0.5545140479374902
30:46:5.864776032398824:0.9878159000860662
41:70:5.026586660801719:0.2196825484242737
5:2:9.153538434547116:0.6408688000252408
94:57:1.5258929617167616:0.3422495707436871
3:89:7.383070659810189:0.502938336341504
97:34:9.399292391381532:0.24970335740592198
16:30:3.8929316804406353:0.22739535842351322
69:8:2.1316090927287434:0.49286696627785687
74:15:8.485630359109985:0.11702412916620186
27:7:2.1637920715821535:0.18835559198252183
69:82:7.987112691829116:0.8388020770869082
66:3:4.0359322253773:0.8073362451987808
64:21:0.4793067219400504:0.8144255386406495

tuesday_events.txt

14
Location Index:Agents:Time Interval
1:73,50,39,25,14,89,73,35,67,97,80,81,13,65,3,41,71,33,10,93,72:10
2:44,5,88:45
6:46,48,82,17,74,98,75,50,71,66,80,62,54,30,16,4,88,9:10
1:36,65:30
4:94,43,78,9,76:10
3:84,73,44,97,4,70,95,77,74,99,18,6:30
5:16,28,88,88,12,5,62,65,83,26,8,40,11,10,6,21,59,21,5,80:10
7:69,18,55,49,68,69,16:45
0:97,73:10
3:39,36,99,78,15,63,48,86,67,48,73,31,41:45
3:86,56:45
9:79,20,8,63,28:10
1:49,81,39,95,54,48,86,62,97,20:60
3:5,68,87,94,53,83,0,53,65,99:45

wednesday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
61,65,4.328046668612032,0.985927575919051
84,10,7.620089774123224,0.6894406168171453
85,1,0.49660140501557426,0.17113663902996323
66,61,5.911171322512088,0.8477257541038907
35,91,8.445849141778336,0.18743157653601095
25,36,3.6993149390935107,0.19610294657542104
20,52,7.652041015033962,0.593720732276105
38,37,8.776325054105284,0.44489657060726107
39,14,7.182823185974644,0.5994661734860806
56,84,6.1841616156809724,0.5447149497146385
41,12,3.2364163292005967,0.053084247009199115
70,51,1.0121018670684523,0.9439448340792216
96,31,2.3861925565270967,0.2077179825704527
30,15,2.7503704544016703,0.6584814644942648
91,57,7.701924871045484,0.5625320422657776
72,88,7.966795926989233,0.765764986379312
49,98,9.395141283755242,0.4936464057057941
45,21,2.554777957118036,0.19138996793090324
13,75,2.0523722473181785,0.1172558789366045
49,11,4.510140468018347,0.6685610259751634
67,76,0.0892268472350799,0.7595045810121461
79,81,6.905425986319638,0.28652218091888504
92,41,8.965797259331046,0.1111830483334203
57,88,1.6077903602598542,0.23666508265851227
94,18,2.9623650828995207,0.8286818654503474
7,43,3.5320340020252647,0.4900135259408406
70,23,6.9623452942853215,0.5989422292851269
46,90,1.2278281431609717,0.9809655525147104
88,82,3.6256539203044325,0.652968150803639
58,98,5.840166797355345,0.08341004682389153
46,92,1.9089385990892749,0.7922878921350877
48,4,6.5047384781314985,0.8623586866547435
97,17,4.859962975118499,0.26880367017223195
52,54,4.117961501993132,0.5779175887357123
44,44,6.3947200819473515,0.5191303061352239
17,20,8.77562936067531,0.5443645239302862
83,2,9.291858605237366,0.0553401914041618
96,38,4.392063292685494,0.7924637198155704
91,89,0.8634506601414438,0.8852301102932261
65,12,1.506487876908037,0.7230459861844032
73,9,6.040549612355375,0.48445441990887483
66,80,7.6930935709725325,0.35808034570668446
21,23,0.6893080651209738,0.6919562246517463
26,73,9.453648282840796,0.6472476848303975
80,15,0.9429433783029362,0.7874479617653929
67,30,9.713077959999799,0.10013524617085334
73,93,4.430791012289528,0.642167550276483
93,69,3.5884355542121726,0.30318563816332755
89,37,4.286112169752502,0.5006202264717081
91,69,0.7687621918845333,0.07230042085270683
62,81,6.654205605701176,0.045457620301632806
9,55,5.672536509959393,0.8385270018459005
68,51,0.24483441268529527,0.25713405068918527
14,99,7.621207045483196,0.8167099553391018
76,4,7.024219118326971,0.9943720801149437
49,69,1.3221231617256168,0.5328342003072312
66,43,4.887747382953863,0.20394253573112664
94,98,7.4126996949549815,0.29367037924723427
37,99,6.286106627722551,0.2619126643141313
63,6,0.832812218767901,0.9470207041963251
76,70,1.342204503326736,0.2937613524203415
98,37,6.626794154267101,0.9018444572054648
61,39,8.09451181666891,0.1964693047685787
42,99,3.3919911278057735,0.29072383449269557
44,45,1.2717686725097233,0.20486822969594776
50,69,7.69505963695207,0.6525106814545318
3,59,7.885329446684147,0.06484688289840057
67,70,3.253587218808436,0.11322294635609997
27,59,1.3979048716689257,0.888610582901755
16,15,9.725008138989033,0.6695882206932914
14,97,7.050542723813264,0.2280615100325062
71,3,3.099115781837357,0.4945560296945123
79,10,0.873045716297951,0.062218995783478004
72,32,3.837300145160926,0.34717495460382664
81,40,1.2811957884594871,0.20557462707871643
64,5,7.560186810066449,0.038954161433137124
44,51,7.547289644141214,0.6010275473664155
23,21,4.476984832575663,0.5376453780892193
34,32,9.401593609226843,0.02609028242161371
88,73,5.109582358750917,0.2502588811398663
50,20,0.2841553213291781,0.7347148282160669
53,49,0.7949851053601231,0.6723526724414021
11,59,3.1526743438539437,0.038438264425936564
17,92,4.954915369342104,0.5765202209730164
97,11,0.7430553059434564,0.07048154609904489
97,74,2.2559935402409836,0.19891247729586103
72,34,7.1045669560761215,0.7861633793033821
26,12,7.775725464106991,0.731181169906113
33,97,1.8568769510888994,0.5079134227431802
68,75,3.630914775760127,0.20061235101170882
16,94,1.2526813019006278,0.14847667968699463
57,67,8.877938195945346,0.9337752911284403
65,73,2.7637284496203307,0.841993871379342
43,6,6.325645938549286,0.7258209512232121
22,17,7.307170412248213,0.5234005003613431
73,51,3.13037572001468,0.6477820575490124
93,32,5.505042987502752,0.35835921674752025
99,78,2.6454288356693367,0.3164982894755156
57,92,9.812289350517588,0.5623329686119016
1,37,2.0648879347633176,0.24980254796571322
35,16,1.891855439278518,0.3822843921947797
22,8,7.762475116667491,0.17771103329866533
38,11,0.5324183508455382,0.034949273885693244
76,77,4.261420288493316,0.37360747330332966
81,41,7.164236487952083,0.7101325593900163
66,43,0.8935357969412583,0.8657665711676644
76,50,3.428200761017467,0.25890478593172395
99,46,0.8468548979927304,0.3191644147119663
9,22,5.089410865355925,0.1669946263236376
86,65,6.166238548237065,0.6861667846158687
5,40,9.952574759084564,0.5740822008793284
31,61,6.196867190769683,0.5013408241986123
1,31,9.786293451410323,0.7493047221140382
32,57,5.955203040682413,0.5154476797308712
53,16,7.899925691111145,0.6717976199569338
44,89,2.2887770907730607,0.5437877431298486
74,3,6.927331092250767,0.7711408817356543
43,85,0.3502160717277869,0.25177275481441486
50,68,3.191663096305529,0.7859630170530432
31,90,2.466720863401598,0.4172613934183278
70,65,5.890885485744323,0.7187113099960679
99,17,4.516599984695651,0.1336996859828995
13,53,8.345028778119513,0.42274493851882244
70,81,4.843046108814995,0.29882949899115174
33,96,5.086940545320671,0.5693637966446243
71,54,8.642047367290424,0.8744982799729936
17,61,2.340219219631605,0.31956414897879293
26,57,6.074845384645643,0.5409699197771384
69,21,2.1294574890971907,0.2709643921783146
49,55,2.800586970486286,0.9267876256478454
26,42,2.044916946626083,0.16778512659495093
14,32,4.404038389281584,0.45118631201309967
23,73,4.896898976924822,0.6552105197229967
14,40,7.208749458944464,0.4052460999354285
74,34,5.2590702640617675,0.3484693657371496
57,4,2.284649090331927,0.9101126726234063
97,45,5.31945308320389,0.9879016506375161
99,6,1.7667532909232442,0.981717913265498
14,21,6.949122932005802,0.3766728232278491
15,83,7.233802451412097,0.33188304141952363
27,53,9.16598064470289,0.5799604444665396
95,61,7.772594985484835,0.23932845465875385
50,1,9.12755457213107,0.7933504476682264
49,71,3.751756909835703,0.7044445200349657
99,0,0.9529698943986609,0.1734518386196756
17,9,8.368977669400305,0.6747855066510295
38,8,7.922329631466242,0.17003065570665266
63,66,0.08248394411642601,0.025204125415014267
18,14,0.35037893477147697,0.2570432890998742
20,12,1.1093342026532693,0.8637866257748087
48,9,1.4385408523522425,0.6011023785605366
27,82,0.8959795585527275,0.8263894883421955
49,29,9.912928347878655,0.1687802962067968
70,31,4.826779819867998,0.7976424167280071
11,94,5.4073162992369515,0.9911025818470419
12,15,0.18557679011843575,0.34219655525217973
35,91,8.388443772655819,0.6190861620759786
37,41,7.3793540838599405,0.27457526357482476
81,21,9.990002615945281,0.15798023159047025
2,68,4.7421748908980526,0.7041157877982428
46,9,3.4582306462648438,0.0982649453911274
12,35,5.7297343030678425,0.8156908469788527
21,80,3.1452009426140846,0.36813466433394093
84,77,0.8745670685564488,0.462304690992159
73,79,5.466148061501178,0.03678380515291757
82,11,4.212045965447891,0.5243474989886192
21,28,8.780982456238265,0.9291271311944286
12,39,7.352457920431851,0.31530648470158695
42,22,6.701632453253513,0.4388446954693722
47,66,7.604844544393275,0.8250566829750561
15,70,7.194567032478998,0.8641595346076059
24,97,2.7169131094048824,0.4711799178846521
21,18,5.221192538365581,0.8151971747381243
84,4,6.8823319278684565,0.550435690861847
75,75,0.8186212924814562,0.23191936021034265
71,19,1.6874259567261274,0.6506780037830153
73,47,3.334031924182553,0.39297451944792383
29,79,9.355379226552895,0.9853684666915478
4,11,1.570152606523092,0.3513192118111663
63,37,0.041291099445380786,0.4773016708317034
21,20,6.483665285102375,0.10154344154574824
19,84,9.518422615895078,0.4711081541121517
83,74,6.048261008286692,0.4656840049930332
7,56,9.5918004379995,0.7993214229516233
22,97,2.6271345433182547,0.735793220201614
75,9,9.04822832573235,0.24573275248001913
93,26,9.969699154601656,0.30249560798448794
91,19,1.105952908729575,0.7445486552483508
47,38,4.32787900607325,0.6363361898649607
31,21,2.801506738243793,0.47238990052666696
34,34,6.227871652666583,0.7127394795977807
15,36,3.9270782124055383,0.20733096743112944
29,23,6.319658248786027,0.45740263269721837
58,26,4.676403660977934,0.7337944076762869
83,27,2.901365627284349,0.7166612123493752
28,73,6.9471069326973245,0.22598254492640868
57,62,3.6176705454479565,0.25728408216306686
69,25,1.594419559468615,0.39618628353300334
3,56,6.452354020091264,0.5262731669374942

wednesday_contacts.txt

500
Agent Index:Interacting Agent Index:Time Interval:Intensity
35:35:7.326649613582364:0.01880486356965283
35:53:7.365575713168727:0.07226775243298234
55:76:1.7425302606537474:0.4079740971798641
30:95:4.307720149563242:0.5889349769548973
98:69:6.544728451402593:0.48025388395194957
6:88:5.072512252507798:0.8389958044296311
45:47:3.8194943780736357:0.06756325928583062
60:36:3.4337954746207435:0.780608576755907
28:58:3.9573951790562067:0.8814937045907152
46:4:3.762262794683556:0.16848724165974238
97:11:8.693414441617387:0.7460650092914961
92:75:0.660206718405888:0.07335924898540769
46:82:3.8637328951746017:0.5951215759777483
28:22:9.800867101130882:0.20271952020628436
37:49:7.060494204085794:0.5366861962135869
30:86:0.33597015028233757:0.10758491189019004
46:77:1.2885169728264612:0.10729300794480534
10:50:5.187607731001368:0.6703832544752654
74:34:8.93544026576964:0.04549012455350221
73:68:9.591015242175292:0.6962332597341828
10:44:0.5823210493669018:0.9689789406714906
33:32:8.436879771291043:0.021557896227508477
22:11:3.438680750408264:0.5398584734054681
91:60:7.210324988424461:0.8787960159091067
36:78:6.454046898476056:0.18222360148833006
44:28:0.883667185292053:0.922569686376188
64:47:8.689647085944292:0.1418009831093382
62:89:2.7679253812446216:0.6435851301783628
78:1:3.352951970635684:0.4754766055188496
69:62:8.345007894678666:0.23798159130601404
50:92:4.481913480277774:0.006233696946746248
98:26:1.1338952630227828:0.673962261965644
37:99:1.0643710496145387:0.5908347291714198
28:4:9.920590767349442:0.6963377903623542
18:50:1.3809350166002898:0.9872588601452001
24:49:2.094116142874566:0.5748045544228527
25:42:5.693608169443762:0.2879191230183471
54:42:2.4086948504552073:0.02547746034366105
51:2:4.451408189223509:0.6032579558452265
60:20:9.005811670608857:0.9856136917050264
65:67:3.19976980590579:0.9186950562480023
3:47:7.181260983583534:0.3040250437725214
68:57:0.7383565151524951:0.723133921333527
55:66:8.367449475616333:0.07980719114975277
9:11:9.97517977101612:0.6127447997868063
31:33:9.975729093568951:0.1045218021830403
83:27:6.640381396448985:0.15661340183018102
95:37:0.726266659582897:0.8758271106350278
17:27:3.731549646431953:0.3229035252474013
86:73:9.8942767252751:0.3990519563823193
3:78:8.720720998194327:0.264559175826406
74:46:5.4153651996120225:0.4173530824931695
95:95:9.484928224637814:0.022785741619000488
82:9:9.340319131198878:0.7952612413547455
8:42:2.475879993476423:0.3886327787838908
82:97:9.866336760171512:0.7188202155620746
56:34:4.302447425405743:0.28633873882974836
81:45:3.724740927783703:0.9167028205119899
25:29:9.199922961967527:0.835041826144438
94:68:3.8825993503747194:0.1168420663032067
94:13:0.42404086307323285:0.7521313232183118
49:11:8.607236167340723:0.6186404850761289
20:25:2.005728328797962:0.05980946246418717
59:91:2.961636869772523:0.1292205857836043
52:74:5.743776860036771:0.9984405022263465
75:72:8.15665662213246:0.8856720647622587
52:74:7.689955305723457:0.24021318982090323
59:44:1.6743181640329041:0.1378869655341155
72:12:7.638936582230792:0.8289049815374233
16:20:5.910006486116654:0.8403350173657851
80:0:9.524612020268266:0.9890454406593926
46:85:0.1978619520777969:0.7542955579200908
94:45:8.182315486473957:0.5539970521800692
26:68:2.410142904138397:0.831822112439275
12:59:4.111959521312285:0.2511690380469713
16:41:8.11653477037407:0.4864135139348226
7:6:8.41408841840117:0.5166597024174876
82:88:7.539262351768925:0.4934590077378854
23:87:2.0191699791040407:0.4650993959348293
14:42:7.959803809049787:0.3859274394432979
72:39:4.988558132860267:0.9719137961462334
36:87:7.904796353087814:0.9033328489705758
49:41:6.441286834271189:0.8596066636809503
26:45:1.5494084880416248:0.21286592285078343
69:84:2.68713252804031:0.6352927537106948
92:50:8.125056564396175:0.964640333194231
25:50:5.745327231004454:0.4996238922147982
48:58:9.430548312366577:0.9662240771532448
51:7:1.5407008662168198:0.43018447051424324
94:85:0.33708865480739925:0.3999028227241679
8:42:0.4676328396476981:0.24681536908527746
3:54:2.082373656993525:0.8211009673756547
47:5:4.176150167437918:0.0068115940272549524
13:18:1.9920879889202947:0.4530248033751796
75:58:4.656733076780746:0.11008410284813352
87:66:5.736523536915983:0.714940200457393
0:99:5.146933211148694:0.14934608064069932
14:37:8.826800764399827:0.36862157844671417
44:89:8.617130159606878:0.629612083711045
76:62:3.808269227488712:0.720616750357066
85:88:6.012459883150846:0.16207079061869079
70:45:8.460933196001442:0.03484670155649383
91:61:9.052509705425388:0.8533345550696678
72:53:7.626567323195205:0.6547255311759907
14:87:6.123814961834867:0.10799512631697394
47:38:9.703366381545296:0.7861481672561152
50:2:6.921877657912186:0.0901922793109946
55:69:8.194239438728719:0.25190470736562665
98:56:2.4951109730602985:0.313928578913919
4:36:2.5502983171221585:0.13611513898232153
79:69:3.7285925099089035:0.17979490568657208
52:95:8.298231631001554:0.2418250870038332
54:74:0.683477780477264:0.12947423620560494
88:35:1.449078957263129:0.9971706032369194
42:6:0.35682089991305177:0.8220981329520314
6:81:5.059336042688641:0.06759370944002929
41:79:9.740240133624962:0.3483763795581215
19:82:6.050535299281256:0.2265648321824162
46:47:1.6596588569359982:0.1808366243887748
18:26:1.7194269810771157:0.469856221060189
40:25:4.586799772966139:0.011566746615992107
44:85:7.874474589247804:0.21940431108028746
21:86:2.022571555919419:0.0267482489112576
55:1:2.4888121465990034:0.6949916683725545
6:65:2.0048251474987087:0.6007769598222094
46:99:5.203358357806579:0.49351952534458354
16:89:7.784970962656438:0.3795554828381028
83:56:4.564802235334828:0.30456464371028225
19:79:1.6659490128697307:0.09406007012891293
77:23:8.95317196939301:0.861612362884061
94:93:5.5446862725779695:0.6345596358727227
17:81:0.8694353708834501:0.8610458639123718
42:77:1.0046753749712778:0.8361913703426427
80:82:7.898528087560851:0.7837288261770342
99:67:5.6795968227766185:0.37214616970155145
57:36:2.7938124820144363:0.10872997346449254
45:73:5.180491151160156:0.183795261793106
10:7:7.565100574481845:0.033325877812489635
79:38:3.158002500478579:0.8762332523999679
46:13:0.8163960575888862:0.11687793448566342
21:98:6.981821368222011:0.6207591025489955
73:8:2.3297846497396932:0.38686431622259887
78:63:0.4436434330479888:0.3894958140703567
34:59:7.191388054443503:0.20095769579977418
73:30:6.053525772644528:0.5967585140005333
41:40:1.3838754859326574:0.502405241786681
49:60:8.295110054329713:0.4305993322400177
83:63:0.48547541285623885:0.32805016959748434
93:31:3.6848854616150293:0.35643612817628656
12:78:0.2273456842385202:0.953077640082888
30:89:7.153927919430508:0.43586809846112506
56:81:0.09816756643055147:0.6295484364565437
83:7:7.4085537764271745:0.033305634133947604
92:42:9.988928688371795:0.2761232871677961
70:88:6.140537017964541:0.9795273172673395
2:90:6.461894038111831:0.1185444682840926
72:46:9.953506502098817:0.15329163492922593
23:90:1.9471840933452211:0.7386518914419935
10:35:6.373387939809772:0.7171440826931259
62:22:0.5596942604876298:0.1432896719544473
38:92:0.9481383578775293:0.7677431462939046
74:22:0.24856782036149716:0.42812724703916316
14:83:8.576386817177024:0.9396215064528413
99:2:9.30095330632649:0.5066561439508125
63:22:6.403955394913:0.1571691479302042
78:87:8.344142685105586:0.2861576111801998
71:55:1.4476785659120595:0.33708688327676783
36:87:0.21348728262420402:0.28711138205567277
4:65:5.67218610317065:0.36711320844191897
2:81:9.67653850599029:0.03156219872649857
61:88:5.724543360415383:0.7932661225893052
53:59:9.185083874031697:0.026215655980081132
7:40:2.8312580779681875:0.49196082108715233
76:96:2.7315283363582776:0.6519656627078148
86:93:8.443020347066351:0.1248034896150344
20:95:3.9697649231974808:0.04606324434996534
73:11:2.5528837643963884:0.6948268362453378
50:68:5.756463265683402:0.995685718348466
41:92:6.3336884749824645:0.1621356000106734
43:18:9.664694397742583:0.8045465791810523
2:39:3.9394795994181777:0.5867371111778215
1:82:4.841485437297484:0.49371708023469907
65:53:2.7498717037820644:0.20583454120465094
73:90:1.121137779062057:0.7937686270961586
53:87:6.2353544374513845:0.015946094567476976
44:63:7.393381816549878:0.3472456996800237
15:90:6.9611182251442685:0.8039647151078758
88:27:8.01133506522802:0.4130507216742557
54:87:0.23852744551902894:0.6264091805644847
21:16:1.081879835206111:0.7790590360105223
22:69:4.123929864290275:0.9702498443951281
9:97:3.0488474948409316:0.677209238072471
67:83:4.86032668987452:0.27054126955750546
62:20:7.499374514038265:0.003010254325323869
24:69:0.3027367484925514:0.42734474246664456
90:30:8.329167767245044:0.13749033066927274
84:33:5.994927378398588:0.3756720097493309
9:76:7.402293740792216:0.05790492058061891

wednesday_events.txt

20
Location Index:Agents:Time Interval
1:24,85,69,73,76,29:10
4:92,10,68,85,96,4,99,56,54,56,52,41,36:45
8:2,72,22:10
1:63,20,94,35,35,18,15,1,36,26,38,35,12:10
4:97,40,12,26,17,84,60,87,99,44,79,15,53:60
0:39,60,6,4:45
5:35,14,24,51,29,41,21,65:30
9:78,8,6,51,18,2,4,53,31,50:30
6:67,97,1,68,59,78,59:60
6:49,61,22,82:30
0:0,79,83,18,77,31,7:60
3:83,15,51,85,22,78,61,33,22,26,17,28,38,58,69,5,36,5,58:60
2:49,60,2:10
9:16,37,29,82,95,68,71,14,12,92,2,11,62,12,9,4,77:60
0:28,88:30
3:72,38,92,9,71,10,41,15,58,85,76,13,48,87,71,18,73,86:45
5:4,56,3:45
5:6,12,70,86,41,36,70,49,14,78,38,15,67,75,32,24,5,60:30
1:23,55:10
6:21,25,65,44,7,57,44,89,54,37,46,93,50,24:45

Example_2

This example is a remodelling of Example_1. It however uses a Scheduled model comprising of the states- Susceptible, Exposed, Asymptomatic, Symptomatic and Recovered (Scheduled SEYAR).

Only interactions between the agents have been defined for the weekdays and weekends from Monday through Sunday.

Time of contact may be a criterion for probabilistically determining the infection transmission between the students, visitors, teacher, etc.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import math

import episimmer.model as model


#User defined functions
#This function is user defined, based on the parameters the user has inputed in agents file and interaction/contact file
#This function represents the probability of getting infected during a single interaction/contact
def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):

	p_inf_symp,p_inf_asymp=p_infected_states_list[0],p_infected_states_list[1]
	#EXAMPLE 1
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_asymp
	else:
		return 0



class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Exposed':1},probability_of_infection_fn,[0.3,0.1]),False,0.99)
		self.insert_state('Exposed',5,2,self.scheduled({'Symptomatic':0.3,'Asymptomatic':0.7}),False,0.01)
		self.insert_state('Symptomatic',11,5,self.scheduled({'Recovered':1}),True,0)
		self.insert_state('Asymptomatic',6,3,self.scheduled({'Recovered':1}),True,0)
		self.insert_state('Recovered',100, 0,self.scheduled({'Recovered':1}),False,0)

agents.txt

100
Agent Index:Type:Residence:HLA Type
0:Visitor:Outside:B
1:Visitor:Teacher Dorm:C
2:Visitor:Outside:C
3:Staff:Teacher Dorm:A
4:Student:Dorm A:A
5:Visitor:Dorm A:B
6:Administration:Teacher Dorm:C
7:Staff:Teacher Dorm:C
8:Staff:Outside:A
9:Teacher:Dorm B:B
10:Visitor:Teacher Dorm:C
11:Administration:Outside:B
12:Visitor:Outside:C
13:Staff:Outside:B
14:Staff:Dorm A:B
15:Visitor:Teacher Dorm:B
16:Staff:Outside:A
17:Staff:Dorm A:B
18:Teacher:Dorm B:C
19:Staff:Dorm A:A
20:Visitor:Outside:B
21:Student:Teacher Dorm:B
22:Teacher:Teacher Dorm:C
23:Staff:Dorm A:C
24:Visitor:Dorm B:B
25:Visitor:Outside:C
26:Visitor:Outside:B
27:Staff:Outside:C
28:Teacher:Dorm A:C
29:Student:Dorm A:B
30:Staff:Dorm A:A
31:Student:Teacher Dorm:C
32:Administration:Teacher Dorm:A
33:Teacher:Dorm A:A
34:Administration:Dorm A:C
35:Teacher:Dorm B:B
36:Student:Dorm A:A
37:Administration:Dorm A:A
38:Teacher:Outside:B
39:Teacher:Dorm B:B
40:Administration:Teacher Dorm:A
41:Staff:Teacher Dorm:A
42:Teacher:Outside:B
43:Administration:Teacher Dorm:B
44:Teacher:Outside:C
45:Visitor:Outside:A
46:Visitor:Teacher Dorm:A
47:Administration:Teacher Dorm:A
48:Staff:Dorm A:A
49:Student:Outside:B
50:Staff:Teacher Dorm:B
51:Administration:Outside:C
52:Student:Dorm B:C
53:Administration:Dorm A:C
54:Administration:Outside:A
55:Administration:Outside:C
56:Administration:Dorm A:C
57:Visitor:Outside:B
58:Teacher:Outside:A
59:Staff:Outside:A
60:Administration:Teacher Dorm:A
61:Student:Teacher Dorm:A
62:Student:Dorm B:A
63:Teacher:Dorm A:C
64:Staff:Dorm A:A
65:Student:Teacher Dorm:C
66:Teacher:Outside:C
67:Staff:Dorm B:B
68:Administration:Teacher Dorm:B
69:Teacher:Teacher Dorm:C
70:Administration:Teacher Dorm:C
71:Visitor:Outside:B
72:Visitor:Teacher Dorm:C
73:Visitor:Teacher Dorm:C
74:Administration:Dorm A:C
75:Student:Outside:C
76:Visitor:Outside:C
77:Student:Teacher Dorm:A
78:Teacher:Dorm A:B
79:Visitor:Dorm B:C
80:Administration:Outside:C
81:Student:Dorm B:A
82:Visitor:Dorm B:B
83:Administration:Outside:A
84:Student:Teacher Dorm:A
85:Student:Dorm A:C
86:Staff:Dorm B:A
87:Administration:Teacher Dorm:B
88:Administration:Dorm A:A
89:Teacher:Dorm A:C
90:Administration:Dorm A:B
91:Student:Outside:C
92:Student:Dorm A:B
93:Teacher:Teacher Dorm:C
94:Student:Outside:C
95:Administration:Teacher Dorm:C
96:Staff:Outside:B
97:Staff:Dorm A:C
98:Teacher:Outside:A
99:Administration:Dorm A:B

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type:Residence:HLA Type>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index:Time Interval:Intensity>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <>
Location list filename <>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

friday_contacts.txt

135
Agent Index:Interacting Agent Index:Time Interval:Intensity
10:88:6.688619668606895:0.9667447062422788
9:95:0.9007299229441801:0.29413754832944883
31:65:2.3104869981567724:0.13629109371609527
76:96:3.014786047555358:0.06339801582240967
51:45:3.131052149585477:0.4445434382525858
89:8:1.9427263725803523:0.8808337927621033
10:58:2.495396084901178:0.3683191418222923
21:6:9.164811448710253:0.9149064054528824
20:94:9.073726154746812:0.09172186189747888
11:35:5.811227020170672:0.04277875290311006
81:67:7.382044703884041:0.7562618254548581
35:88:5.427284912563087:0.4919887871599472
64:98:3.2931497408328347:0.20795695942795245
21:60:5.309981205719868:0.011521792726691515
96:55:9.656859364978201:0.3703774321272413
56:15:9.416354213552863:0.6794383500051776
26:84:1.984408867513613:0.29733391878026927
20:92:4.853416282497795:0.7638596564144793
74:36:4.155107158450942:0.3304639571662785
98:80:5.883730603507602:0.7125416288385673
48:58:4.237992321318459:0.2184722842877974
63:11:9.4185033672319:0.6937998477526771
93:84:0.07677001757306234:0.48147174789615876
4:14:3.9106702580659745:0.8198449581478692
20:25:9.759725526106168:0.7121362469200376
8:40:9.859266700927066:0.9002242531813217
7:48:5.9862020774776905:0.2562517464656725
23:54:1.687746631438165:0.5900449712604049
50:10:7.664351665510184:0.34811947944265953
28:46:5.972242241867672:0.8446013225231795
48:84:0.4610657004284924:0.6920856877392875
22:80:5.797512189699676:0.5749543208902808
15:69:0.6755755530815644:0.16837016924547443
89:21:3.7647209748264734:0.022369240867610274
95:99:7.419111728757224:0.6755793454767197
46:1:6.0869705981206765:0.9392659049557766
96:62:0.38005028756432413:0.9685916051859171
75:35:1.5759150731503702:0.7930309701559252
83:18:6.419755471582778:0.8909924833495784
63:5:4.374873831058975:0.14767216650642556
77:36:1.1436741940145367:0.027032871240488587
31:27:0.03543073074623537:0.26791737345013567
93:58:8.600159028547823:0.5089371541057991
37:83:9.525552257972498:0.7630639782440107
12:71:0.1838245228022528:0.16301999982715865
4:28:7.201106455933197:0.639241011778947
39:55:2.1450605439606854:0.0072084624135791975
76:28:0.8317688391569544:0.11354085521055068
92:75:8.891200749890123:0.40037278495757134
63:67:0.6057359831130937:0.49096137048602173
1:78:2.175748319238159:0.1973094235179783
44:90:6.603685950367471:0.07742541911225254
43:48:5.313085158201147:0.0031820058422277198
40:66:5.140130492045794:0.3522022574851984
73:47:7.622359504852094:0.3803414058582355
44:6:9.90535715069833:0.7067631788482184
82:71:8.438586749469684:0.05817509423223455
27:89:6.191647064682342:0.4954437234413782
40:16:2.7203192040973834:0.20918796475319368
72:94:1.0722333108247561:0.29041196859888474
71:3:4.911064356203861:0.40762232519762864
96:13:6.0216102844761465:0.18221116663426262
87:44:8.942071102975323:0.6762865235784432
81:87:5.828205005560443:0.43654291147178037
67:0:9.558161218422207:0.9782982086424898
26:15:2.747686471657702:0.5841505638191209
60:64:3.1877027193758387:0.7852519193645222
28:21:8.89719398196214:0.7796779703774409
37:27:6.148709351415107:0.11498229333292553
11:64:2.588199198086797:0.9232497898570643
94:60:4.584180815888526:0.6663030600822625
96:14:0.50361178946847:0.6276123660867005
60:21:6.304111960148045:0.40129177112848724
72:1:7.946241811259695:0.7384589147638106
16:72:8.280083142609529:0.4050472195277236
9:11:9.83701917013762:0.5098460996889553
72:29:2.7846659029729883:0.040863074669492905
0:87:2.5247982332670693:0.1763267929861304
9:42:4.288106193000074:0.2738502204725093
83:42:0.7506400165327698:0.7642430599819986
12:76:9.9768333694983:0.2717578760733744
20:49:5.381409836273989:0.6346301712804985
16:16:3.6726867565695507:0.5582714052891703
3:85:1.3686976214395152:0.29712675725144
74:15:3.4809262245707284:0.08676173861125958
99:0:6.3566236770104645:0.7076734815700955
92:2:1.2152547236516253:0.06654621024479845
62:39:1.2779870965308293:0.45103199912883984
0:67:0.001817668070662748:0.29057807898622035
68:31:7.533477004492588:0.9192380824068297
12:95:3.6035444861225017:0.6605518577198357
80:59:7.992128919597919:0.4588233013708509
92:9:5.942982815932124:0.8525527661682476
54:49:2.5528781753994645:0.1674153560281869
67:56:2.4068751851152905:0.06936911168175486
99:22:3.5335711374409917:0.39379603080655945
91:95:3.9199908183515664:0.25378177050876816
73:18:3.503121031888783:0.7584130737841819
68:46:1.4189787199600157:0.019877623645405795
44:23:4.212711155988669:0.30143615210535746
47:39:0.5087911598281225:0.5893668330016113
48:92:2.3542849120881657:0.9451616102571446
8:54:6.903186150983789:0.2695255667504325
14:84:9.462171277620177:0.060500538799988846
91:25:3.697664572724438:0.4079539487067759
88:55:7.346264967721757:0.552949376315497
52:88:9.665066927538911:0.7401975800017172
18:7:4.4697307603688285:0.3782737394491341
90:32:2.916400428655871:0.536697768154158
2:74:8.317196407757693:0.2955317179537563
65:73:8.914697419270194:0.8930540458434777
20:21:4.219761483887274:0.2632063077194766
75:97:6.310357238443305:0.14546059647798926
26:69:5.469932282412852:0.1777212237813397
22:31:8.687063973633554:0.02966055949862567
87:78:3.4690077115478637:0.5578236892580205
62:47:7.198318314161049:0.8276001292402286
74:98:2.3265643497552024:0.22498444445538013
51:72:4.797553032172871:0.13299317181491055
36:76:5.369674743302922:0.6308887356663198
81:59:5.147324117443669:0.8496019187336087
55:82:5.933758031306022:0.4066184094819313
54:6:2.448195518938933:0.0869846065164368
31:97:6.658117713557282:0.4701995171802008
44:26:5.384133256347262:0.2881262392733547
66:93:4.77688056130655:0.02994329042677224
91:19:2.565625338887955:0.888807228949255
40:4:4.003688953910921:0.9128964821711357
94:91:7.023901631427076:0.6382076850534217
17:52:9.229885545120888:0.3200856999256
82:5:6.528401052084762:0.747569444766493
20:29:3.094567113936595:0.6996607131893888
85:19:3.4859677673907297:0.4504028250467983
12:62:2.279581910720033:0.31311123094403015
96:66:7.081217549271823:0.7148731929250536

generate_agents.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type:Residence:HLA Type'
	info_dict['Type']=['Student','Teacher','Administration','Staff','Visitor']
	info_dict['Residence']=['Dorm A','Dorm B','Outside','Teacher Dorm']
	info_dict['HLA Type']=['A','B','C']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')


write_to_file('agents.txt',100)

generate_contacts.py

import random

import numpy as np


def write_to_file(filename,n,no_contacts):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index:Time Interval:Intensity'

	f=open(filename,'w')
	f.write(str(no_contacts)+'\n')
	f.write(header+'\n')

	for i in range(no_contacts):
		agent=random.randint(0,n-1)
		contact_agent=random.randint(0,n-1)
		time=random.random()*10
		intensity=random.random()
		f.write(str(agent)+':'+str(contact_agent)+':'+str(time)+':'+str(intensity)+'\n')


write_to_file('monday_contacts.txt',100,300)
write_to_file('tuesday_contacts.txt',100,100)
write_to_file('wednesday_contacts.txt',100,500)
write_to_file('thursday_contacts.txt',100,200)
write_to_file('friday_contacts.txt',100,135)
write_to_file('saturday_contacts.txt',100,50)
write_to_file('sunday_contacts.txt',100,70)

interaction_files_list.txt

<monday_contacts.txt>
<tuesday_contacts.txt>
<wednesday_contacts.txt>
<thursday_contacts.txt>
<friday_contacts.txt>
<saturday_contacts.txt>
<sunday_contacts.txt>

monday_contacts.txt

300
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:91:3.901351436822462:0.7339762595213493
78:90:3.5626711544249745:0.6746469145132054
5:85:3.045488921169931:0.039540230198430115
86:81:3.35308756346429:0.3633954339845933
11:89:6.326551736128173:0.6289266025075126
41:89:9.329187350891646:0.6353349553288311
20:71:0.38645434909083676:0.04041798989591305
92:68:5.889425651825421:0.3950396909932594
2:47:6.557852441820289:0.7245690803250271
99:32:8.402723639687704:0.0029308245084296303
3:33:3.5133963523940634:0.5984631970990983
59:72:5.370053020778495:0.531313701010571
94:59:5.361572617932277:0.9886697867090888
21:82:9.786681409791774:0.644945666368522
48:42:5.011982887515333:0.051662026866201693
62:94:3.2808909541391196:0.9379521953670432
70:28:3.856798574421678:0.2557608578207182
6:59:5.678890997712748:0.3530777753051588
37:67:9.131898601767697:0.858499260774226
52:74:9.986369997027031:0.3156120836947629
31:94:1.507816879463777:0.750555105240906
99:93:2.121959362802376:0.09703892296171557
37:94:5.400780286788532:0.4299044759629851
15:81:4.75977897522104:0.3232455011774399
51:63:8.55510185721477:0.31288557896015257
4:59:5.9222428832641425:0.12157485621270103
43:73:6.795602865846028:0.06744185332954145
59:1:2.7021747467696375:0.050538725122540984
60:93:2.943901539081496:0.4982339562937653
3:84:4.133634564366487:0.3075102931608772
73:44:8.134038990032534:0.8418295238039117
17:86:0.542806560857374:0.7588119151895625
85:77:0.2859974668290466:0.7241059456194887
14:16:5.329831905451478:0.9195203828276385
1:90:2.211130035725443:0.23639098557775062
8:70:1.5406578056068143:0.4262130830791522
41:0:1.1579089653984453:0.7851213807119591
50:37:4.484956048115092:0.3060348403762886
89:88:8.043811518556971:0.4727074037001201
58:95:0.8868990689387879:0.3467640517252304
3:86:6.190128446233331:0.8176423375406843
19:22:6.298136491836642:0.5649230836250814
64:54:4.536312531879237:0.7989489195824466
1:61:7.833473806936388:0.650092667048976
43:76:3.44149914401369:0.8746721680294639
26:4:6.373337730771791:0.17592940756741804
60:35:9.934829550819842:0.5345586622312622
16:51:1.5764360886713513:0.6770030520140922
91:75:9.528950938813134:0.6204530683653462
39:42:7.840684207317652:0.6089954987487575
61:65:3.319567445374041:0.5478018921036643
52:46:4.646705033209998:0.537254045209896
95:50:1.7647409730344588:0.02688151759300872
51:35:4.156824965180649:0.7123780511350246
75:78:3.9226537983258227:0.596842377897293
74:58:7.563226282263413:0.8062241896776831
7:84:0.7120251049265447:0.9302238734157992
47:95:8.024189071209314:0.2387216724542346
57:28:6.2859322129180475:0.1556546337774144
70:30:2.530642559415648:0.25633238312842377
88:16:9.06713427487879:0.41516872075378486
32:35:8.09237307848392:0.5857892642742539
17:65:7.8134917233836285:0.28104998118729463
58:79:6.223781312011015:0.012229676686173363
90:90:4.692384410823149:0.46217556562178697
71:62:9.08294852508194:0.7287708959264037
33:20:1.542102652267403:0.8389929454049759
90:74:4.633437833538752:0.65537480210047
97:11:8.325837108492266:0.08894675467827051
10:72:6.557672074227439:0.8651050525879831
2:73:4.872758588552571:0.09357856657833297
57:65:6.828660337659751:0.48495229529925665
95:47:0.7466953354057215:0.6603079823678228
92:49:2.0077342778968554:0.9189494309774255
82:53:0.24061611784117698:0.9198471939191474
64:6:0.5767175723374529:0.5800677289768271
56:50:2.8103728644720705:0.7661027286936849
88:65:5.495737535254017:0.13475930874418607
39:26:4.520441829762211:0.7752928037603906
62:15:2.527874839657712:0.1705345466390138
75:37:6.949647409442933:0.5105509864259511
4:20:2.97399456614634:0.11289483289257596
41:6:4.273511532203043:0.09718585800915025
4:46:7.362236247628521:0.04058096586641702
43:36:8.117474485572878:0.14526881403519276
56:47:3.855901848144012:0.06033216610055803
82:62:7.36834872994402:0.7504447501739113
34:37:6.940059348353467:0.9688323772735252
80:84:1.746381666139043:0.7823637521600265
11:48:7.364223469449296:0.36985754872005294
24:63:8.322818469697424:0.26920941824660416
95:93:8.565298534711246:0.36630815362030034
55:98:8.261077047953284:0.24720488756898507
67:32:8.928969126003851:0.2768960055237545
61:35:8.836832665518946:0.8525592430588332
4:79:9.86527008653734:0.5241984425341051
15:84:7.895868992916919:0.9243365521462359
4:44:8.716228021549393:0.9307245519590631
96:55:6.888764694183994:0.0792443519258833
72:90:7.406214716467515:0.8859191527012792
16:14:4.69851133682884:0.19881286599667536
61:63:3.380324616991465:0.8268422238613041
52:16:0.19216831328206507:0.8647705352734371
45:84:1.2697886559236449:0.6513461320262995
50:83:6.159864427212866:0.23346664207112955
69:30:3.4770888522834897:0.36611239444780763
70:90:3.5286680285590846:0.061422783069107645
93:39:0.9637079127853909:0.807360297039483
59:97:7.043532289504258:0.12207223530271005
43:84:2.227673296719175:0.8300830010185617
47:42:1.833397217400502:0.21499046911985964
76:1:7.8677237896729535:0.1308918842693122
36:55:1.3055580188425742:0.7277279779721338
62:0:0.6371814138898102:0.739520928356319
9:66:3.6931844772562252:0.49433217498267423
5:88:1.486445418424901:0.7994315842241301
67:44:4.389739626245813:0.7501063883906391
87:66:1.5283456139385265:0.933656123136008
23:48:6.137030114523152:0.9235785663451955
55:39:5.593904911357448:0.7918698321362837
6:98:8.573685590443334:0.6643971331641266
58:40:9.370090787366754:0.16062179115642827
9:7:5.99552450577316:0.22398865033159399
78:78:1.6985126182973498:0.23431129397478678
27:36:1.7553777448861463:0.9571656933455291
10:2:3.340403747124703:0.266154426065234
20:32:9.04965093388158:0.7998074320401931
62:43:4.48824832171709:0.19692009534109944
66:68:1.044013500391131:0.17518002535790944
81:91:2.032022113548353:0.590068376259575
79:5:3.6678672963264725:0.8583172648269328
88:25:2.7906016247631857:0.3417441357034283
48:22:8.274301104789616:0.6180500643903922
37:34:4.895008043767715:0.470617322501378
65:98:3.9939963178189073:0.7597707393883737
8:99:4.344354090436447:0.08000834895567921
33:14:2.721637241356134:0.5469317967238905
97:61:4.480139087307686:0.9370228932878472
20:89:5.052620560783816:0.2537641232093184
77:4:7.5548012755079155:0.7647336265540196
34:53:2.3637671040815222:0.8631258102243161
97:97:1.1517561038645152:0.6080198126602016
47:4:4.764917096374596:0.5152616692151232
56:7:0.7673797837521523:0.05295581093999868
12:65:5.820115979848362:0.6738384932399847
22:33:8.160077472113436:0.35575801582167244
0:24:2.5976244236881127:0.07228477664392474
97:67:1.3349001353042622:0.5467070433629635
4:12:5.412935866383392:0.11638905261774857
95:64:4.328183987891099:0.5038839316589997
91:91:1.8006275689774043:0.7069818589813456
33:64:9.979922860196544:0.1730588104192059
82:14:5.625659929969839:0.427124084779392
80:66:0.12556782560516622:0.6318204731180151
84:81:8.965005435252678:0.1329683648749237
7:7:2.7621652564272834:0.22081572672352956
60:97:1.6189519082706272:0.5054371256483898
61:47:1.001552280836816:0.44870613684494454
12:28:1.7394787076567975:0.15123469338547757
92:66:1.9575307012529986:0.5143410029855222
35:60:5.974833467758106:0.18060226652566203
19:47:2.4143782472302853:0.7229634935521948
32:33:4.084222023013261:0.8052825167982911
61:42:3.880567198086904:0.33785485991760156
9:7:4.797609189111243:0.6557842812818759
60:17:6.786175767301016:0.855473866429063
87:80:6.840555606112167:0.10932325733796955
14:84:5.475907747014465:0.23291755605347986
91:29:9.17560043418257:0.17718783157595752
28:78:3.4425085921579193:0.8242337800670188
60:34:7.2394297827991005:0.8755428155410877
54:64:2.2671905891345414:0.7334964590961794
67:84:5.948336450041793:0.3098471787427216
11:56:6.700839762851763:0.3207358083318562
38:77:0.9674523146234348:0.6023850656632548
9:44:4.69793570668195:0.5029890933381677
47:20:5.319830568980307:0.38707577261884796
26:86:4.7817534968627555:0.2976309298220806
41:47:0.20291653431164902:0.4554712242013542
17:64:7.3452775056010955:0.6483550983905965
8:2:2.016021005062094:0.47350242384381125
27:52:5.803404417975604:0.9532995101175942
29:20:9.704176176125863:0.08789736542495086
50:87:3.1885515636171515:0.9367769520102764
93:29:2.0395613845066887:0.4946398943812982
76:97:9.019353389326039:0.1445289893279038
99:46:6.582281848173897:0.5425316978102004
86:6:1.6825724869426617:0.22432772872032958
90:29:7.500546164660645:0.30905885655729015
16:78:2.424664767871816:0.18813093908551015
87:61:3.2116382773342567:0.8869057054380951
27:0:6.159493456473577:0.2276835244003813
90:78:7.329164728333016:0.26167298307249465
76:42:6.930905362346148:0.9096883575644671
46:6:8.782850221647784:0.3169474488876317
62:40:1.370250262759245:0.5553566618394318
81:15:5.902292977530621:0.0556777547804056
32:33:2.3285018362586083:0.22805846655926876

saturday_contacts.txt

50
Agent Index:Interacting Agent Index:Time Interval:Intensity
78:67:6.687995944426314:0.4033605962521598
60:25:9.766242543855506:0.06792652175390823
18:90:3.76439853374662:0.9833305943472379
4:19:6.506721718246754:0.47110824328789613
73:26:2.5400982744608336:0.40140270897106645
68:57:0.7095173223023121:0.2328466079907241
57:54:1.545474789816078:0.6593172582955799
90:25:7.843174009450287:0.8268544568417224
28:52:1.3914812455190517:0.8024812081343093
9:71:1.5518951054968155:0.9318507773242553
39:28:8.476359135606552:0.7763246795933149
49:50:4.7211099258761555:0.2950133516599971
52:12:1.6886708754449598:0.27197127175998526
19:20:5.6781326401719046:0.9590963962294797
22:77:1.530950907625851:0.3754295473613327
89:88:5.658452775120387:0.0803874959012173
4:12:7.636759575481204:0.2831880696193816
58:57:8.601430915297016:0.5434659477112249
4:32:6.147275128372893:0.856773362268219
75:95:3.3575411423184542:0.46747804925146763
48:73:4.294729247064641:0.050670872480034546
87:31:0.20788603769580694:0.06469788200264015
50:44:2.983096090063986:0.02735893758806829
12:73:3.5332686474645323:0.6308187130898412
59:18:6.507883109382785:0.8679178454556692
90:76:0.6431511136089729:0.38181470878945245
93:55:0.688031974008203:0.02165710753326533
93:54:0.5174288624109147:0.4172063304023521
90:55:2.351244324988293:0.20155062301576254
6:69:3.204281646873234:0.6853704116954752
37:45:4.207020809561934:0.5062926163922263
50:85:3.3126767901315013:0.11439025603353281
18:98:0.5483540344431337:0.5831224256744627
3:20:8.466441890639738:0.15531801936817402
70:84:4.252674278698835:0.1021412687119303
84:96:9.65755023003075:0.9104362499713785
85:30:4.477617210548132:0.2798872009598208
27:81:3.4726517979202596:0.6867846228811487
33:5:9.418615513088076:0.3089650238414625
82:19:5.969411859418942:0.9201045742327656
16:63:4.806229721717362:0.9196419859150736
29:41:7.958618332948535:0.44643319464470743
32:85:7.863072004177757:0.8397192112109673
64:63:2.8034310396456483:0.5005219190796429
22:53:8.636229353574626:0.5573464209207643
20:81:1.3492272749291145:0.5759296391255372
72:1:3.170961659254469:0.864395776604293
51:5:6.983939525485372:0.3998399286929163
6:11:2.828084537368467:0.7569874076404247
84:6:3.225352755378524:0.13692972894786104

sunday_contacts.txt

70
Agent Index:Interacting Agent Index:Time Interval:Intensity
29:74:9.16372608230145:0.796954964751048
6:51:7.883900864399966:0.4086674799106108
80:96:0.14815771583060466:0.9598774863311844
74:97:4.308374781710823:0.5469594486352
51:13:9.042079023826053:0.4951854351944386
80:59:1.194192610139505:0.1806780778265884
29:52:8.298008123594673:0.6055655082754013
11:99:7.398529511198307:0.8423463023583696
94:96:0.8114992155173495:0.6185711886621139
32:66:2.3703816553952883:0.35420962128864475
39:99:3.855243189079789:0.19683280636170353
14:20:6.993801240807475:0.9924238391700014
69:5:5.368207898395178:0.3296239694354882
22:82:5.141505384517688:0.6680939851970867
16:14:4.381681285284316:0.13324126477503573
63:73:1.1377617766820813:0.016765931895245334
53:42:4.331827823657975:0.560247114997433
74:72:8.487044883624892:0.44857507061224955
48:53:8.142210501452041:0.22185756589964178
39:80:0.919320244588725:0.0066855429075109996
3:21:9.289809772081991:0.48205140669390023
68:46:4.536474019847931:0.6855403269937351
58:90:0.8335888281998161:0.5829499953459416
5:56:8.623750119046413:0.6850571684408665
37:46:2.19563994708889:0.32831432045342446
43:16:6.54823708272073:0.7241818732660742
55:95:1.356326584676878:0.8593548177807079
33:51:5.72041717661566:0.524825828129498
56:97:0.05304038966948088:0.9396784738557239
39:25:1.6057401908444169:0.7729036122188875
75:49:5.1930718546095:0.6210496845686397
50:72:6.400159461397456:0.4083531569308648
16:36:3.095902490181952:0.7697749975800223
98:36:8.736666708714022:0.9269722338361613
25:4:0.1996745353487972:0.3912838723392581
54:37:4.379634943637777:0.14566373998141358
92:55:0.019798603565377437:0.7045448754207068
36:91:1.6904254816989417:0.9488514792773272
75:0:9.344337089059099:0.31555189896437286
98:93:5.920573213081997:0.0028759470703639822
73:27:6.323996215279141:0.9268743088920766
10:26:0.3335853333487415:0.22327795239905257
83:6:1.8792327407608012:0.5844054293798739
80:39:8.666561745531057:0.6341570680368916
85:64:8.279808342659074:0.10714932227380469
51:80:2.597488396875214:0.0753952752053868
36:39:5.6836065070360675:0.6191528017058452
72:11:4.7881710302403455:0.7655770299526184
46:90:1.0209935816785076:0.3288165509174008
44:3:3.9645570905388516:0.9433763441413141
20:19:4.608574167399386:0.879494666634502
5:30:3.2439699423921597:0.1099899352310858
82:95:2.2446524075665786:0.3730883570466935
36:47:7.523082303216631:0.46842401692247715
67:80:0.46591410875939654:0.43019339282451874
86:1:4.153321745378494:0.8744248883962594
32:87:3.740524247725736:0.3904419258374895
4:88:7.107645004605604:0.971958195970546
80:46:6.087211809269826:0.5332140334707597
20:16:9.743912977300749:0.06325827742806112
11:68:3.451500943176198:0.1235586691736077
7:86:1.3489846728692534:0.9380271839281606
98:76:2.3319468321605408:0.23635317254322508
59:48:0.5527503420919422:0.9217009076588242
45:94:8.674582793785653:0.6210440386455588
24:38:1.587140836439298:0.7617430249347842
66:36:1.1370077466599082:0.7580769247367519
92:66:5.149056575604316:0.20909269304442213
96:32:7.470321654585279:0.14446721285379327
57:32:5.236089542116535:0.012270130984272543

thursday_contacts.txt

200
Agent Index:Interacting Agent Index:Time Interval:Intensity
9:80:7.0382921246333705:0.9556820598269046
44:69:8.573011972540401:0.07924998376906311
10:57:1.5325958641775272:0.3093902519850843
42:10:8.701123888970558:0.962585163449696
92:17:6.157994099854182:0.7080803028223787
30:59:9.725783500251366:0.7314558080633391
75:50:3.8058557518124214:0.3168807779694327
40:62:6.43557676676667:0.9763099081805485
92:27:0.04439101149650737:0.417023535886784
9:96:4.064443587754805:0.3627068026184982
57:97:2.9937333519214326:0.04831281631023343
15:10:5.893399813507008:0.47319793911862373
4:58:2.189181419606845:0.6803283844589387
71:62:1.7525285905496146:0.13896567010484562
79:36:8.506352552863355:0.8691976202423097
76:53:6.137904467285217:0.07106840002404269
84:36:2.560188707141876:0.7771950084819546
52:43:7.668274637265973:0.7312993690534784
54:93:7.939847618469336:0.7776433290453468
81:68:5.384535911579822:0.23525255976832482
93:16:9.920757993127333:0.8934543441881765
1:72:7.758134739731689:0.24703891010135515
94:31:4.736159938253104:0.279716657877369
11:91:6.604975713945529:0.2980626079316899
22:89:7.307471438634465:0.8331314885020186
66:92:5.074020217973926:0.5867164552992925
46:20:7.872054213346798:0.07697026703119414
68:84:0.014172442899552662:0.3614765333867669
82:9:1.0094025461973999:0.7513051533741296
88:35:8.916625728448652:0.13212679832443874
3:61:8.879634677276655:0.16048328962110692
30:63:0.889562352523886:0.14175368432296465
60:41:4.057982037703882:0.9460153048461237
27:8:3.4522820993929315:0.263099965041526
76:33:5.350168457592791:0.4817138552741015
1:85:6.03291472992156:0.7362304926092693
47:46:5.151177915023272:0.524784652285693
43:80:6.3260521909884115:0.20238179959236602
46:6:1.4188793789888487:0.9554001282463949
68:2:8.760839211072776:0.5522656652793739
71:87:9.866051901497453:0.050298054713874385
90:11:5.444448491136552:0.9347109216145608
17:22:3.1981154659531565:0.21362937499069423
55:24:0.31133364718463397:0.9904127541387862
39:36:6.652772458814239:0.32511126196824647
51:17:0.1908445268782133:0.7776073952742899
38:27:4.9287219964583775:0.6766708526045345
43:68:2.345297470120279:0.4888662207402883
52:77:6.2704802068309515:0.9727888002522699
57:65:0.31410385209842073:0.5678620629870902
58:9:4.5043887132533875:0.9529991526026419
32:91:8.856779214806078:0.6205084549405729
36:9:2.99684211320568:0.20084350883099966
52:42:2.234391573182888:0.7936651279874264
66:97:5.497317602861229:0.8037936396717074
43:75:8.631273282189033:0.6467101601379966
91:93:5.899984231792686:0.5589976222351022
89:98:3.4357223028901385:0.28650955658054356
32:69:7.035480596894519:0.715682876100546
36:47:0.702440123848932:0.37014161233455
97:35:7.795073444954294:0.690234242431429
49:26:8.188641044378683:0.2854457891281498
23:48:5.8860192391972745:0.9858892118609247
94:66:4.8331558942120045:0.9392247213322765
1:28:3.940617896767412:0.8119157936104054
94:9:9.511243180423108:0.4866582339311317
16:13:1.3706419546146398:0.4606816198641699
4:94:6.079250764705122:0.664159392552753
8:95:9.367600455819955:0.28978090388795374
87:94:0.009281959133973228:0.9533789826774827
58:89:1.9742256941383884:0.12038943184213302
84:58:8.2927498262796:0.6289705093238441
96:78:6.145813134167143:0.8797391678218863
15:29:7.304166478831061:0.37660257369721994
33:10:8.415998407750381:0.3884970977718254
44:83:1.5842806601754167:0.8979250123521796
0:89:2.301629122139719:0.07010399854914007
56:69:2.2033530452009864:0.0386290184572774
39:21:8.365740266584353:0.8769992573517463
35:8:0.6039725358473025:0.7231100758772858
16:29:1.1869583871524414:0.7934164894804442
13:60:1.1083952560802401:0.8160926988887836
0:76:7.296924179379242:0.04145973049986551
96:10:7.083416807339589:0.31052447378674664
67:95:7.3778033130326035:0.4699849766808416
14:60:6.545948462503307:0.28553558881248886
43:46:9.73653464036593:0.08518557315885533
39:55:8.001834086567442:0.1449534039528918
96:29:3.3347114644129827:0.4751405480035604
1:3:3.9843061077162076:0.2904432377787004
22:86:0.7737914282516112:0.9949485827235354
39:54:9.44229177563296:0.7555177614543236
13:8:2.9093184369975322:0.4074161100925633
90:56:6.3954756331314115:0.3630477505326515
95:59:7.741332185095796:0.0686127194562044
63:6:7.329546037754219:0.047602827898245326
57:55:2.2058024651955788:0.5420130616536876
45:64:7.972444005640122:0.9346612569555631
17:96:0.5028192989338531:0.602861775981457
19:47:1.1593911790613642:0.3294905796595542
5:73:5.053371460984918:0.43453735386468306
47:14:1.671267644260136:0.7617381750767552
5:17:3.8521114646242918:0.9027839801336598
30:95:8.155368057480747:0.3039655272261964
42:0:5.301894475432239:0.12825534758882506
59:93:0.867332721346481:0.8543727044000994
61:24:4.545866060225478:0.29454914795747555
85:51:7.265989073625477:0.9771268329383078
19:63:4.114022700622767:0.40193113111336387
45:47:1.0866634685103327:0.6439994336060687
36:54:3.3080183883197356:0.05478430344551444
27:43:4.491839482154164:0.34138799298187006
96:83:3.2537132537203695:0.4690103280088781
3:17:8.210480957200954:0.6785354918047558
60:0:8.166288756067337:0.009825187110891531
80:57:3.6405957600181194:0.9731578767400095
4:12:5.167714502627886:0.08442010394327959
36:60:1.5436074012458656:0.38231602955762645
13:81:7.487919848355968:0.7720185857814735
86:81:9.832572756034889:0.932466012208083
85:43:2.3720109674290333:0.8201010230106307
32:21:9.953311147163454:0.011674606541613675
41:94:0.5708653782110851:0.8171371735366577
80:62:7.826269089129624:0.8054818446439931
59:42:3.4752456682963286:0.16962944629105137
35:30:8.950010240117711:0.7321501698872532
36:68:8.049175109984219:0.8751769192948862
90:78:8.313662977740753:0.021380973721606078
4:60:0.3863981340857503:0.64324289181507
92:5:1.5821836994859562:0.6832838495910474
92:35:5.759253574243678:0.6722529014859097
63:52:0.3248004646469238:0.889428065406194
76:58:4.657899334607473:0.1269797853566087
20:23:2.030506718629682:0.623571833876037
67:56:5.721392098935411:0.9740022252292175
39:71:2.3800650310864215:0.39527668002658867
57:68:8.743679973789662:0.11559725358582273
9:57:4.426655660307315:0.3831574189336966
90:35:4.500662171278994:0.6457649176495883
11:15:3.610230581472095:0.7749786563954427
78:30:3.537127278785983:0.672334116297062
94:51:8.461216862609769:0.5454202707832448
75:26:7.878760507607612:0.23953114774140905
62:21:8.03728326258731:0.7317362756887453
41:28:9.881900293689839:0.5158710625716576
55:59:5.187576120008606:0.09037421440274218
66:30:0.2822708894510517:0.8239935690953065
14:86:2.4350591158958035:0.0451732234158444
10:13:7.540167443077943:0.9128796041719606
11:11:2.5872234084376977:0.2745810085653637
63:18:5.8349459811306135:0.2628000302629594
8:70:7.389502622983511:0.33781675668735134
94:92:9.716335252977252:0.7560749193156806
46:6:6.697843308826012:0.3071889852788805
14:56:3.0115810056757883:0.019309987557965647
43:59:7.70335287625792:0.07801146422444183
13:19:7.1531177132632635:0.46172693965817924
32:58:0.0947302768753655:0.4435550454724231
65:41:7.846459075221219:0.20486103557339408
79:30:4.855795600531242:0.6324741482930949
94:72:9.192984245755726:0.47308851194268364
1:19:2.5189273919855326:0.5697138720443463
43:89:5.623253504764232:0.3205680634577276
23:49:5.996101075819311:0.3092468469691083
24:77:1.1575274845932815:0.29449933851511156
97:14:1.542998906358647:0.5324160705560573
24:34:2.8547843066335075:0.8823443962826881
88:94:3.3126621067162567:0.36194029301617625
59:89:8.775881914064165:0.8253261132282622
6:80:3.9288893586194327:0.10275972449442927
71:59:1.6479602486244127:0.04174936740649626
55:54:1.5015663207731555:0.06830746730647275
52:2:0.9008968763444658:0.4046997291491362
53:73:1.8688136024961355:0.5830614775602793
36:8:3.9728441806543637:0.135649600130743
64:44:3.9868053958346583:0.12018302290146932
20:81:2.3295936709442913:0.16782594204362378
70:98:2.4625686349731533:0.7447068575679722
83:68:3.0500910480749255:0.18871352909901662
12:36:1.3232525955055607:0.6301253264588437
24:4:8.511899833302598:0.03323414664925628
50:77:7.491775365021729:0.2614312397444818
77:8:8.606105243413465:0.6596871488477454
41:11:3.3624084193448764:0.5842044609543943
98:13:5.435720234282897:0.07937243127913918
21:65:0.5322387729059574:0.38013044360335524
11:81:1.473579773422019:0.4182034100396437
71:6:4.054832359579801:0.994191633021709
91:44:3.9765231844593063:0.6317211275561101
93:14:5.805280716375303:0.662059365871648
86:69:3.1533754603139252:0.6050328383845425
36:22:4.280692953835742:0.7771808522245846
76:78:7.500105794724697:0.5285135775933644
78:73:9.218428037446689:0.6502891286543955
77:24:9.141627497458963:0.6764185110368829
99:20:2.9402718121860625:0.94933964891294
51:60:5.440972736761095:0.08167094302929112
53:46:1.4756695868875869:0.5147498179121255

tuesday_contacts.txt

100
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:45:6.213436231456636:0.3731609728338897
89:81:4.579771459232793:0.11434815810612176
0:16:3.242934650368129:0.3141918044148243
15:82:7.975466857130672:0.6128656081719708
12:29:1.371000381482833:0.3302304647764601
14:7:7.821227878776517:0.02845995074557328
85:83:6.395632457075665:0.7751395265422735
76:14:3.5755744413957458:0.5574299399414683
5:62:9.58634391552728:0.7052359820907551
33:45:0.7076539116973712:0.29372417115115146
46:38:6.129146387692819:0.2609382873034033
25:66:7.924473535575229:0.5101208649146122
25:30:2.2435787778590233:0.34060744129158593
34:60:4.927953728053525:0.6700587895933668
47:5:2.684771776287241:0.2574115092392507
89:58:9.625734441309854:0.5968114997896796
3:61:5.220803442701763:0.8620230034643891
29:91:2.4686173240322318:0.6230867759670403
2:95:1.5754431658870482:0.6105753814387738
99:4:7.691047725707579:0.3413757611023611
78:93:2.9318192055348415:0.4648065055530436
51:24:8.197221547880316:0.9718876331351113
92:27:9.198396797255626:0.16543165396821713
60:99:1.9852917319237706:0.6754749253049644
47:60:2.926189356051805:0.9833964216145247
62:53:8.777231457375235:0.7284431458587418
7:93:9.19597804582149:0.36088453244829266
18:16:6.90568418912085:0.47964664098450793
39:75:8.829446299574954:0.8566338306237319
79:27:0.30209164979904113:0.9162862348077758
41:47:2.8150099179100363:0.2202297658034008
34:87:5.121262434689546:0.34522516985978635
52:69:8.376611116331295:0.9721430380048826
31:7:8.568390078953934:0.2609747271152305
90:39:3.3323074765201985:0.3738473348752833
8:48:9.61161080916259:0.11255940822682353
29:42:1.046490193853703:0.9703762943992277
8:52:1.3703006046548538:0.06626361915074641
75:91:1.4840408750275702:0.6152611616346282
37:26:7.825349435322081:0.8239794472248464
22:84:4.940400200673159:0.7619456896388551
59:90:5.784623371260322:0.10666187082198009
21:86:0.3471548050285156:0.6173979174975259
42:90:8.178704998755766:0.4609972690917916
29:95:4.8406036225093:0.09106867309864752
53:58:2.9237402820948066:0.8880798506295625
64:21:9.633445470500215:0.3795317805234453
62:36:1.3372576343073461:0.9333367878739499
40:29:6.759730185924982:0.40384662674309035
20:56:0.9484204437096111:0.2700243168712728
91:13:5.593847444401174:0.14251730023029452
8:38:0.8118455773477451:0.15534571880671544
9:58:1.9613155867738086:0.9964979966329512
28:44:8.039543224362301:0.92149645738936
88:93:6.612640065946129:0.7475311567942813
40:19:7.620144079622059:0.3182416084415045
57:79:4.092781780687574:0.7267655904498955
11:49:5.677218509537134:0.620710266950606
87:9:0.5998969632207896:0.9462038643902423
79:52:0.3517405280543384:0.4570039635821589
98:32:3.0513911846409467:0.11192808504123763
48:26:2.144916542978387:0.7649703267181862
19:60:2.5378878697564122:0.5125815380642406
82:21:6.355984386247059:0.7538837278153913
76:82:9.092557531602177:0.74210496101346
18:7:9.409297527034369:0.9147893913873082
19:22:1.2673823917478577:0.3677686846010142
6:64:4.108843727499912:0.9996670729666973
59:39:3.968592643367236:0.6153826071593608
31:3:4.102429735302982:0.8091549649634372
75:67:6.408176908148027:0.4632439420199308
34:51:3.29285916399488:0.07362754325129339
96:44:0.8606828956909396:0.6380526760438952
32:55:7.632667913997516:0.8151815459035505
92:96:9.988069711163364:0.4201982040837776
76:55:2.7540455819170298:0.2728983302205812
71:69:0.2404635910591657:0.7019088464354047
83:7:3.7833428461767804:0.5059053649402946
54:81:3.347201881226524:0.6078784506485931
78:11:2.377145199017725:0.7250049576896354
28:25:0.6506573295260609:0.36397213695897845
71:55:5.388066989872032:0.7128044897824171
92:36:3.198366280780307:0.22223364625950692
87:38:1.1459230613858307:0.7737585259260796
30:20:4.088682224830098:0.9133713693060721
61:96:0.8000325343560455:0.9509401081347084
64:96:4.276249476121806:0.5545140479374902
30:46:5.864776032398824:0.9878159000860662
41:70:5.026586660801719:0.2196825484242737
5:2:9.153538434547116:0.6408688000252408
94:57:1.5258929617167616:0.3422495707436871
3:89:7.383070659810189:0.502938336341504
97:34:9.399292391381532:0.24970335740592198
16:30:3.8929316804406353:0.22739535842351322
69:8:2.1316090927287434:0.49286696627785687
74:15:8.485630359109985:0.11702412916620186
27:7:2.1637920715821535:0.18835559198252183
69:82:7.987112691829116:0.8388020770869082
66:3:4.0359322253773:0.8073362451987808
64:21:0.4793067219400504:0.8144255386406495

wednesday_contacts.txt

500
Agent Index:Interacting Agent Index:Time Interval:Intensity
35:35:7.326649613582364:0.01880486356965283
35:53:7.365575713168727:0.07226775243298234
55:76:1.7425302606537474:0.4079740971798641
30:95:4.307720149563242:0.5889349769548973
98:69:6.544728451402593:0.48025388395194957
6:88:5.072512252507798:0.8389958044296311
45:47:3.8194943780736357:0.06756325928583062
60:36:3.4337954746207435:0.780608576755907
28:58:3.9573951790562067:0.8814937045907152
46:4:3.762262794683556:0.16848724165974238
97:11:8.693414441617387:0.7460650092914961
92:75:0.660206718405888:0.07335924898540769
46:82:3.8637328951746017:0.5951215759777483
28:22:9.800867101130882:0.20271952020628436
37:49:7.060494204085794:0.5366861962135869
30:86:0.33597015028233757:0.10758491189019004
46:77:1.2885169728264612:0.10729300794480534
10:50:5.187607731001368:0.6703832544752654
74:34:8.93544026576964:0.04549012455350221
73:68:9.591015242175292:0.6962332597341828
10:44:0.5823210493669018:0.9689789406714906
33:32:8.436879771291043:0.021557896227508477
22:11:3.438680750408264:0.5398584734054681
91:60:7.210324988424461:0.8787960159091067
36:78:6.454046898476056:0.18222360148833006
44:28:0.883667185292053:0.922569686376188
64:47:8.689647085944292:0.1418009831093382
62:89:2.7679253812446216:0.6435851301783628
78:1:3.352951970635684:0.4754766055188496
69:62:8.345007894678666:0.23798159130601404
50:92:4.481913480277774:0.006233696946746248
98:26:1.1338952630227828:0.673962261965644
37:99:1.0643710496145387:0.5908347291714198
28:4:9.920590767349442:0.6963377903623542
18:50:1.3809350166002898:0.9872588601452001
24:49:2.094116142874566:0.5748045544228527
25:42:5.693608169443762:0.2879191230183471
54:42:2.4086948504552073:0.02547746034366105
51:2:4.451408189223509:0.6032579558452265
60:20:9.005811670608857:0.9856136917050264
65:67:3.19976980590579:0.9186950562480023
3:47:7.181260983583534:0.3040250437725214
68:57:0.7383565151524951:0.723133921333527
55:66:8.367449475616333:0.07980719114975277
9:11:9.97517977101612:0.6127447997868063
31:33:9.975729093568951:0.1045218021830403
83:27:6.640381396448985:0.15661340183018102
95:37:0.726266659582897:0.8758271106350278
17:27:3.731549646431953:0.3229035252474013
86:73:9.8942767252751:0.3990519563823193
3:78:8.720720998194327:0.264559175826406
74:46:5.4153651996120225:0.4173530824931695
95:95:9.484928224637814:0.022785741619000488
82:9:9.340319131198878:0.7952612413547455
8:42:2.475879993476423:0.3886327787838908
82:97:9.866336760171512:0.7188202155620746
56:34:4.302447425405743:0.28633873882974836
81:45:3.724740927783703:0.9167028205119899
25:29:9.199922961967527:0.835041826144438
94:68:3.8825993503747194:0.1168420663032067
94:13:0.42404086307323285:0.7521313232183118
49:11:8.607236167340723:0.6186404850761289
20:25:2.005728328797962:0.05980946246418717
59:91:2.961636869772523:0.1292205857836043
52:74:5.743776860036771:0.9984405022263465
75:72:8.15665662213246:0.8856720647622587
52:74:7.689955305723457:0.24021318982090323
59:44:1.6743181640329041:0.1378869655341155
72:12:7.638936582230792:0.8289049815374233
16:20:5.910006486116654:0.8403350173657851
80:0:9.524612020268266:0.9890454406593926
46:85:0.1978619520777969:0.7542955579200908
94:45:8.182315486473957:0.5539970521800692
26:68:2.410142904138397:0.831822112439275
12:59:4.111959521312285:0.2511690380469713
16:41:8.11653477037407:0.4864135139348226
7:6:8.41408841840117:0.5166597024174876
82:88:7.539262351768925:0.4934590077378854
23:87:2.0191699791040407:0.4650993959348293
14:42:7.959803809049787:0.3859274394432979
72:39:4.988558132860267:0.9719137961462334
36:87:7.904796353087814:0.9033328489705758
49:41:6.441286834271189:0.8596066636809503
26:45:1.5494084880416248:0.21286592285078343
69:84:2.68713252804031:0.6352927537106948
92:50:8.125056564396175:0.964640333194231
25:50:5.745327231004454:0.4996238922147982
48:58:9.430548312366577:0.9662240771532448
51:7:1.5407008662168198:0.43018447051424324
94:85:0.33708865480739925:0.3999028227241679
8:42:0.4676328396476981:0.24681536908527746
3:54:2.082373656993525:0.8211009673756547
47:5:4.176150167437918:0.0068115940272549524
13:18:1.9920879889202947:0.4530248033751796
75:58:4.656733076780746:0.11008410284813352
87:66:5.736523536915983:0.714940200457393
0:99:5.146933211148694:0.14934608064069932
14:37:8.826800764399827:0.36862157844671417
44:89:8.617130159606878:0.629612083711045
76:62:3.808269227488712:0.720616750357066
85:88:6.012459883150846:0.16207079061869079
70:45:8.460933196001442:0.03484670155649383
91:61:9.052509705425388:0.8533345550696678
72:53:7.626567323195205:0.6547255311759907
14:87:6.123814961834867:0.10799512631697394
47:38:9.703366381545296:0.7861481672561152
50:2:6.921877657912186:0.0901922793109946
55:69:8.194239438728719:0.25190470736562665
98:56:2.4951109730602985:0.313928578913919
4:36:2.5502983171221585:0.13611513898232153
79:69:3.7285925099089035:0.17979490568657208
52:95:8.298231631001554:0.2418250870038332
54:74:0.683477780477264:0.12947423620560494
88:35:1.449078957263129:0.9971706032369194
42:6:0.35682089991305177:0.8220981329520314
6:81:5.059336042688641:0.06759370944002929
41:79:9.740240133624962:0.3483763795581215
19:82:6.050535299281256:0.2265648321824162
46:47:1.6596588569359982:0.1808366243887748
18:26:1.7194269810771157:0.469856221060189
40:25:4.586799772966139:0.011566746615992107
44:85:7.874474589247804:0.21940431108028746
21:86:2.022571555919419:0.0267482489112576
55:1:2.4888121465990034:0.6949916683725545
6:65:2.0048251474987087:0.6007769598222094
46:99:5.203358357806579:0.49351952534458354
16:89:7.784970962656438:0.3795554828381028
83:56:4.564802235334828:0.30456464371028225
19:79:1.6659490128697307:0.09406007012891293
77:23:8.95317196939301:0.861612362884061
94:93:5.5446862725779695:0.6345596358727227
17:81:0.8694353708834501:0.8610458639123718
42:77:1.0046753749712778:0.8361913703426427
80:82:7.898528087560851:0.7837288261770342
99:67:5.6795968227766185:0.37214616970155145
57:36:2.7938124820144363:0.10872997346449254
45:73:5.180491151160156:0.183795261793106
10:7:7.565100574481845:0.033325877812489635
79:38:3.158002500478579:0.8762332523999679
46:13:0.8163960575888862:0.11687793448566342
21:98:6.981821368222011:0.6207591025489955
73:8:2.3297846497396932:0.38686431622259887
78:63:0.4436434330479888:0.3894958140703567
34:59:7.191388054443503:0.20095769579977418
73:30:6.053525772644528:0.5967585140005333
41:40:1.3838754859326574:0.502405241786681
49:60:8.295110054329713:0.4305993322400177
83:63:0.48547541285623885:0.32805016959748434
93:31:3.6848854616150293:0.35643612817628656
12:78:0.2273456842385202:0.953077640082888
30:89:7.153927919430508:0.43586809846112506
56:81:0.09816756643055147:0.6295484364565437
83:7:7.4085537764271745:0.033305634133947604
92:42:9.988928688371795:0.2761232871677961
70:88:6.140537017964541:0.9795273172673395
2:90:6.461894038111831:0.1185444682840926
72:46:9.953506502098817:0.15329163492922593
23:90:1.9471840933452211:0.7386518914419935
10:35:6.373387939809772:0.7171440826931259
62:22:0.5596942604876298:0.1432896719544473
38:92:0.9481383578775293:0.7677431462939046
74:22:0.24856782036149716:0.42812724703916316
14:83:8.576386817177024:0.9396215064528413
99:2:9.30095330632649:0.5066561439508125
63:22:6.403955394913:0.1571691479302042
78:87:8.344142685105586:0.2861576111801998
71:55:1.4476785659120595:0.33708688327676783
36:87:0.21348728262420402:0.28711138205567277
4:65:5.67218610317065:0.36711320844191897
2:81:9.67653850599029:0.03156219872649857
61:88:5.724543360415383:0.7932661225893052
53:59:9.185083874031697:0.026215655980081132
7:40:2.8312580779681875:0.49196082108715233
76:96:2.7315283363582776:0.6519656627078148
86:93:8.443020347066351:0.1248034896150344
20:95:3.9697649231974808:0.04606324434996534
73:11:2.5528837643963884:0.6948268362453378
50:68:5.756463265683402:0.995685718348466
41:92:6.3336884749824645:0.1621356000106734
43:18:9.664694397742583:0.8045465791810523
2:39:3.9394795994181777:0.5867371111778215
1:82:4.841485437297484:0.49371708023469907
65:53:2.7498717037820644:0.20583454120465094
73:90:1.121137779062057:0.7937686270961586
53:87:6.2353544374513845:0.015946094567476976
44:63:7.393381816549878:0.3472456996800237
15:90:6.9611182251442685:0.8039647151078758
88:27:8.01133506522802:0.4130507216742557
54:87:0.23852744551902894:0.6264091805644847
21:16:1.081879835206111:0.7790590360105223
22:69:4.123929864290275:0.9702498443951281
9:97:3.0488474948409316:0.677209238072471
67:83:4.86032668987452:0.27054126955750546
62:20:7.499374514038265:0.003010254325323869
24:69:0.3027367484925514:0.42734474246664456
90:30:8.329167767245044:0.13749033066927274
84:33:5.994927378398588:0.3756720097493309
9:76:7.402293740792216:0.05790492058061891

Example_3

This example is a remodelling of Example_1 which uses a Scheduled model comprising of the states- Susceptible, Exposed, Asymptomatic, Symptomatic and Recovered (Scheduled SEYAR).

Here, the interactions have been broadly classified as weekday interactions and weekend interactions.

This example helps to gauge the disease transmission by generalising contacts over the weekdays and weekends.

Users who do not have valid data about the interactions and events taking place every single day throughout the week may use this example to get an approximate idea about infection spread.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import math

import episimmer.model as model


#User defined functions
#This function is user defined, based on the parameters the user has inputed in agents file and interaction/contact file
#This function represents the probability of getting infected during a single interaction/contact
def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):

	p_inf_symp,p_inf_asymp=p_infected_states_list[0],p_infected_states_list[1]
	#EXAMPLE 1
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_asymp
	else:
		return 0


class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Exposed':1},probability_of_infection_fn,[0.4,0.2]),False,0.9)
		self.insert_state('Exposed',1,0,self.scheduled({'Symptomatic':0.4,'Asymptomatic':0.6}),False,0.1)
		self.insert_state('Symptomatic',11,5,self.scheduled({'Recovered':1}),True,0)
		self.insert_state('Asymptomatic',9,3,self.scheduled({'Recovered':1}),True,0)
		self.insert_state('Recovered',100, 0,self.scheduled({'Recovered':1}),False,0)

agents.txt

300
Agent Index:Type:Susceptibility
0:Office Staff:0.7
1:Office Staff:0.7
2:Office Staff:0.7
3:Office Staff:0.7
4:Office Staff:0.7
5:Office Staff:0.7
6:Office Staff:0.7
7:Office Staff:0.7
8:Office Staff:0.7
9:Office Staff:0.7
10:Office Staff:0.7
11:Office Staff:0.7
12:Office Staff:0.7
13:Office Staff:0.7
14:Office Staff:0.7
15:Office Staff:0.7
16:Office Staff:0.7
17:Office Staff:0.7
18:Office Staff:0.7
19:Office Staff:0.7
20:Office Staff:0.7
21:Office Staff:0.7
22:Office Staff:0.7
23:Office Staff:0.7
24:Office Staff:0.7
25:Office Staff:0.7
26:Office Staff:0.7
27:Office Staff:0.7
28:Office Staff:0.7
29:Office Staff:0.7
30:Office Staff:0.7
31:Office Staff:0.7
32:Office Staff:0.7
33:Office Staff:0.7
34:Office Staff:0.7
35:Office Staff:0.7
36:Office Staff:0.7
37:Office Staff:0.7
38:Office Staff:0.7
39:Office Staff:0.7
40:Office Staff:0.7
41:Office Staff:0.7
42:Office Staff:0.7
43:Office Staff:0.7
44:Office Staff:0.7
45:Office Staff:0.7
46:Office Staff:0.7
47:Office Staff:0.7
48:Office Staff:0.7
49:Office Staff:0.7
50:Office Staff:0.7
51:Office Staff:0.7
52:Office Staff:0.7
53:Office Staff:0.7
54:Office Staff:0.7
55:Office Staff:0.7
56:Office Staff:0.7
57:Office Staff:0.7
58:Office Staff:0.7
59:Office Staff:0.7
60:Office Staff:0.7
61:Office Staff:0.7
62:Office Staff:0.7
63:Office Staff:0.7
64:Office Staff:0.7
65:Office Staff:0.7
66:Office Staff:0.7
67:Office Staff:0.7
68:Office Staff:0.7
69:Office Staff:0.7
70:Office Staff:0.7
71:Office Staff:0.7
72:Office Staff:0.7
73:Office Staff:0.7
74:Office Staff:0.7
75:Office Staff:0.7
76:Office Staff:0.7
77:Office Staff:0.7
78:Office Staff:0.7
79:Office Staff:0.7
80:Office Staff:0.7
81:Office Staff:0.7
82:Office Staff:0.7
83:Office Staff:0.7
84:Office Staff:0.7
85:Office Staff:0.7
86:Office Staff:0.7
87:Office Staff:0.7
88:Office Staff:0.7
89:Office Staff:0.7
90:Office Staff:0.7
91:Office Staff:0.7
92:Office Staff:0.7
93:Office Staff:0.7
94:Office Staff:0.7
95:Office Staff:0.7
96:Office Staff:0.7
97:Office Staff:0.7
98:Office Staff:0.7
99:Office Staff:0.7
100:Developer:0.5
101:Developer:0.5
102:Developer:0.5
103:Developer:0.5
104:Developer:0.5
105:Developer:0.5
106:Developer:0.5
107:Developer:0.5
108:Developer:0.5
109:Developer:0.5
110:Developer:0.5
111:Developer:0.5
112:Developer:0.5
113:Developer:0.5
114:Developer:0.5
115:Developer:0.5
116:Developer:0.5
117:Developer:0.5
118:Developer:0.5
119:Developer:0.5
120:Developer:0.5
121:Developer:0.5
122:Developer:0.5
123:Developer:0.5
124:Developer:0.5
125:Developer:0.5
126:Developer:0.5
127:Developer:0.5
128:Developer:0.5
129:Developer:0.5
130:Developer:0.5
131:Developer:0.5
132:Developer:0.5
133:Developer:0.5
134:Developer:0.5
135:Developer:0.5
136:Developer:0.5
137:Developer:0.5
138:Developer:0.5
139:Developer:0.5
140:Developer:0.5
141:Developer:0.5
142:Developer:0.5
143:Developer:0.5
144:Developer:0.5
145:Developer:0.5
146:Developer:0.5
147:Developer:0.5
148:Developer:0.5
149:Developer:0.5
150:Developer:0.5
151:Developer:0.5
152:Developer:0.5
153:Developer:0.5
154:Developer:0.5
155:Developer:0.5
156:Developer:0.5
157:Developer:0.5
158:Developer:0.5
159:Developer:0.5
160:Developer:0.5
161:Developer:0.5
162:Developer:0.5
163:Developer:0.5
164:Developer:0.5
165:Developer:0.5
166:Developer:0.5
167:Developer:0.5
168:Developer:0.5
169:Developer:0.5
170:Developer:0.5
171:Developer:0.5
172:Developer:0.5
173:Developer:0.5
174:Developer:0.5
175:Developer:0.5
176:Developer:0.5
177:Developer:0.5
178:Developer:0.5
179:Developer:0.5
180:Developer:0.5
181:Developer:0.5
182:Developer:0.5
183:Developer:0.5
184:Developer:0.5
185:Developer:0.5
186:Developer:0.5
187:Developer:0.5
188:Developer:0.5
189:Developer:0.5
190:Developer:0.5
191:Developer:0.5
192:Developer:0.5
193:Developer:0.5
194:Developer:0.5
195:Developer:0.5
196:Developer:0.5
197:Developer:0.5

config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <50>
Agent Parameter Keys <Agent Index:Type:Susceptibility>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index:Time Interval>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <>
Location list filename <>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_agents.py

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type:Susceptibility'
	info_dict['Type']=['Office Staff','Developer','Designer']
	info_dict['Susceptibility']=['0.7','0.5','1']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+info_dict[j][int(i/100)])
		f.write('\n')


write_to_file('agents.txt',300)

generate_contacts.py

import random

import numpy as np


def write_to_file(filename,n,no_contacts):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index:Room Ventilation'

	f=open(filename,'w')
	f.write(str(no_contacts)+'\n')
	f.write(header+'\n')

	for i in range(no_contacts):
		agent=random.randint(0,n-1)
		contact_agent=random.randint(0,n-1)
		room_ventilation=random.random()
		f.write(str(agent)+':'+str(contact_agent)+':'+str(room_ventilation)+'\n')


write_to_file('weekday_contacts.txt',300,500)
write_to_file('weekend_contacts.txt',100,200)

interaction_files_list.txt

<weekday_contacts.txt>
<weekday_contacts.txt>
<weekday_contacts.txt>
<weekday_contacts.txt>
<weekday_contacts.txt>
<weekend_contacts.txt>
<weekend_contacts.txt>

weekday_contacts.txt

500
Agent Index:Interacting Agent Index:Time Interval
86:194:0.363382677097838
23:60:0.05737508630439536
109:88:0.45794609256026597
23:141:0.5326634717797075
299:29:0.006734061473219755
13:247:0.6286590175132486
158:57:0.18082438170571258
251:251:0.9519285487708604
107:23:0.742462470241382
44:160:0.10285448532918684
246:274:0.39491731832217347
105:127:0.3341803119034249
1:174:0.9969373839469244
263:200:0.8423653895324584
244:65:0.33937038548754117
269:57:0.48614330828732555
132:136:0.44976001666591714
226:230:0.49539846919393515
93:200:0.7559543913204214
58:147:0.01834053747072839
260:273:0.21822865069483433
178:136:0.298072406473165
221:32:0.7558013532248159
106:161:0.7996376078347497
246:94:0.960508722484202
233:58:0.07019633430167904
117:244:0.060562662456084326
158:271:0.6641867817478397
37:135:0.40423448794829175
50:72:0.8232365257744092
291:196:0.5856674426589038
37:95:0.17594777098447156
137:218:0.2764172465951007
248:270:0.48124554876359227
226:162:0.462186877573173
115:282:0.8971316529276944
226:249:0.786932309810938
239:162:0.3991380771019811
152:175:0.5038893774078231
57:143:0.24785503325970137
197:232:0.5422477248558065
70:216:0.4438739309259937
183:262:0.07745927132117303
297:223:0.3242344852921568
78:58:0.04656415789927448
288:90:0.9065990607024984
237:49:0.5221692082913499
111:15:0.7534925296966496
142:293:0.20347231872591687
58:111:0.9130598304242583
244:239:0.883233212874234
37:289:0.4526572251099553
23:272:0.656652850732811
132:180:0.15193979711878325
285:132:0.38719252083343847
265:70:0.7102260860264488
275:281:0.3801859777615577
234:125:0.9046412406218968
65:292:0.11839064287684187
84:260:0.2530985966726006
90:31:0.4016686706133782
163:146:0.14287369519201176
8:248:0.6967240386110859
77:156:0.26515665703947633
183:287:0.6989534834349946
68:215:0.11481872440923402
98:245:0.7722998686336559
143:158:0.3681875412524903
224:183:0.502218145131218
158:2:0.6026560984068073
189:94:0.9319973057671235
59:280:0.41894970612916904
289:0:0.33455374399786897
46:5:0.8071199222806446
33:271:0.18449984750803194
233:299:0.09056863000660853
231:48:0.17758642516269996
280:39:0.04328882534444067
282:67:0.669048440451856
254:64:0.3189782344027632
76:17:0.2196952676995123
246:277:0.15192436983253998
69:56:0.4247050422014911
74:157:0.5939188585025204
265:36:0.30084578810895235
110:225:0.22643940227841797
13:103:0.8332915331978291
13:16:0.06488051084215773
279:100:0.9615034503622395
131:7:0.6821420773438361
1:37:0.6677760907690996
188:158:0.8626653776959949
6:237:0.8348739038044689
66:89:0.6182622079932064
49:32:0.353727219858078
34:271:0.003003110714105084
146:110:0.025196453940818486
119:147:0.4497086397318325
20:211:0.6739909753015358
240:293:0.4550940251868444
209:6:0.7890407556568965
142:256:0.32959011831311325
160:287:0.9716755722220477
243:49:0.48470610304984507
47:5:0.3806170044235232
114:49:0.8109087230488309
41:201:0.34982385429562946
105:109:0.15453140623929296
189:182:0.9987148573984527
190:10:0.6884612962428445
128:144:0.4197320908568287
194:9:0.18474621844504158
147:215:0.03680454870432193
253:107:0.9203894716002635
43:269:0.24002484994371742
78:220:0.6502655201684909
137:280:0.1401085158602715
151:118:0.07167680488633343
254:292:0.6529724699042042
141:265:0.39900166693133055
138:88:0.9932565879608933
272:37:0.30850648882541
275:187:0.6482628935877461
52:101:0.7989984618298684
224:46:0.9994325610813167
235:57:0.2563353771650976
262:275:0.44538958692385966
278:197:0.053750049353002205
158:111:0.37436177958465555
76:66:0.3348842707235894
46:115:0.7142496848656643
279:206:0.06842916154209433
141:206:0.17021915688443245
224:32:0.7961735112777278
130:107:0.7073683270230737
243:69:0.12452186337589866
26:73:0.3939868537230834
218:250:0.49626620772379104
30:18:0.9131388595492964
1:296:0.5125777820034538
253:178:0.4164696825851325
138:278:0.597683741507795
288:151:0.4352091237102317
16:74:0.8947838287826291
56:161:0.4232408850483762
125:247:0.573706510635327
40:102:0.18154180653123564
60:40:0.4198515964666686
72:149:0.4187418837320297
75:251:0.39660787331923975
1:249:0.8028977438395267
148:49:0.0470201864939368
213:124:0.9649994646127331
190:233:0.14775262245870946
103:71:0.05474865415255736
47:265:0.8533566610628922
269:228:0.7375518597879671
67:169:0.9496769944040246
7:218:0.8304403416049523
104:210:0.6686329684597243
31:55:0.7776200366434306
184:249:0.7222393916956648
42:119:0.08564625136703519
266:119:0.2236585583035382
60:163:0.6738857354376312
72:40:0.07448199525388277
21:80:0.0748936140349411
278:40:0.4805754800661015
171:37:0.1522121169824765
255:40:0.7705164164916082
200:94:0.9761227908663562
150:80:0.8026269260870561
147:129:0.18643765593714945
153:292:0.10091816738929849
32:255:0.41186308663387594
15:42:0.186276427571201
272:167:0.790901664913838
118:233:0.6465225126239372
70:282:0.7745919435644555
120:65:0.1454760297186556
228:230:0.6333391258668252
239:229:0.4543077744078845
174:199:0.6027975994256701
219:25:0.13238273280484747
269:299:0.3449185353142791
117:4:0.5341178811039785
288:26:0.10512978773834103
299:253:0.03606322419137731
4:20:0.96866090907813
142:298:0.8176190418375724
228:56:0.7157321928629918
252:236:0.2780206260172835
160:70:0.7394534717330496
51:236:0.9378676232322529
15:284:0.7276905018224065
12:228:0.45925036590036117
142:233:0.8257956616436953
150:18:0.5973796855420124

weekend_contacts.txt

200
Agent Index:Interacting Agent Index:Time Interval
9:27:0.7285820490251275
79:30:0.43643267663334784
41:97:0.3154443198946647
89:81:0.11728253995313387
61:0:0.6292758581589997
81:68:0.11658195025730422
51:42:0.8663124120452983
53:36:0.13745025623570328
24:23:0.5454298200874506
74:30:0.4418880258696175
84:50:0.7426280143108092
1:56:0.48148017923781816
47:31:0.060247113295321064
48:33:0.027821145349981813
15:46:0.4047557258531924
58:57:0.7677138219259788
7:37:0.5695610700353275
41:80:0.4868919400666598
91:1:0.44572956054671264
65:65:0.8184337090007665
72:93:0.6233878336385458
13:50:0.7982555614439892
50:43:0.6435229692924852
74:48:0.18735649262419363
97:21:0.5197075259377777
11:46:0.505955942211802
13:61:0.4288339878254973
9:51:0.43044501616932207
67:16:0.1502552019369643
27:44:0.7830943076006983
38:66:0.13861648219027933
87:65:0.4241581472297228
25:96:0.02322988852501151
74:25:0.9414510134524089
68:69:0.2738617364986812
13:5:0.9005335573109458
54:45:0.1924656860815962
59:17:0.36171522590733096
16:46:0.9090115224227183
88:75:0.6889231457811219
66:98:0.060084372913805195
15:5:0.7382695034997636
42:26:0.7534090986161659
10:59:0.7406463452145128
1:56:0.5223259583426555
2:53:0.3778207773493224
12:59:0.6527332906907739
62:40:0.6488937581016144
29:99:0.8133083770929995
33:90:0.6747685263582849
34:43:0.24268710004046734
35:26:0.532751273683614
87:57:0.1501292799958357
57:40:0.6123648951398082
74:10:0.4152419866455974
80:78:0.5012705076900813
69:24:0.6676719379202161
31:29:0.6662890500090981
14:5:0.8210185511790247
94:85:0.7720579665922562
29:4:0.25164912122147276
20:51:0.5105009112231861
89:46:0.6528751025315475
95:66:0.802192342698625
39:33:0.9101612303599376
81:33:0.8543775546407352
21:56:0.3706113844831149
68:44:0.12422646481572663
48:43:0.34682001446864763
13:17:0.6276500371441114
63:94:0.8146946568645466
88:78:0.5209578611791694
97:36:0.5123862949195922
52:97:0.4574947691538137
43:30:0.44561396193011504
46:29:0.6217236713947522
56:5:0.8772376795860019
4:16:0.6217621242078103
94:12:0.8230146856102053
14:74:0.6988758758853924
17:66:0.3472631714141693
40:20:0.12207650155552974
25:23:0.5800189525117784
66:88:0.6231440219982287
44:18:0.1322346728631314
25:63:0.6618679383445565
31:47:0.4610914135225338
46:89:0.5190042465490252
47:42:0.00815358980027292
86:65:0.6629266107589123
41:6:0.3054998038668849
7:26:0.6170363111465597
7:71:0.057822962934058086
20:99:0.10650563537390068
88:50:0.8519175577245196
80:68:0.9045120816451955
95:93:0.6002774315662224
55:17:0.05348533516312315
9:73:0.9434486171598113
24:67:0.9653899553809171
26:72:0.3500033869960779
98:94:0.6471491235861588
44:64:0.4925243637130817
80:24:0.36347250972453493
68:14:0.10755205351312014
41:92:0.5534739132333136
68:76:0.32888896259325007
78:62:0.1829331563912473
62:52:0.7666092813105568
9:35:0.605864942537051
72:14:0.7603134311799778
97:55:0.18169217686916594
36:31:0.9554387517081616
81:80:0.7878947956757256
77:3:0.34460592606183593
55:22:0.46426480497754763
82:11:0.566208134085341
75:13:0.2120654842769314
72:72:0.6860038205658056
14:44:0.19369672128787352
28:92:0.843721177343362
13:77:0.19323777487351812
24:51:0.5449500685706895
11:18:0.7713598344121039
75:40:0.07904358227043118
95:88:0.5352527545939727
91:22:0.8217839918727673
19:62:0.6616821073865188
85:84:0.2956423186993312
38:94:0.9384682183894522
6:55:0.3665512544012547
92:84:0.708453603460874
94:58:0.08117462718888191
96:51:0.4178148231204242
26:52:0.838227869464405
43:94:0.5552463928422697
28:72:0.379227837297593
55:41:0.6264748109369973
39:77:0.598279966773809
60:32:0.4893552884083898
32:91:0.4214880241005229
88:59:0.09083345798603704
73:27:0.9462435120077348
23:18:0.06916520818263272
15:95:0.9016398071698573
77:59:0.28385632668515914
0:3:0.29921694525977405
30:14:0.9626807472136768
42:24:0.8850199206146271
76:88:0.3795640240125865
36:7:0.7645121997604492
84:42:0.7746278270508948
89:18:0.8065816783548845
41:25:0.23240771047169273
81:6:0.7955775578407817
45:9:0.9731745872561515
90:9:0.6247358599634997
17:31:0.2630901922381782
23:33:0.34624303536116974
89:17:0.9900291304139344
16:53:0.6706330916247727
54:62:0.5258139282918697
64:19:0.008898383173741808
32:17:0.7090567698435072
3:0:0.4598274286770714
52:26:0.16077246925129873
76:49:0.9206884789189445
56:22:0.034833882748383616
73:3:0.895648468674582
68:74:0.7933804792856991
73:91:0.12836423179759393
74:33:0.7975432659046741
8:46:0.3595264311357027
41:77:0.528005322314149
17:21:0.14794508161072506
28:76:0.032913082466142884
41:55:0.360780391399085
19:94:0.535819004389484
32:6:0.6930718827077623
89:65:0.7794182681357521
32:11:0.20415982454366854
98:69:0.9823460910304447
86:45:0.5727646627164533
33:65:0.5688298896215904
1:33:0.360258869666344
95:22:0.15350203769224513
38:86:0.18196978924728302
20:45:0.5166470856920276
71:64:0.23502842989578565
87:39:0.41301175814214985
77:52:0.37615432912780833
71:30:0.7061446694808394
97:22:0.6138999794578468
66:21:0.07118958421535238
68:89:0.8631414492866706
5:59:0.40500132701992997
5:33:0.4418457705742146
52:94:0.40395873627358325

Scheduled_SEYAR

This example has been built by introducing two additional states to the Scheduled SIR disease model- “Asymptomatic” and “Symptomatic”.

It includes one event where the agents either contribute to or receive from ambient infection.

This is a basic example which is used as a baseline for building more complex Scheduled models by introducing complexity in events, interactions, policies and restrictions.

This example helps in showing disease spread through an event where all agents interact with each other, and where transition from one state to another, for a population, may be symmetric about a mean. This is important for scenarios in which the population under consideration collectively shows a state transition that averages to a mean with some deviation. For custom distributions, please refer to the Miscellaneous examples directory.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.6
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	beta=0.001
	return ambient_infection*beta


class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Exposed':1}),False,0.95)
		self.insert_state('Exposed',5,2,self.scheduled({'Symptomatic':0.3,'Asymptomatic':0.7}),False,0.02)
		self.insert_state('Symptomatic',11,5,self.scheduled({'Recovered':1}),True,0.02)
		self.insert_state('Asymptomatic',6,3,self.scheduled({'Recovered':1}),True,0.01)
		self.insert_state('Recovered',100, 0,self.scheduled({'Recovered':1}),False,0)

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
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38
39
40
41
42
43
44
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51
52
53
54
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58
59
60
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config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Scheduled_SIR

The disease transmission between states may be defined by schedule change in states. The states are Susceptible, Infected and Recovered.

The mean and variance parameters are used for the independent transitions.

Episimmer has used this example as a fundamental Scheduled model which uses the SIR compartmentalised representation.

In some populations, the agents might show a recovery period of 10 days on an average with a deviation of say 3 days, instead of the whole population recovering at some constant rate. In other words, the transition between states may be symmetric about some mean. In such situations, the transition between the states can be scheduled with some mean and standard deviation.

This model provides a more realistic method of transitioning between states with respect to how the general population reacts to disease spread.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	beta=0.001
	return ambient_infection*beta


class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Infected':1}),False,0.99)
		self.insert_state('Infected',6,3,self.scheduled({'Recovered':1}),True,0.01)
		self.insert_state('Recovered',0, 0,self.scheduled({'Recovered':1}),False,0)

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Stochastic_SEYAR

This example has been built by extending the Stochastic SIR model by introducing the additional states “Asymptomatic” and “Symptomatic”.

Episimmer uses this example to explore a different type of compartmentalised model. This helps ascertain the fact that the simulation works for more complex state transitions as well (Exposed to Asymptomatic and Symptomatic).

This example proves that we can have multiple transitions from a single state and each of these transitions can be chosen with a certain probability.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.5
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0.01
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection())
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.1))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.1))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
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1
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config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <50>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Stochastic_SIR

This example has been built using the Stochastic Model consisting of three states Susceptible, Infected and Recovered.

The agents are allowed to interact in an event at a location where they can contribute to or receive from an ambient infection.

As output, it plots a graph that helps us visualize disease spread over a specified number of time steps and a specified number of worlds.

This example is the fundamental Stochastic model in Episummer which uses the SIR compartmentalised representation.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model

#The two functions event_contribute_fn and event_receive_fn together control the spread of infection

# This function states the amount an agent contributes to ambient infection in the region
#note that only infected agents contibute to the ambient infection
def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0

#This function states the probability of an agent becoming infected from the ambient infection
def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']	#These are the states that will be used by the compartmental model
		infected_states=['Infected']	#These are the states that can infect
		state_proportion={				#This is the starting proportions of each state
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)  #We use the inbuilt model in the package
		self.set_transition('Susceptible', 'Infected', self.p_infection())	#Adding S-> I dependent transition
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))	#Adding the I->R independent transition


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
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agents.txt

1000
Agent Index
0
1
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config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Stochastic_SIRID_variable_rates

This example has been built by extending the Stochastic SIR model by introducing the additional states “ICU” and “Dead”.

This example demonstrates the flexibility that can be achieved while working with different compartmentalised models.

During the outbreak of an infection, the agents of the population may adapt to the changing environments by inventing new and better medication. Such behavioural approaches may influence the rate at which the population progresses to a state.

This shows that it is not always necessary to have a constant rate of transition between states. The variable rates of state transition may be defined as a function of the current time step (as seen in UserModel.py).

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		#if time_step%10<2:
		#	return True
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


#User defined functions
def fn1(current_time_step): #ICU recovery improves over time as doctors learn how to treat better
	return min(0.5,0.2+current_time_step*0.01)

def fn2(current_time_step): #People going into ICU decreases due to better drugs
	return max(0.02,0.1-current_time_step*0.001)

def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	#6th to 10th day lockdown so no spread, after that precations are taken so rate of infection decreases
	if current_time_step<5:
		beta=0.001
	elif current_time_step<10:
		beta=0
	else:
		beta=0.0008
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered','ICU','Dead']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0,
							'ICU':0,
							'Dead':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.1))
		self.set_transition('Infected', 'ICU', self.p_function(fn2))
		self.set_transition('ICU', 'Recovered', self.p_function(fn1))
		self.set_transition('ICU', 'Dead', self.p_standard(0.05))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Interaction_Spaces

The examples covered under this sub-directory cover clear cut examples of Episimmer’s flexibility on how agents can interact with each other in the simulation environment.

Currently, Episimmer can model the following modules for agent interactions :

1. Interactions (Simple one-to-one agent contact)

2. Events (Complete graph of interactions)

3. One time event

4. Probabilistic Interactions

For further details, refer to Tutorial 2.2.

To run an example :

            cd examples/Interaction_Spaces
            python ../../episimmer/main.py Two_events_alternating_with_intersection

---

Double_Style_Interactions

This example showcases the use of both regular and probabilistic interactions.

Since we have both types of interactions, we should use ‘Agent Index:Interacting Agent Index’ key, omitting the ‘Probability:Agents’ as the parameter keys. Additional user-defined parameters (like ‘duration’) must be present in both the interaction files and appended to the Interaction Info Keys.

Thus our config.txt has the parameters : ‘Agent Index:Interacting Agent Index:duration’

The disease model being used here is the Stochastic Model comprising of the states Susceptible, Infected and Recovered.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.8
	return 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.8,
							'Infected':0.1,
							'Recovered':0.1
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))



		self.name='Stochastic SIR'

agents.txt

12
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11

config.txt

Random Seed <10>
Number of worlds <10>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index:duration>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <probabilistic_interaction_files_list.txt>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

# This code require two inputs, 'number of agents' and 'probability of edge'
# To run <python generate_files.py 100 0.1>

import random
import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_interactions(filename,no_agents,p):
	info_dict={}
	#Agent ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index'
	lines=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				lines.append(str(i)+':'+str(j)+'\n')
				lines.append(str(j)+':'+str(i)+'\n')

	f=open(filename,'w')
	f.write(str(len(lines))+'\n')
	f.write(header+'\n')

	for line in lines:
		f.write(line)

number_of_agents=int(sys.argv[1])
p=float(sys.argv[2])
write_agents('agents.txt',number_of_agents)
write_interactions('interactions_list.txt',number_of_agents,p)

generate_files_csv.py

import csv
import random


def write_agents(filename,no_agents):
	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)

	    writer.writeheader()
	    for i in range(no_agents):
	    	writer.writerow({'Agent Index': i})


def write_interactions(filename,no_agents,p):
	agent_list=[]
	interacting_agent_list=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				agent_list.append(i)
				agent_list.append(j)
				interacting_agent_list.append(j)
				interacting_agent_list.append(i)

	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index','Interacting Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)
	    writer.writeheader()
	    for i in range(len(agent_list)):
	        writer.writerow({'Agent Index':agent_list[i],'Interacting Agent Index':interacting_agent_list[i]})




write_agents('agents.csv',100)
write_interactions('interactions_list.csv', 100, 0.1)

interaction_files_list.txt

<interactions_list.csv>

interactions_list.csv

Agent Index,Interacting Agent Index,duration
0,1,9
1,2,4
3,4,4
4,5,3
6,7,5

locations.txt

1
Location Index
0

probabilistic_interaction_files_list.txt

<probabilistic_interactions_list.txt>

probabilistic_interactions_list.txt

10
Probability:Agents:duration
1:3,5,10,11:4
0.5:0,1,2:4
0.2:6,7:45
1:8:67
0:9:2
0.1:3,4,5,6:44
0.1:1,2,9:45
0.1:4,7:67
0.1:10,11:12
0.1:1,10:34

One_Time_Event

This example uses the Stochastic SIR model where the agents can interact with each other via cycling events (standard events) and one time events.

The one time event has to be configured in a separate file which then needs to be included in the config.txt file.

While configuring the file containing the one time event, along with specifying the agents that are a part of it, it is also necessary to specify the times steps which are involved in the event.

In Episimmer, an event is enforced by calling it cyclicly in an order specified in the event_files_list.txt, and these events take place throughout the specified number of time steps.

At times, in realistic scenarios such as in a university, there are some events that do not take place periodically. For instance, cultural week might be for only one week within a span of two months.

Hence, the concept of one time events has been introduced in this example which helps us specify such infrequent and exceptional events as part of the considered time period.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
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40
41
42
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44
45
46
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50
51
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54
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84
85
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91
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103
104
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106
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178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
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24
25
26
27
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29
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31
32
33
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35
36
37
38
39
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43
44
45
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51
52
53
54
55
56
57
58
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60
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62
63
64
65
66
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68
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70
71
72
73
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75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
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141
142
143
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153
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155
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159
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161
162
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181
182
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196
197

config.txt

Random Seed <3>
Number of worlds <3>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <one_time_event.txt>

empty_event.txt

0
Location Index:Agents

event_files_list.txt

<non_empty_event.txt>
<empty_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

non_empty_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

one_time_event.txt

1
Time Step:Location Index:Agents
1,3:0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Probabilistic_Interaction

This example showcases the use of probabilistic interactions. The agents can contribute or receive infection based on who they interact with, and these interactions are probabilistically determined.

While configuring the probabilistic interactions file, along with specifying the agents that are interacting with each other, it is necessary to specify the probability with which they interact.

The probabilistic interactions are considered to be bidirectional (as compared to interactions which are unidirectional) and hence can be specified as: Probability:Agents where “Agents” are all the interacting agents separated by commas.

Also note that you need not pass ‘Probability:Agents’ in the config.txt interactions info keys but may do so to enforce the use of only probabilistic interactions (Adding normal interactions would give you an error).

The disease model being used here is the Stochastic Model comprising of the states Susceptible, Infected and Recovered.

For instance, the interaction between a student and a teacher in a university can be considered to be probabilistic taking into account that the student has a consistent 95% attendance.

Hence, probabilistic interactions allow us to specify interactions between agents that are not most definitely bound to happen.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.8
	return 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.8,
							'Infected':0.1,
							'Recovered':0.1
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))

		self.name='Stochastic SIR'

agents.txt

12
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11

config.txt

Random Seed <10>
Number of worlds <10>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Probability:Agents>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <probabilistic_interaction_files_list.txt>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

# This code require two inputs, 'number of agents' and 'probability of edge'
# To run <python generate_files.py 100 0.1>

import random
import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_interactions(filename,no_agents,p):
	info_dict={}
	#Agent ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index'
	lines=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				lines.append(str(i)+':'+str(j)+'\n')
				lines.append(str(j)+':'+str(i)+'\n')

	f=open(filename,'w')
	f.write(str(len(lines))+'\n')
	f.write(header+'\n')

	for line in lines:
		f.write(line)

number_of_agents=int(sys.argv[1])
p=float(sys.argv[2])
write_agents('agents.txt',number_of_agents)
write_interactions('interactions_list.txt',number_of_agents,p)

generate_files_csv.py

import csv
import random


def write_agents(filename,no_agents):
	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)

	    writer.writeheader()
	    for i in range(no_agents):
	    	writer.writerow({'Agent Index': i})


def write_interactions(filename,no_agents,p):
	agent_list=[]
	interacting_agent_list=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				agent_list.append(i)
				agent_list.append(j)
				interacting_agent_list.append(j)
				interacting_agent_list.append(i)

	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index','Interacting Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)
	    writer.writeheader()
	    for i in range(len(agent_list)):
	        writer.writerow({'Agent Index':agent_list[i],'Interacting Agent Index':interacting_agent_list[i]})




write_agents('agents.csv',100)
write_interactions('interactions_list.csv', 100, 0.1)

locations.txt

1
Location Index
0

probabilistic_interaction_files_list.txt

<probabilistic_interactions_list.txt>

probabilistic_interactions_list.txt

10
Probability:Agents
1:3,5,10,11
0.5:0,1,2
0.2:6,7
1:8
0:9
0.1:3,4,5,6
0.1:1,2,9
0.1:4,7
0.1:10,11
0.1:1,10

Scheduled_SEYAR_Interactions

This example builds over the Scheduled SEYAR model. It uses interaction constructs in Episimmer to define contact between two agents, to influence the transmission of disease between states.

The file interactions_list.txt contains all the interactions between the agents.

The interactions are unidirectional, hence we have to specify two interactions to depict bidirectional contact between any two agents.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.1
	return 0

def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.6
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Exposed':1},probability_of_infection_fn,[0.3,0.1]),False,0.95)
		self.insert_state('Exposed',5,2,self.scheduled({'Symptomatic':0.3,'Asymptomatic':0.7}),False,0.02)
		self.insert_state('Symptomatic',11,5,self.scheduled({'Recovered':1}),True,0.02)
		self.insert_state('Asymptomatic',6,3,self.scheduled({'Recovered':1}),True,0.01)
		self.insert_state('Recovered',100, 0,self.scheduled({'Recovered':1}),False,0)

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interactions_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

import random
import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_interactions(filename,no_agents,p):
	info_dict={}
	#Agent ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index'
	lines=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				lines.append(str(i)+':'+str(j)+'\n')
				lines.append(str(j)+':'+str(i)+'\n')

	f=open(filename,'w')
	f.write(str(len(lines))+'\n')
	f.write(header+'\n')

	for line in lines:
		f.write(line)

number_of_agents=int(sys.argv[1])
p=float(sys.argv[2])
write_agents('agents.txt',number_of_agents)
write_interactions('interactions_list.txt',number_of_agents,p)

interactions_files_list.txt

<interactions_list.txt>

interactions_list.txt

1006
Agent Index:Interacting Agent Index
1:470
470:1
2:968
968:2
5:802
802:5
6:95
95:6
6:996
996:6
8:373
373:8
8:428
428:8
8:676
676:8
9:895
895:9
10:43
43:10
10:108
108:10
13:86
86:13
14:921
921:14
15:584
584:15
18:324
324:18
18:888
888:18
21:583
583:21
21:850
850:21
21:957
957:21
24:86
86:24
28:151
151:28
28:438
438:28
28:654
654:28
29:315
315:29
29:705
705:29
29:757
757:29
30:242
242:30
31:160
160:31
31:647
647:31
31:884
884:31
32:254
254:32
32:910
910:32
34:757
757:34
35:934
934:35
36:424
424:36
40:296
296:40
40:518
518:40
40:571
571:40
41:192
192:41
41:236
236:41
42:737
737:42
44:984
984:44
45:679
679:45
47:636
636:47
48:678
678:48
50:398
398:50
50:576
576:50
50:742
742:50
52:508
508:52
54:916
916:54
56:801
801:56
58:128
128:58
58:831
831:58
59:623
623:59
59:947
947:59
61:602
602:61
64:167
167:64
64:341
341:64
68:491
491:68
73:338
338:73
74:437
437:74
74:907
907:74
76:127
127:76
77:275
275:77
79:825
825:79
79:915
915:79
80:297
297:80
80:529
529:80
81:517
517:81
82:554
554:82
83:279
279:83
83:586
586:83
83:624
624:83
85:89
89:85
86:819
819:86
87:905
905:87
88:394
394:88
89:230
230:89
90:778
778:90
91:462
462:91
93:229
229:93
93:497
497:93
93:544
544:93
94:669
669:94
94:781
781:94
94:852
852:94
97:161
161:97
97:232
232:97
98:301
301:98
102:223
223:102
102:414
414:102
102:885
885:102
102:969
969:102
103:600
600:103
105:427
427:105
105:784
784:105
109:120
120:109
110:297
297:110
110:480
480:110

locations.txt

1
Location Index
0

Stochastic_SIR_complete_graph

This example is a variation of the Stochastic SIR model. Instead of having the disease spread in O(n) time using the concept of ambient infections as seen in Stochastic_SIR, it spreads between each pair of agents in O(n^2) time using interactions.

This is why Events as a disease spread module is very important in Episimmer. It is a very efficient way of modelling interactions between the agents where we have no information on single agent-agent interactions.

Here, each agent interacts with every other agent bidirectionally and all of these interactions are listed in the complete_interactions_list.txt file.

It considers the agents to be vertices of a complete graph with each edge being an interaction between the two connecting agent vertices.

Episimmer uses this example to give a different perspective of disease flow and can be used where all the considered agents are part of an event.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


#This function represents the probability of getting infected during a single interaction/contact
def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.001  #This is the probability of getting infected from contact in a time step isf contact is infected
	return 0 # If contact is not infected then the probability of them infecting you is 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))	#Adding S-> I transition which is based on probability)fInfection_fn
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))



		self.name='Stochastic SIR on complete graph'

agents.txt

30
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

complete_interactions_list.txt

870
Agent Index:Interacting Agent Index
0:1
0:2
0:3
0:4
0:5
0:6
0:7
0:8
0:9
0:10
0:11
0:12
0:13
0:14
0:15
0:16
0:17
0:18
0:19
0:20
0:21
0:22
0:23
0:24
0:25
0:26
0:27
0:28
0:29
1:0
1:2
1:3
1:4
1:5
1:6
1:7
1:8
1:9
1:10
1:11
1:12
1:13
1:14
1:15
1:16
1:17
1:18
1:19
1:20
1:21
1:22
1:23
1:24
1:25
1:26
1:27
1:28
1:29
2:0
2:1
2:3
2:4
2:5
2:6
2:7
2:8
2:9
2:10
2:11
2:12
2:13
2:14
2:15
2:16
2:17
2:18
2:19
2:20
2:21
2:22
2:23
2:24
2:25
2:26
2:27
2:28
2:29
3:0
3:1
3:2
3:4
3:5
3:6
3:7
3:8
3:9
3:10
3:11
3:12
3:13
3:14
3:15
3:16
3:17
3:18
3:19
3:20
3:21
3:22
3:23
3:24
3:25
3:26
3:27
3:28
3:29
4:0
4:1
4:2
4:3
4:5
4:6
4:7
4:8
4:9
4:10
4:11
4:12
4:13
4:14
4:15
4:16
4:17
4:18
4:19
4:20
4:21
4:22
4:23
4:24
4:25
4:26
4:27
4:28
4:29
5:0
5:1
5:2
5:3
5:4
5:6
5:7
5:8
5:9
5:10
5:11
5:12
5:13
5:14
5:15
5:16
5:17
5:18
5:19
5:20
5:21
5:22
5:23
5:24
5:25
5:26
5:27
5:28
5:29
6:0
6:1
6:2
6:3
6:4
6:5
6:7
6:8
6:9
6:10
6:11
6:12
6:13
6:14
6:15
6:16
6:17
6:18
6:19
6:20
6:21
6:22
6:23
6:24

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_interactions(filename,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	no_interactions=no_agents*(no_agents-1)
	header='Agent Index:Interacting Agent Index'

	f=open(filename,'w')
	f.write(str(no_interactions)+'\n')
	f.write(header+'\n')

	for i in range(no_agents):
		for j in range(no_agents):
			if i!=j:
				f.write(str(i)+':'+str(j)+'\n')

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_interactions('complete_interactions_list.txt',number_of_agents)

interaction_files_list.txt

<complete_interactions_list.txt>

locations.txt

1
Location Index
0

Stochastic_SIR_random_graph

This example is a variation of the Stochastic_SIR_complete_graph model.

Instead of all the agents participating in the interactions, only a set of agents interact with each other bidirectionally and all of these interactions are listed in the interactions_list.txt file.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


#This function represents the probability of getting infected during a single interaction/contact
def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.1  #This is the probability of getting infected from contact in a time step isf contact is infected
	return 0 # If contact is not infected then the probability of them infecting you is 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.97,
							'Infected':0.03,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))	#Adding S-> I transition which is based on probability_of_infection_fn
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))

		self.name='Stochastic SIR on complete graph'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

agents.txt

100
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

# This code require two inputs, 'number of agents' and 'probability of edge'
# To run <python generate_files.py 100 0.1>

import random
import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_interactions(filename,no_agents,p):
	info_dict={}
	#Agent ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index'
	lines=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				lines.append(str(i)+':'+str(j)+'\n')
				lines.append(str(j)+':'+str(i)+'\n')

	f=open(filename,'w')
	f.write(str(len(lines))+'\n')
	f.write(header+'\n')

	for line in lines:
		f.write(line)

number_of_agents=int(sys.argv[1])
p=float(sys.argv[2])
write_agents('agents.txt',number_of_agents)
write_interactions('interactions_list.txt',number_of_agents,p)

generate_files_csv.py

import csv
import random
import sys


def write_agents(filename,no_agents):
	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)

	    writer.writeheader()
	    for i in range(no_agents):
	    	writer.writerow({'Agent Index': i})


def write_interactions(filename,no_agents,p):
	agent_list=[]
	interacting_agent_list=[]
	for i in range(no_agents-1):
		for j in range(i+1,no_agents):
			if random.random()<p:
				agent_list.append(i)
				agent_list.append(j)
				interacting_agent_list.append(j)
				interacting_agent_list.append(i)

	with open(filename, 'w', newline='') as file:
	    fieldnames = ['Agent Index','Interacting Agent Index']
	    writer = csv.DictWriter(file, fieldnames=fieldnames)
	    writer.writeheader()
	    for i in range(len(agent_list)):
	        writer.writerow({'Agent Index':agent_list[i],'Interacting Agent Index':interacting_agent_list[i]})


n = int(sys.argv[1])
prob = float(sys.argv[2])

write_agents('agents.csv',n)
write_interactions('interactions_list.csv', n, prob)

interaction_files_list.txt

<interactions_list.csv>

interactions_list.csv

Agent Index,Interacting Agent Index
0,23
23,0
0,39
39,0
0,41
41,0
0,44
44,0
0,46
46,0
0,61
61,0
0,67
67,0
0,80
80,0
1,6
6,1
1,14
14,1
1,16
16,1
1,23
23,1
1,27
27,1
1,33
33,1
1,52
52,1
1,57
57,1
1,61
61,1
1,62
62,1
1,67
67,1
1,73
73,1
1,86
86,1
1,89
89,1
2,7
7,2
2,14
14,2
2,49
49,2
2,53
53,2
2,61
61,2
2,68
68,2
2,69
69,2
2,70
70,2
2,73
73,2
2,76
76,2
2,77
77,2
2,95
95,2
3,17
17,3
3,50
50,3
3,63
63,3
3,64
64,3
3,79
79,3
3,88
88,3
3,94
94,3
3,95
95,3
4,14
14,4
4,24
24,4
4,57
57,4
4,80
80,4
4,89
89,4
4,94
94,4
5,10
10,5
5,15
15,5
5,27
27,5
5,52
52,5
6,8
8,6
6,14
14,6
6,16
16,6
6,30
30,6
6,39
39,6
6,41
41,6
6,47
47,6
6,51
51,6
6,58
58,6
6,72
72,6
6,75
75,6
6,86
86,6
6,94
94,6
7,9
9,7
7,13
13,7
7,15
15,7
7,19
19,7
7,22
22,7
7,26
26,7
7,43
43,7
7,44
44,7
7,49
49,7
7,68
68,7
7,69
69,7
7,73
73,7
7,78
78,7
8,11
11,8
8,21
21,8
8,28
28,8
8,39
39,8
8,44
44,8
8,47
47,8
8,49
49,8
8,58
58,8
8,59
59,8
8,60
60,8
8,66
66,8
8,74
74,8
8,86
86,8
8,99
99,8
9,18
18,9
9,25
25,9
9,28
28,9
9,29
29,9
9,30
30,9
9,39
39,9
9,40
40,9
9,49

interactions_list.txt

90
Agent Index:Interacting Agent Index
1:15
15:1
4:47
47:4
4:74
74:4
4:96
96:4
7:90
90:7
9:78
78:9
13:85
85:13
14:41
41:14
15:38
38:15
17:37
37:17
17:95
95:17
19:92
92:19
20:83
83:20
22:28
28:22
22:61
61:22
25:45
45:25
25:66
66:25
25:84
84:25
26:50
50:26
26:92
92:26
27:39
39:27
33:36
36:33
33:98
98:33
34:56
56:34
34:63
63:34
34:78
78:34
35:44
44:35
35:48
48:35
36:62
62:36
41:80
80:41
42:97
97:42
43:67
67:43
44:53
53:44
44:65
65:44
49:80
80:49
50:64
64:50
51:91
91:51
56:97
97:56
58:67
67:58
59:76
76:59
60:95
95:60
68:82
82:68
75:77
77:75
76:82
82:76
89:99
99:89

locations.txt

1
Location Index
0

Stochastic_SIR_two_locations

This example uses the Stochastic SIR model to show infection spread using events at two locations at the same time step.

The different locations may have multiple events taking place in them simultaneously.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<two_events.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,start_agent_index, end_agent_index):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(2)+'\n')
	f.write(header+'\n')

	line=str(0)+':'	#location index 0
	for i in range(start_agent_index,int(end_agent_index/2)):
		line+=str(i)
		if i!=int(end_agent_index/2)-1:
			line+=','
	f.write(line+'\n')
	line=str(1)+':'	#location index 1
	for i in range(int(end_agent_index/2),end_agent_index):
		line+=str(i)
		if i!=end_agent_index-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('two_events.txt',0,number_of_agents)

locations.txt

2
Location Index
0
1

two_events.txt

2
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499
1:500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Two_events_alternating_one_location

This example involves alternating events in a single location.

It is one of the different variations of real-life scenarios involving events and locations where these events occur.

Episimmer uses this example to highlight the cyclicity of events and how they are cyclicly called in some particular order. This is demonstrated by introducing two events in the event_files_list.txt file.

Such examples can be used to simulate university scenarios where there are for instance two classes being held in the same lecture hall on alternate days.

The user has the liberty decide whether these events occur in the same location or not.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<first_event.txt>
<second_event.txt>

first_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,start_agent_index, end_agent_index):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'
	no_agents=end_agent_index-start_agent_index

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(start_agent_index,end_agent_index):
		line+=str(i)
		if i!=end_agent_index-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('first_event.txt',0,int(number_of_agents/2))
write_events('second_event.txt',int(number_of_agents/2),number_of_agents)

locations.txt

1
Location Index
0

second_event.txt

1
Location Index:Agents
0:500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Two_events_alternating_with_intersection

This example is a variation of the model Stochastic_SIR_two_events_alternating. Instead of having two alternating events in which neither of the two events have common agents with the other, we are introducing a model in which the alternating events may have a few agents in common to both of them.

This example has been introduced to deal with situations where an infected agent may belong to more than one event.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.1))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <100>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<first_event.txt>
<second_event.txt>

first_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,start_agent_index, end_agent_index):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'
	no_agents=end_agent_index-start_agent_index

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(start_agent_index,end_agent_index):
		line+=str(i)
		if i!=end_agent_index-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('first_event.txt',0,int(number_of_agents*0.505))
write_events('second_event.txt',int(number_of_agents*0.495),number_of_agents)

locations.txt

1
Location Index
0

second_event.txt

1
Location Index:Agents
0:495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Two_independent_events_in_two_locations

This example helps in visualizing disease spread through two events having no common agents in two locations.

It is another variation of multiple events occurring in more than one locations.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<two_events.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,start_agent_index, end_agent_index):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(2)+'\n')
	f.write(header+'\n')

	line=str(0)+':'	#location index 0
	for i in range(start_agent_index,int(end_agent_index/2)):
		line+=str(i)
		if i!=int(end_agent_index/2)-1:
			line+=','
	f.write(line+'\n')
	line=str(1)+':'	#location index 1
	for i in range(int(end_agent_index/2),end_agent_index):
		line+=str(i)
		if i!=end_agent_index-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('two_events.txt',0,number_of_agents)

locations.txt

2
Location Index
0
1

two_events.txt

2
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499
1:500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Miscellaneous

The examples under this directory showcase additional functionality of Episimmer like External Prevalence, List of Lists and Custom Distributions for Scheduled disease models.

We also include examples that showcase the level of complexity and creativity that the user can harness with Episimmer.

To run an example :

            cd examples/Miscellaneous
            python ../../episimmer/main.py 3D_Cellular_Automaton

---

3D_Cellular_Automaton

This example is built over the Scheduled SEYAR model.

This is a creative example of modelling interactions between the agents as a cellular automaton. For a nice visualization of the interactions, use the view_interaction_graph python file in the Scripts/Visualization directory.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list, contact_agent, c_dict, current_time_step):
	if contact_agent.state in ['Exposed', 'Symptomatic', 'Asymptomatic']:
		return 0.5
	return 0

class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible', None, None, self.p_infection({'Exposed':1},probability_of_infection_fn,[0.3, 0.1]),False,0.90)
		self.insert_state('Exposed', 5, 2, self.scheduled({'Symptomatic':0.3,'Asymptomatic':0.7}),False,0.02)
		self.insert_state('Symptomatic', 30, 5, self.scheduled({'Recovered':1}),True,0.03)
		self.insert_state('Asymptomatic', 25, 3, self.scheduled({'Recovered':1}),True,0.05)
		self.insert_state('Recovered', 100, 0, self.scheduled({'Recovered': 1}),False,0)

agents.txt

800
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <>
Number of worlds <2>
Number of Days <60>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interactions_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

import sys


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(1, n + 1):
		f.write(str(i-1)+'\n')

def write_interactions(filename, n, k):
	header = 'Agent Index:Interacting Agent Index'

	f = open(filename, 'w')
	# k*n*(n - 1)
	f.write(str(2*(k*2*n*(n - 1) + n*n*(k-1)))+'\n')
	f.write(header+'\n')

	for l in range(k):
		for i in range(n*n):
			if l != k-1:
				f.write(str(i+l*n*n) + ':' + str(i +(l+1)*n*n) + '\n')
				f.write(str(i+(l+1)*n*n) + ':' + str(i +l*n*n) + '\n')

			if i<n*n-n:
				f.write(str(i+l*n*n) + ':' + str(i+l*n*n+n) + '\n')
				f.write(str(i+l*n*n+n) + ':' + str(i+l*n*n) + '\n')

			if (i+1) % n != 0:
				f.write(str(i+l*n*n) + ':' + str(i+l*n*n+1) + '\n')
				f.write(str(i+l*n*n+1) + ':' + str(i+l*n*n) + '\n')

	f.close()

# n x n x k agents
n = int(sys.argv[1])
k = int(sys.argv[2])

no_of_agents = k*n*n
write_agents('agents.txt', no_of_agents)
write_interactions('interactions.txt', n, k)

interactions.txt

3840
Agent Index:Interacting Agent Index
0:400
400:0
0:20
20:0
0:1
1:0
1:401
401:1
1:21
21:1
1:2
2:1
2:402
402:2
2:22
22:2
2:3
3:2
3:403
403:3
3:23
23:3
3:4
4:3
4:404
404:4
4:24
24:4
4:5
5:4
5:405
405:5
5:25
25:5
5:6
6:5
6:406
406:6
6:26
26:6
6:7
7:6
7:407
407:7
7:27
27:7
7:8
8:7
8:408
408:8
8:28
28:8
8:9
9:8
9:409
409:9
9:29
29:9
9:10
10:9
10:410
410:10
10:30
30:10
10:11
11:10
11:411
411:11
11:31
31:11
11:12
12:11
12:412
412:12
12:32
32:12
12:13
13:12
13:413
413:13
13:33
33:13
13:14
14:13
14:414
414:14
14:34
34:14
14:15
15:14
15:415
415:15
15:35
35:15
15:16
16:15
16:416
416:16
16:36
36:16
16:17
17:16
17:417
417:17
17:37
37:17
17:18
18:17
18:418
418:18
18:38
38:18
18:19
19:18
19:419
419:19
19:39
39:19
20:420
420:20
20:40
40:20
20:21
21:20
21:421
421:21
21:41
41:21
21:22
22:21
22:422
422:22
22:42
42:22
22:23
23:22
23:423
423:23
23:43
43:23
23:24
24:23
24:424
424:24
24:44
44:24
24:25
25:24
25:425
425:25
25:45
45:25
25:26
26:25
26:426
426:26
26:46
46:26
26:27
27:26
27:427
427:27
27:47
47:27
27:28
28:27
28:428
428:28
28:48
48:28
28:29
29:28
29:429
429:29
29:49
49:29
29:30
30:29
30:430
430:30
30:50
50:30
30:31
31:30
31:431
431:31
31:51
51:31
31:32
32:31
32:432
432:32
32:52
52:32
32:33
33:32
33:433
433:33

interactions_files_list.txt

<interactions.txt>

locations.txt

1
Location Index
0

Complex Stochastic Model

This is an example of how creative a user can get with respect to the disease model he/she would want to use. As you can see from the UserModel.py despite the presence of many states, Episimmer has the ability to build complex models without any effort.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


class UserModel(model.StochasticModel):
	def __init__(self):

		beta=0.05
		delta=0.05
		sigma=0.3
		beta1=0.3
		k0=0.2
		mu=0.2
		kt=0.3
		kt1=0.3
		kt2=0.3
		gamma1=0.3
		gamma2=0.3
		gamma3=0.3

		individual_types=['S','A','I','P','R','E','XS','XA','XI','XE']
		infected_states=[]
		state_proportion={
							'S':1
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('S','A',self.p_standard(beta))
		self.set_transition('A','I',self.p_standard(sigma))
		self.set_transition('XS','XA',self.p_standard(beta1*beta))
		self.set_transition('XA','XI',self.p_standard(sigma))
		self.set_transition('S','XS',self.p_standard(k0))
		self.set_transition('XS','S',self.p_standard(mu))
		self.set_transition('A','XA',self.p_standard(k0))
		self.set_transition('XA','A',self.p_standard(mu))
		self.set_transition('E','XE',self.p_standard(k0))
		self.set_transition('XE','E',self.p_standard(mu))
		self.set_transition('I','XI',self.p_standard(k0))
		self.set_transition('XI','I',self.p_standard(mu))
		self.set_transition('S','E',self.p_standard(delta))
		self.set_transition('XS','XE',self.p_standard(beta1*delta))
		self.set_transition('I','P',self.p_standard(kt))
		self.set_transition('XI','P',self.p_standard(kt))
		self.set_transition('A','P',self.p_standard(kt2))
		self.set_transition('XA','P',self.p_standard(kt2))
		self.set_transition('E','P',self.p_standard(kt1))
		self.set_transition('XE','P',self.p_standard(kt1))
		self.set_transition('P','R',self.p_standard(gamma3))
		self.set_transition('I','R',self.p_standard(gamma2))
		self.set_transition('XI','R',self.p_standard(gamma2))
		self.set_transition('E','R',self.p_standard(gamma1))
		self.set_transition('XE','R',self.p_standard(gamma1))


		self.name='Probablistic Lockdown Model'

agents.txt

1300
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <>
One Time Event filename <>

locations.txt

1
Location Index
0

List_Of_Lists

Episimmer can handle a list of File lists in the config file and run them concurrently. This is to provide the ability to simulate timetable-like simulation environments. For example, Student Timetables for each class are different but the classes are conducted at the same time.

Thus, Events, Interactions and Probabilistic Interactions can pass multiple file_lists in the config file as shown in this example.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import math

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):

	p_inf_symp,p_inf_asymp=p_infected_states_list[0],p_infected_states_list[1]
	#EXAMPLE 1
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval']))*p_inf_asymp
	else:
		return 0

	#Example 2
	if contact_agent.state=='Symptomatic':
		return math.tanh(float(c_dict['Time Interval'])*float(c_dict['Intensity']))*p_inf_symp
	elif contact_agent.state=='Asymptomatic':
		return math.tanh(float(c_dict['Time Interval'])*float(c_dict['Intensity']))*p_inf_asymp
	else:
		return 0

def event_contribute_fn(agent,event_info,location,current_time_step):
	#Example 1
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.3
	else:
		return 0

	#Example 2
	susceptibility=1
	if agent.info['HLA Type']=='A':
		susceptibility=0.9

	if agent.state=='Symptomatic':
		return math.tanh(float(event_info['Time Interval']))*(1-location.info['Ventilation'])*susceptibility
	elif agent.state=='Asymptomatic':
		return 0.3*math.tanh(float(event_info['Time Interval']))*(1-location.info['Ventilation'])*susceptibility

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	p=math.tanh(ambient_infection*0.3)
	return p




class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0.01,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection(probability_of_infection_fn,[0.3,0.1]))
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.2))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.2))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.csv

Agent Index,Type,Residence,HLA Type
0,Staff,Dorm B,C
1,Student,Teacher Dorm,C
2,Visitor,Dorm B,A
3,Student,Teacher Dorm,C
4,Visitor,Dorm A,A
5,Administration,Dorm A,B
6,Teacher,Teacher Dorm,C
7,Teacher,Teacher Dorm,C
8,Visitor,Dorm B,C
9,Visitor,Dorm B,C
10,Visitor,Outside,C
11,Administration,Dorm A,C
12,Student,Outside,A
13,Visitor,Outside,B
14,Administration,Dorm A,B
15,Student,Outside,C
16,Student,Teacher Dorm,A
17,Student,Dorm A,C
18,Teacher,Teacher Dorm,A
19,Visitor,Dorm A,B
20,Student,Dorm A,A
21,Administration,Outside,C
22,Visitor,Dorm B,A
23,Staff,Dorm A,B
24,Visitor,Dorm B,A
25,Staff,Teacher Dorm,C
26,Teacher,Teacher Dorm,B
27,Student,Teacher Dorm,A
28,Teacher,Outside,C
29,Administration,Outside,A
30,Administration,Dorm B,A
31,Teacher,Dorm B,A
32,Teacher,Outside,A
33,Teacher,Dorm A,C
34,Staff,Outside,B
35,Student,Dorm B,C
36,Visitor,Outside,C
37,Staff,Dorm A,C
38,Staff,Teacher Dorm,B
39,Student,Dorm A,C
40,Administration,Outside,C
41,Teacher,Teacher Dorm,A
42,Visitor,Dorm A,C
43,Administration,Teacher Dorm,C
44,Administration,Dorm A,B
45,Administration,Dorm B,C
46,Student,Dorm A,B
47,Visitor,Dorm B,B
48,Staff,Dorm A,A
49,Student,Outside,B
50,Visitor,Dorm B,C
51,Student,Teacher Dorm,B
52,Teacher,Teacher Dorm,B
53,Student,Outside,A
54,Administration,Dorm A,A
55,Teacher,Outside,C
56,Teacher,Dorm A,A
57,Visitor,Dorm A,A
58,Visitor,Dorm A,B
59,Staff,Dorm A,A
60,Staff,Dorm A,A
61,Teacher,Teacher Dorm,B
62,Teacher,Dorm B,C
63,Staff,Dorm A,B
64,Visitor,Dorm B,C
65,Staff,Outside,C
66,Administration,Dorm A,B
67,Administration,Outside,A
68,Teacher,Teacher Dorm,B
69,Student,Outside,A
70,Staff,Dorm B,C
71,Visitor,Outside,A
72,Staff,Dorm B,A
73,Student,Outside,B
74,Administration,Outside,C
75,Teacher,Dorm B,A
76,Administration,Outside,A
77,Teacher,Teacher Dorm,C
78,Teacher,Teacher Dorm,B
79,Staff,Outside,A
80,Student,Dorm A,C
81,Visitor,Outside,B
82,Administration,Outside,C
83,Teacher,Dorm B,C
84,Staff,Teacher Dorm,C
85,Student,Dorm B,B
86,Teacher,Dorm B,C
87,Teacher,Dorm A,A
88,Staff,Teacher Dorm,B
89,Visitor,Dorm B,C
90,Administration,Teacher Dorm,B
91,Visitor,Dorm B,C
92,Student,Teacher Dorm,C
93,Visitor,Teacher Dorm,B
94,Administration,Outside,B
95,Administration,Teacher Dorm,C
96,Administration,Dorm B,A
97,Staff,Dorm B,C
98,Visitor,Teacher Dorm,B
99,Teacher,Dorm B,B

agents.txt

100
Agent Index:Type:Residence:HLA Type
0:Visitor:Outside:B
1:Visitor:Teacher Dorm:C
2:Visitor:Outside:C
3:Staff:Teacher Dorm:A
4:Student:Dorm A:A
5:Visitor:Dorm A:B
6:Administration:Teacher Dorm:C
7:Staff:Teacher Dorm:C
8:Staff:Outside:A
9:Teacher:Dorm B:B
10:Visitor:Teacher Dorm:C
11:Administration:Outside:B
12:Visitor:Outside:C
13:Staff:Outside:B
14:Staff:Dorm A:B
15:Visitor:Teacher Dorm:B
16:Staff:Outside:A
17:Staff:Dorm A:B
18:Teacher:Dorm B:C
19:Staff:Dorm A:A
20:Visitor:Outside:B
21:Student:Teacher Dorm:B
22:Teacher:Teacher Dorm:C
23:Staff:Dorm A:C
24:Visitor:Dorm B:B
25:Visitor:Outside:C
26:Visitor:Outside:B
27:Staff:Outside:C
28:Teacher:Dorm A:C
29:Student:Dorm A:B
30:Staff:Dorm A:A
31:Student:Teacher Dorm:C
32:Administration:Teacher Dorm:A
33:Teacher:Dorm A:A
34:Administration:Dorm A:C
35:Teacher:Dorm B:B
36:Student:Dorm A:A
37:Administration:Dorm A:A
38:Teacher:Outside:B
39:Teacher:Dorm B:B
40:Administration:Teacher Dorm:A
41:Staff:Teacher Dorm:A
42:Teacher:Outside:B
43:Administration:Teacher Dorm:B
44:Teacher:Outside:C
45:Visitor:Outside:A
46:Visitor:Teacher Dorm:A
47:Administration:Teacher Dorm:A
48:Staff:Dorm A:A
49:Student:Outside:B
50:Staff:Teacher Dorm:B
51:Administration:Outside:C
52:Student:Dorm B:C
53:Administration:Dorm A:C
54:Administration:Outside:A
55:Administration:Outside:C
56:Administration:Dorm A:C
57:Visitor:Outside:B
58:Teacher:Outside:A
59:Staff:Outside:A
60:Administration:Teacher Dorm:A
61:Student:Teacher Dorm:A
62:Student:Dorm B:A
63:Teacher:Dorm A:C
64:Staff:Dorm A:A
65:Student:Teacher Dorm:C
66:Teacher:Outside:C
67:Staff:Dorm B:B
68:Administration:Teacher Dorm:B
69:Teacher:Teacher Dorm:C
70:Administration:Teacher Dorm:C
71:Visitor:Outside:B
72:Visitor:Teacher Dorm:C
73:Visitor:Teacher Dorm:C
74:Administration:Dorm A:C
75:Student:Outside:C
76:Visitor:Outside:C
77:Student:Teacher Dorm:A
78:Teacher:Dorm A:B
79:Visitor:Dorm B:C
80:Administration:Outside:C
81:Student:Dorm B:A
82:Visitor:Dorm B:B
83:Administration:Outside:A
84:Student:Teacher Dorm:A
85:Student:Dorm A:C
86:Staff:Dorm B:A
87:Administration:Teacher Dorm:B
88:Administration:Dorm A:A
89:Teacher:Dorm A:C
90:Administration:Dorm A:B
91:Student:Outside:C
92:Student:Dorm A:B
93:Teacher:Teacher Dorm:C
94:Student:Outside:C
95:Administration:Teacher Dorm:C
96:Staff:Outside:B
97:Staff:Dorm A:C
98:Teacher:Outside:A
99:Administration:Dorm A:B

config.txt

Random Seed <3>
Number of worlds <3>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type:Residence:HLA Type>
Agent list filename <agents.csv>
Interaction Info Keys <Agent Index:Interacting Agent Index:Time Interval:Intensity>
Interaction Files list filename <interaction_files_list.txt,interaction_files_list2.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index:Type:Ventilation:Roomsize:Capacity>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents:Time Interval>
Event Files list filename <event_files_list.txt,event_files_list2.txt>
One Time Event filename <>

even_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
67,66,9.857117310137038,0.5920888806387263
39,40,2.9606587904019332,0.28799857726311495
19,48,9.933376750841031,0.5563869797724015
47,14,2.707792838677613,0.11910322049310873
97,93,7.330200583028975,0.05479876188162769
36,52,5.8053943720215395,0.5087501005692888
16,29,2.9259700624959297,0.7450077742936179
24,76,6.615452592843672,0.23066023231585764
98,1,0.6129732778252273,0.03079626452546913
52,51,4.991234253861199,0.11029211445761467
49,64,4.281211334785262,0.09369780403627248
76,15,3.110100778578777,0.16882508544803732
4,82,5.018950780821032,0.4528425446162895
25,23,1.523435869691594,0.8698275706188255
69,26,5.982815427997005,0.8195210582557968
6,69,6.002756213947941,0.47565948855915063
51,92,0.9693831534702857,0.8545362381598459
4,54,8.926690297564479,0.19650516555917197
96,37,1.0761711632578574,0.2856646294271543
56,11,6.206036904245246,0.025636240561556556
63,45,9.243970250499935,0.8459046432706034
18,80,7.21308647170134,0.18639074663509203
22,25,8.566766221711488,0.9194158046579632
46,78,9.436676144535742,0.3066038278097547
55,33,0.36786224695660175,0.46598870340260434
80,31,4.295593090845587,0.7887243574197714
74,96,9.079412546074366,0.2008470536485456
93,11,1.811367363309666,0.24854787128323197
17,45,1.3031037142786772,0.736713001093112
54,77,6.689236468914333,0.18914544406995604
48,55,6.561686523837922,0.8415382745176084
95,24,2.1199045466842223,0.3191748832651885
0,43,6.361037571365211,0.054785174673453896
0,86,0.05530006267194132,0.44722966677561227
67,8,5.108232528265227,0.0780720907027247
6,57,9.073223985113284,0.6305218263588744
16,8,6.875884563883529,0.7889755826254277
35,48,3.63455049866231,0.23653026160724466
93,72,2.726782980498262,0.7473684674491506
43,65,0.5285308238378339,0.8079994482515851
30,84,5.435250398044084,0.22794249938945577
68,66,5.812705194521447,0.3987768568223644
37,30,5.462713140719986,0.2651939540942704
17,49,9.554281145866424,0.2854383038592877
3,1,7.129363264331846,0.6332198675617925
34,66,3.7040828036302886,0.15835840507645926
39,53,8.70713280127657,0.7521303893855847
85,42,2.9065301405226784,0.4061131567948121
4,71,7.566520295016451,0.4702871038447024
25,72,3.3524932671715546,0.991241688877458

even_events.txt

10
Location Index:Agents:Time Interval
1:63,91,41,97,7,44,73,88,6,36,22:45
5:70,76,47:60
1:50,91,48,78:10
9:60,55,98,50,0,11,22,13,92,54,33,84,14:60
5:7,76,83,54,90,94,79,2,75,34,7,96,50,10,23,54,94,15,41,87,6:30
2:44,90,20,90,40,99,25,95,5,3,31,31,50,31,4,48,40,80,6:10
1:99,53,83,90,63,45,12,82,28,96,68:10
2:61,99,34,2,53,88,77,16,90,28,43,63,62,88,42:30
3:93,29,68,43,29,0,67,49,9,92:10
6:91,31,55,9,57,71,39,25,76,76,17,6,18,34,27,4,75,4,62,29,6:60

event_files_list.txt

<monday_events.txt>
<tuesday_events.txt>
<wednesday_events.txt>
<thursday_events.txt>
<friday_events.txt>
<saturday_events.txt>
<sunday_events.txt>

event_files_list2.txt

<odd_events.txt>
<even_events.txt>

friday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
96,57,5.004142877127,0.42191921620925354
54,27,5.641672551963007,0.7462919078948316
25,8,1.794290991869778,0.8194479950290448
48,12,0.22317783046829742,0.5930024147218874
32,1,2.0332990080122935,0.9049495038090604
79,4,5.349489905495666,0.25834688605446654
16,19,7.482148590293117,0.8597260125619213
1,84,1.643656099054901,0.6492400176694979
91,92,7.539420185381803,0.1572574957436378
0,18,0.412793980070838,0.12359152155948494
27,4,2.1036845816262373,0.7556071327228134
44,93,0.5338106410878762,0.3250995579281485
7,85,2.700768270414009,0.6396363196460858
25,54,7.851908992747192,0.03235332764508547
86,18,4.264343699418963,0.03294294008400289
47,49,2.2001671472930973,0.8547969093028639
88,57,9.21067656780389,0.9149669418045047
91,60,5.954707715564772,0.5719792018299477
70,13,8.590870958001473,0.3467306495161502
39,25,3.0242832181735766,0.4138264196643887
31,32,4.446511361608556,0.3307555630717903
72,16,7.152478159718685,0.4051997279355414
51,56,5.126758363544265,0.03510071951222604
57,69,1.8038913841447546,0.6695529982166318
50,34,6.781050924626727,0.00925049088045582
9,57,2.0483981637076076,0.8150216743517682
2,46,1.9695147002286972,0.8236731954676536
76,10,3.2367032040028865,0.189359530938495
93,40,3.5290802325675985,0.5770031224285761
13,51,8.366286124001043,0.7531077788790762
3,43,4.667816904834296,0.5663977961774596
28,87,4.033038804641689,0.523226854909037
95,61,4.099665005428983,0.382169865819883
11,58,5.760839569829173,0.5331949513335849
18,26,0.09758072728262124,0.7763890421115288
13,8,3.3584379922185317,0.1863867865578278
60,60,7.177857666160996,0.10434104385849963
65,96,1.1312776613884845,0.9674134933532024
6,33,5.839416802331847,0.378146491269558
48,43,7.271644108335251,0.8995373336774743
84,81,0.43165651762144375,0.3493858177036441
17,78,4.34280471192559,0.2029239277672824
58,12,3.017355257636595,0.08158660374123372
23,76,2.964865343386526,0.0690704096811312
48,35,5.776276020339678,0.893577319760597
73,2,1.7577645470923775,0.06759772321371171
37,96,1.933497529060112,0.38664529916662904
55,16,9.506699244782125,0.5224209433841157
21,89,3.9832543315545896,0.21727916670097103
30,43,9.439600407370815,0.4025782968889513
4,94,4.0558134618401365,0.4412080905758925
83,87,0.5577932147347009,0.46357822403968507
64,17,5.698770885091612,0.3799729422257563
82,6,7.314244632540017,0.9379001142886053
51,47,4.972927027027878,0.7498405462605637
46,21,3.7018950508852324,0.5939738834476441
19,24,9.129329003202235,0.3757168235721946
78,68,9.318254737275796,0.30123710713292373
65,48,2.7578489748122936,0.7651692440439245
85,96,4.750884998198361,0.30555530537592257
44,59,3.7588664444315434,0.029012995900403737
42,53,7.965520525420933,0.500322500665823
34,89,8.137472839811998,0.10683445931853242
31,74,7.154376619192167,0.020288324506276934
14,87,5.458581884838476,0.4920008942367613
80,69,8.248995001589027,0.23526849665722827
50,21,2.5306642194391116,0.3794406316118146
61,45,5.298710794959918,0.37907685807994806
85,24,8.321777043104493,0.536579378508093
62,76,7.7095256085339345,0.3829852909795268
97,68,6.720884566803925,0.3390071114792286
60,77,4.533234610599038,0.8113433047634149
26,10,8.06790434062078,0.30299925326446464
47,43,8.69852562934309,0.7895856290622782
78,27,3.6823345284575018,0.6433166905984297
82,95,2.9028972238732917,0.5743434116842108
48,50,1.2885001947678354,0.5845643239525443
24,92,5.10702514748865,0.8295933418361403
4,70,3.697077445626795,0.7706536847343546
19,48,9.705052235091756,0.270603140864078
11,35,1.6398273857230894,0.5619636810964064
64,82,8.60868600219238,0.5136674086504184
24,73,4.747910218034176,0.9698540725625405
77,39,9.537053731100741,0.3184912393594964
26,65,6.623026331783141,0.12464013667280971
18,6,3.118879056544123,0.1687671012080162
25,88,1.661248992411558,0.0027643427331558446
30,16,1.7496507474499778,0.7533736018848727
14,68,4.7877230911941435,0.8567674787579299
90,35,4.017104747627666,0.3475651824665301
50,90,7.885812663252305,0.15224668925837392
88,69,3.527703312016046,0.321127267891277
60,76,4.367915897127837,0.855080625730378
65,1,1.2124336273196634,0.09352156937936174
41,44,2.1441067147164086,0.8308344029216161
75,37,5.23272398959814,0.12124510374511377
87,47,4.538318227134673,0.9102798004594629
65,93,8.049689874685107,0.6156585039063263
27,58,0.3047661250962619,0.7169403094641904
83,16,9.20113436803063,0.07680089025478865
52,32,3.3875605244645546,0.44228488826006074
5,90,7.7095709383023046,0.9799160115631094
52,97,3.6345203156375074,0.7827122469716974
12,58,4.838114696803647,0.05473584699724787
10,72,0.508058948403507,0.9765336364302032
17,50,4.027819358150032,0.24275873502950995
14,97,9.524236590404163,0.11350964370743688
11,77,3.527122966633823,0.5955015777009192
91,45,4.772816223394329,0.7847202464900873
78,67,6.20973216289181,0.8563825326861786
0,64,0.9431439754563342,0.5482371426367199
58,16,6.549678084867544,0.7318436530470492
7,1,8.399829627881095,0.4050885728277188
92,79,2.5015507201515397,0.6365583371748141
88,12,9.988248273349624,0.91778648665744
36,78,3.5159152629245307,0.2713837465739497
51,55,9.375528856307408,0.3026126573046497
92,16,4.027980101435947,0.9242330260173324
3,39,7.560986956014491,0.3032799651405086
69,93,8.318737374058491,0.9468290390539934
56,19,8.769740801633382,0.7910807429615233
3,78,4.85867456393448,0.9293999905478831
43,34,7.62621241841282,0.4909205436131492
38,70,0.8485749761563488,0.8174025274111226
97,21,1.4620852192773992,0.28112583098770594
78,0,3.6170753191985927,0.08955589947833242
4,75,6.741010108321763,0.042069505873642
85,91,9.104591257308506,0.6706786433254306
77,47,7.373105749484914,0.342128451024016
97,25,7.551534579609695,0.8037059903606372
0,65,0.18652005163854013,0.5596295475108785
74,32,3.8660598542307167,0.36416943608251073
32,42,2.979184547336715,0.8546082155458167
58,37,7.356194239909315,0.6748553130671284
6,4,9.441521880256872,0.6546753816907176

friday_contacts.txt

135
Agent Index:Interacting Agent Index:Time Interval:Intensity
10:88:6.688619668606895:0.9667447062422788
9:95:0.9007299229441801:0.29413754832944883
31:65:2.3104869981567724:0.13629109371609527
76:96:3.014786047555358:0.06339801582240967
51:45:3.131052149585477:0.4445434382525858
89:8:1.9427263725803523:0.8808337927621033
10:58:2.495396084901178:0.3683191418222923
21:6:9.164811448710253:0.9149064054528824
20:94:9.073726154746812:0.09172186189747888
11:35:5.811227020170672:0.04277875290311006
81:67:7.382044703884041:0.7562618254548581
35:88:5.427284912563087:0.4919887871599472
64:98:3.2931497408328347:0.20795695942795245
21:60:5.309981205719868:0.011521792726691515
96:55:9.656859364978201:0.3703774321272413
56:15:9.416354213552863:0.6794383500051776
26:84:1.984408867513613:0.29733391878026927
20:92:4.853416282497795:0.7638596564144793
74:36:4.155107158450942:0.3304639571662785
98:80:5.883730603507602:0.7125416288385673
48:58:4.237992321318459:0.2184722842877974
63:11:9.4185033672319:0.6937998477526771
93:84:0.07677001757306234:0.48147174789615876
4:14:3.9106702580659745:0.8198449581478692
20:25:9.759725526106168:0.7121362469200376
8:40:9.859266700927066:0.9002242531813217
7:48:5.9862020774776905:0.2562517464656725
23:54:1.687746631438165:0.5900449712604049
50:10:7.664351665510184:0.34811947944265953
28:46:5.972242241867672:0.8446013225231795
48:84:0.4610657004284924:0.6920856877392875
22:80:5.797512189699676:0.5749543208902808
15:69:0.6755755530815644:0.16837016924547443
89:21:3.7647209748264734:0.022369240867610274
95:99:7.419111728757224:0.6755793454767197
46:1:6.0869705981206765:0.9392659049557766
96:62:0.38005028756432413:0.9685916051859171
75:35:1.5759150731503702:0.7930309701559252
83:18:6.419755471582778:0.8909924833495784
63:5:4.374873831058975:0.14767216650642556
77:36:1.1436741940145367:0.027032871240488587
31:27:0.03543073074623537:0.26791737345013567
93:58:8.600159028547823:0.5089371541057991
37:83:9.525552257972498:0.7630639782440107
12:71:0.1838245228022528:0.16301999982715865
4:28:7.201106455933197:0.639241011778947
39:55:2.1450605439606854:0.0072084624135791975
76:28:0.8317688391569544:0.11354085521055068
92:75:8.891200749890123:0.40037278495757134
63:67:0.6057359831130937:0.49096137048602173
1:78:2.175748319238159:0.1973094235179783
44:90:6.603685950367471:0.07742541911225254
43:48:5.313085158201147:0.0031820058422277198
40:66:5.140130492045794:0.3522022574851984
73:47:7.622359504852094:0.3803414058582355
44:6:9.90535715069833:0.7067631788482184
82:71:8.438586749469684:0.05817509423223455
27:89:6.191647064682342:0.4954437234413782
40:16:2.7203192040973834:0.20918796475319368
72:94:1.0722333108247561:0.29041196859888474
71:3:4.911064356203861:0.40762232519762864
96:13:6.0216102844761465:0.18221116663426262
87:44:8.942071102975323:0.6762865235784432
81:87:5.828205005560443:0.43654291147178037
67:0:9.558161218422207:0.9782982086424898
26:15:2.747686471657702:0.5841505638191209
60:64:3.1877027193758387:0.7852519193645222
28:21:8.89719398196214:0.7796779703774409
37:27:6.148709351415107:0.11498229333292553
11:64:2.588199198086797:0.9232497898570643
94:60:4.584180815888526:0.6663030600822625
96:14:0.50361178946847:0.6276123660867005
60:21:6.304111960148045:0.40129177112848724
72:1:7.946241811259695:0.7384589147638106
16:72:8.280083142609529:0.4050472195277236
9:11:9.83701917013762:0.5098460996889553
72:29:2.7846659029729883:0.040863074669492905
0:87:2.5247982332670693:0.1763267929861304
9:42:4.288106193000074:0.2738502204725093
83:42:0.7506400165327698:0.7642430599819986
12:76:9.9768333694983:0.2717578760733744
20:49:5.381409836273989:0.6346301712804985
16:16:3.6726867565695507:0.5582714052891703
3:85:1.3686976214395152:0.29712675725144
74:15:3.4809262245707284:0.08676173861125958
99:0:6.3566236770104645:0.7076734815700955
92:2:1.2152547236516253:0.06654621024479845
62:39:1.2779870965308293:0.45103199912883984
0:67:0.001817668070662748:0.29057807898622035
68:31:7.533477004492588:0.9192380824068297
12:95:3.6035444861225017:0.6605518577198357
80:59:7.992128919597919:0.4588233013708509
92:9:5.942982815932124:0.8525527661682476
54:49:2.5528781753994645:0.1674153560281869
67:56:2.4068751851152905:0.06936911168175486
99:22:3.5335711374409917:0.39379603080655945
91:95:3.9199908183515664:0.25378177050876816
73:18:3.503121031888783:0.7584130737841819
68:46:1.4189787199600157:0.019877623645405795
44:23:4.212711155988669:0.30143615210535746
47:39:0.5087911598281225:0.5893668330016113
48:92:2.3542849120881657:0.9451616102571446
8:54:6.903186150983789:0.2695255667504325
14:84:9.462171277620177:0.060500538799988846
91:25:3.697664572724438:0.4079539487067759
88:55:7.346264967721757:0.552949376315497
52:88:9.665066927538911:0.7401975800017172
18:7:4.4697307603688285:0.3782737394491341
90:32:2.916400428655871:0.536697768154158
2:74:8.317196407757693:0.2955317179537563
65:73:8.914697419270194:0.8930540458434777
20:21:4.219761483887274:0.2632063077194766
75:97:6.310357238443305:0.14546059647798926
26:69:5.469932282412852:0.1777212237813397
22:31:8.687063973633554:0.02966055949862567
87:78:3.4690077115478637:0.5578236892580205
62:47:7.198318314161049:0.8276001292402286
74:98:2.3265643497552024:0.22498444445538013
51:72:4.797553032172871:0.13299317181491055
36:76:5.369674743302922:0.6308887356663198
81:59:5.147324117443669:0.8496019187336087
55:82:5.933758031306022:0.4066184094819313
54:6:2.448195518938933:0.0869846065164368
31:97:6.658117713557282:0.4701995171802008
44:26:5.384133256347262:0.2881262392733547
66:93:4.77688056130655:0.02994329042677224
91:19:2.565625338887955:0.888807228949255
40:4:4.003688953910921:0.9128964821711357
94:91:7.023901631427076:0.6382076850534217
17:52:9.229885545120888:0.3200856999256
82:5:6.528401052084762:0.747569444766493
20:29:3.094567113936595:0.6996607131893888
85:19:3.4859677673907297:0.4504028250467983
12:62:2.279581910720033:0.31311123094403015
96:66:7.081217549271823:0.7148731929250536

friday_events.txt

10
Location Index:Agents:Time Interval
2:45,66,63,77,71,80,50,14,92,19:60
7:42,17,26,76,4,4,1,42,29,78,2,24,24,48,35,10,52,57,1,94,11:30
9:19,99:10
5:1,74,5,46,18,27,43,60,17,29,70,4,92,94,37,63,96,33,11,81:60
4:43,29,49,74,40,99,3,48,31,96,93,69:10
1:24,57,37,92,20,6,28,94,2,95,88,16,81:45
1:71,65,42,66,49,79,32,50,16,43,55,67,32,92,81,91,87,76,82,4:30
0:68,57,48,79,3,26,63,25,29,21,44,35,37,56,85,55,39,6,40,97:30
9:74,26,10,55,59,41,64,38,46,76,70,33,17,31:60
0:55,16,79,60,40,54:60

generate_agents.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type:Residence:HLA Type'
	info_dict['Type']=['Student','Teacher','Administration','Staff','Visitor']
	info_dict['Residence']=['Dorm A','Dorm B','Outside','Teacher Dorm']
	info_dict['HLA Type']=['A','B','C']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')


write_to_file('agents.txt',100)

generate_agents_csv.py

import random
from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Type','Residence','HLA Type']
    writer = DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()
    for i in range(100):
        info_dict={}
        info_dict['Agent Index']=i
        info_dict['Type']=random.choice(['Student','Teacher','Administration','Staff','Visitor'])
        info_dict['Residence']=random.choice(['Dorm A','Dorm B','Outside','Teacher Dorm'])
        info_dict['HLA Type']=random.choice(['A','B','C'])
        writer.writerow(info_dict)

generate_contacts.py

import random

import numpy as np


def write_to_file(filename,n,no_contacts):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index:Time Interval:Intensity'

	f=open(filename,'w')
	f.write(str(no_contacts)+'\n')
	f.write(header+'\n')

	for i in range(no_contacts):
		agent=random.randint(0,n-1)
		contact_agent=random.randint(0,n-1)
		time=random.random()*10
		intensity=random.random()
		f.write(str(agent)+':'+str(contact_agent)+':'+str(time)+':'+str(intensity)+'\n')


write_to_file('monday_contacts.txt',100,300)
write_to_file('tuesday_contacts.txt',100,100)
write_to_file('wednesday_contacts.txt',100,500)
write_to_file('thursday_contacts.txt',100,200)
write_to_file('friday_contacts.txt',100,135)
write_to_file('saturday_contacts.txt',100,50)
write_to_file('sunday_contacts.txt',100,70)

generate_events.py

import random

import numpy as np


def write_to_file(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents:Time Interval'
	n=random.randint(10,20)
	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		line=str(random.randint(0,no_locations-1))+':'
		for i in range(random.randint(0,20)):
			line+=str(random.randint(0,no_agents-1))+','
		line+=str(random.randint(0,no_agents-1))
		line+=':'+str(random.choice([10,30,45,60]))+'\n'

		f.write(line)

write_to_file('monday_events.txt',10,100)
write_to_file('tuesday_events.txt',10,100)
write_to_file('wednesday_events.txt',10,100)
write_to_file('thursday_events.txt',10,100)
write_to_file('friday_events.txt',10,100)
write_to_file('saturday_events.txt',15,100)
write_to_file('sunday_events.txt',2,100)

generate_interactions.py

import csv
import random


def write_interactions(filename,n,no_contacts):
	with open(filename, 'w', newline='') as file:
		fieldnames = ['Agent Index', 'Interacting Agent Index', 'Time Interval', 'Intensity']
		writer = csv.DictWriter(file, fieldnames=fieldnames)
		writer.writeheader()
		for i in range(no_contacts):
			agent=random.randint(0,n-1)
			contact_agent=random.randint(0,n-1)
			time=random.random()*10
			intensity=random.random()
			writer.writerow({'Agent Index':agent, 'Interacting Agent Index':contact_agent, 'Time Interval':time, 'Intensity':intensity})


write_interactions('monday_contacts.csv',100,300)
write_interactions('tuesday_contacts.csv',100,100)
write_interactions('wednesday_contacts.csv',100,500)
write_interactions('thursday_contacts.csv',100,200)
write_interactions('friday_contacts.csv',100,135)
write_interactions('saturday_contacts.csv',100,50)
write_interactions('sunday_contacts.csv',100,70)

generate_locations.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Type:Ventilation:Roomsize:Capacity'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		f.write(':'+random.choice(['Open Air','Lab','Classroom','Cafeteria','Library']))
		f.write(':'+str(random.random()))
		f.write(':'+str(random.randint(10,20)))
		f.write(':'+str(random.randint(30,40)))
		f.write('\n')


write_to_file('locations.txt',10)

interaction_files_list.txt

<monday_contacts.csv>
<tuesday_contacts.csv>
<wednesday_contacts.csv>
<thursday_contacts.csv>
<friday_contacts.csv>
<saturday_contacts.csv>
<sunday_contacts.csv>

interaction_files_list2.txt

<odd_contacts.csv>
<even_contacts.csv>

locations.txt

10
Location Index:Type:Ventilation:Roomsize:Capacity
0:Lab:0.17931671057168175:14:33
1:Open Air:0.29934348486313356:17:34
2:Open Air:0.8567996764630795:19:36
3:Lab:0.15045983775394045:13:31
4:Open Air:0.37538590106059433:18:38
5:Library:0.12777319682254162:14:40
6:Open Air:0.5840889324453068:13:34
7:Cafeteria:0.9846490323043516:16:34
8:Classroom:0.7226268679741341:20:33
9:Open Air:0.25595540385626603:12:38

monday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
53,50,6.513987164288512,0.009841241328195971
75,41,9.148192540295673,0.24346079227118766
48,15,7.767801788987083,0.7136052498759755
52,88,1.5827087482234237,0.20764631674966094
91,29,2.7343235816879474,0.14895783199367685
38,8,4.334045729516742,0.6439094471011881
29,63,4.210981817246595,0.2554432621369428
27,36,8.47635840100173,0.3832054559612531
61,26,9.548384089998105,0.6427227428081111
59,75,0.19282581475098315,0.6828452816536151
43,36,2.766678550264544,0.09873500692734027
25,65,3.1770527299851494,0.3435712193957915
8,40,8.102382721566547,0.09587949195147027
94,25,8.241622912147497,0.5730417318280656
59,47,7.699772353241968,0.39279197145264855
89,56,0.5541668883448503,0.06333660535403263
17,97,0.39998863659011175,0.7631024669772002
21,24,9.481067041452134,0.383204002416655
89,60,6.957525839570176,0.30328744337885094
52,38,2.3412395878589987,0.16383796788949623
28,0,6.141151899527708,0.7962272950902385
86,66,0.2603884125232203,0.7358136231991173
91,84,3.6123649897326304,0.7799712779618293
10,8,8.663266709710413,0.12171387351501184
61,29,1.6262042324661152,0.10065614499197328
50,21,4.841685610806538,0.7543211044433643
36,87,3.452267039247221,0.24586831246758334
81,82,8.650432434700082,0.06323310864576226
92,22,5.715589643104055,0.31823996685111133
73,35,6.284157263259199,0.11694633297875623
10,84,8.999192526045253,0.6437786483355039
41,24,3.329425451169008,0.5970435365473865
83,28,6.085727947739475,0.16747815697677937
91,92,2.3851917083174436,0.7073630889864982
43,70,8.455754235213357,0.10182165120349396
5,83,6.174698872178428,0.941209396024936
59,14,2.291049580905622,0.5455844456290104
13,2,9.46944893647284,0.7369595962496633
58,44,3.57900560392034,0.6837125615283298
25,93,5.116785984157063,0.13994535890464188
62,46,7.148574887294899,0.5405338300800007
22,90,1.0063834871966304,0.4877659103098638
99,46,0.8665745698758154,0.10833600628847495
38,43,0.7143250674023671,0.23448581373131705
4,16,8.1451375328301,0.16790513318785627
4,92,4.96476895002348,0.8104031815415705
98,4,5.911646782174485,0.7988305382316447
65,93,2.6847021765065024,0.287871730729368
7,54,9.222677019633089,0.537725039535849
29,68,2.9028843779458393,0.3334779206701901
81,42,9.099960055312769,0.344508935524367
96,3,1.8276294599828247,0.5879862401519191
12,69,7.444276394152981,0.14065410813768553
53,24,3.8426989168155936,0.553110116244658
32,53,2.8024046447519813,0.14749352841808105
31,26,1.8300810567986148,0.2511953179826105
52,63,8.181614960177079,0.5342722919206799
43,79,3.6303269687559436,0.041962863257867467
32,67,6.808129891567074,0.9655803746876441
88,7,6.529327196638947,0.8705529513644207
24,22,2.343935060531636,0.9962011342143507
52,76,9.903506135903946,0.24535465759316943
34,71,3.440764716127096,0.8830014936240537
90,8,4.9723666216465015,0.8660633726701565
2,16,3.981201904138718,0.5462666429394176
51,73,9.173840400289956,0.865539433334778
29,47,3.148978965476934,0.008528755768497653
49,17,4.411053661515005,0.29462045868329656
94,20,2.99799449269492,0.008094047003058158
14,89,6.932501717103873,0.47267759190124015
71,63,2.4410782836620495,0.8450391983408515
65,41,8.452847533443084,0.2344428882511541
93,89,5.446234835932727,0.330607083902015
75,74,7.1764769352007916,0.025050360616053924
4,20,5.261301134184475,0.8635716467281545
50,54,9.317619443580812,0.648110436821533
74,16,0.9372646530940454,0.9950542940364594
87,38,7.183818262108614,0.13361402344750717
14,30,9.169442123777605,0.16886440853265938
88,71,3.3102347294712953,0.5561612966102644
85,33,7.070634425610756,0.2863648970197845
89,46,9.177775831868844,0.07552739773663264
3,94,8.183009216900762,0.4620521108586507
34,24,2.1401199464054477,0.5509220560673987
72,6,1.7324077130068205,0.8059066985821882
79,34,0.44615888882443167,0.36544912453839706
80,10,8.963528390783331,0.6351811175877636
50,9,7.047409633314041,0.41897643552173114
18,41,8.56145461013509,0.43182853230932194
14,83,7.53361659631241,0.217323186288224
86,35,8.89767842506425,0.9536791563015704
82,4,8.12157962871083,0.37018509361329277
97,31,8.648762210396342,0.34765263360451615
7,99,8.776027691582023,0.25878808302839884
18,83,6.269859483863993,0.3125403759007429
44,77,4.464382164004213,0.13901033013312758
15,84,8.98611873951888,0.5071629988483005
71,12,4.984748548530953,0.40055692578644464
55,15,2.5012472920711772,0.5476296836584915
29,20,1.323833021883356,0.28336173169726786
18,29,0.1507888595102047,0.07317326091711773
87,31,5.74536343344707,0.4686744188513029
16,46,3.6391719351673633,0.0794946256761716
47,6,4.142412582685995,0.12319743144062067
44,62,4.543247941899508,0.247526152958766
37,3,8.042882111482527,0.7441702016120487
16,62,5.768476020179256,0.47664663572590715
62,70,1.8980155315166736,0.23647147445571282
22,5,8.046646738605578,0.6345913535526976
2,30,5.274857202949921,0.3008106462729032
25,77,1.2803466196095459,0.36112700934738684
25,88,0.27025905784243576,0.33565355291757737
99,62,8.168023992027726,0.10124829380096845
26,41,1.8811304853591704,0.9029155005956867
26,17,8.589612826175477,0.9213970386212076
77,47,7.831043375818597,0.4247028116988881
35,17,4.152762492180637,0.4745679027732439
71,70,6.160068068019173,0.6521567323522888
10,93,4.425380020352634,0.7054440482566736
16,28,3.311087652028042,0.9166201071677543
85,31,6.45496925102327,0.22055188410179216
59,85,3.2656053850868405,0.4212341556171103
37,1,2.483329632428263,0.2826638966987185
93,70,3.188395899451053,0.21875828829817812
76,73,7.711950568255208,0.5022244747840047
56,50,2.785621560145656,0.9185163236605833
24,30,9.29755591451815,0.4328965887063524
43,79,6.304758039767384,0.8186748853057808
43,42,5.542757737114167,0.19833513133534986
16,68,3.102059433024935,0.11092284566460309
55,22,4.48744700920494,0.8547061529638681
24,45,9.16283607437596,0.5803737376495258
55,49,9.957213997749054,0.18841253768327848
98,92,5.763243882706385,0.9134748899042435
79,45,2.0356604853285454,0.9602500660221783
42,49,5.046691251349574,0.12302780997543783
19,8,8.395827945704132,0.5118220858973469
86,67,3.3253137878628913,0.31971277856462366
88,59,2.9664952022095536,0.19401296630828513
22,68,0.3384038872996886,0.20935264425984557
29,80,6.115713782624931,0.7495259112078474
28,91,9.320314287253098,0.16028074257299518
53,20,0.14522197751418386,0.6035801190491846
9,3,7.584164121549164,0.15831465204436646
31,88,2.730702741829185,0.29430232215069896
52,61,0.15088073664091484,0.8229746138822506
27,9,4.728286397295321,0.2628311922872978
33,86,7.614042631030896,0.6361740132693546
88,96,7.931938114936079,0.8622558475628161
82,59,7.5582925932469145,0.618105304402339
69,92,6.098154553251156,0.8344150383601279
23,50,2.771872594923157,0.5233656747968031
32,79,2.9523425703149506,0.05141448060184706
43,96,5.495483582006947,0.6075736888332405
47,49,8.948174139790332,0.703515789046357
47,82,7.79232272382948,0.28713868588623237
3,55,2.1197005987232687,0.8710215637843753
17,16,7.636187145481843,0.4463501376184047
66,17,5.96216082558347,0.1156295679843291
88,40,3.8833354969600564,0.7827144296503963
96,65,2.7887703424127586,0.00308759494866484
62,32,2.018292224720104,0.5025080299253926
55,54,1.7003344426926126,0.08895997472318506
60,57,9.673556267275426,0.6050642601172523
67,52,4.877761334010176,0.9199827916724133
63,13,1.7776983995884343,0.460048381662998
82,12,2.3935471506408144,0.8012083235883181
66,32,0.11754795261362139,0.37092099189472627
3,11,6.846716842391126,0.5511448261403317
98,78,8.156000887373777,0.07543486878681582
56,15,7.6584173143233,0.11395001805719251
82,67,2.510812196657246,0.2697436040624521
66,88,5.850870985161633,0.5194042053397994
78,22,5.318050188652826,0.3741489531161384
17,17,5.682766690116123,0.20102723484565566
84,58,4.600053324359877,0.8665601602485907
51,83,7.905940738999307,0.23155308931227125
38,92,6.330499459552722,0.3949879347311296
17,51,2.305887472499214,0.8529892916573967
93,49,5.043004483856393,0.6064709004839419
69,95,0.2476376406695313,0.17395004518990098
8,41,2.59984936498891,0.42830518585779387
64,74,7.659173451036309,0.7110193229540483
49,67,4.430934873881354,0.6036641096307729
46,56,8.343715010160377,0.46405645321798394
93,84,8.20735245970513,0.9654588716041783
52,36,4.071777045509133,0.09514425913775437
56,13,9.263991384633917,0.7514734590438714
29,74,6.844402733610884,0.9869675256906077
40,54,4.596949606037163,0.051095680266435295
54,52,9.074772108375173,0.8012517742676243
20,9,5.9595745038121715,0.6852065620945298
25,43,9.578013720201213,0.33143069568800143
53,94,8.883872807719479,0.143434155051809
37,86,1.0007570551447964,0.3335433501456836
90,12,8.867737588204768,0.7987419668276835
76,26,4.114199161021061,0.2052471840848128
84,37,6.40601059783028,0.29110876156895105
71,36,3.164557443444541,0.3836278225803993

monday_contacts.txt

300
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:91:3.901351436822462:0.7339762595213493
78:90:3.5626711544249745:0.6746469145132054
5:85:3.045488921169931:0.039540230198430115
86:81:3.35308756346429:0.3633954339845933
11:89:6.326551736128173:0.6289266025075126
41:89:9.329187350891646:0.6353349553288311
20:71:0.38645434909083676:0.04041798989591305
92:68:5.889425651825421:0.3950396909932594
2:47:6.557852441820289:0.7245690803250271
99:32:8.402723639687704:0.0029308245084296303
3:33:3.5133963523940634:0.5984631970990983
59:72:5.370053020778495:0.531313701010571
94:59:5.361572617932277:0.9886697867090888
21:82:9.786681409791774:0.644945666368522
48:42:5.011982887515333:0.051662026866201693
62:94:3.2808909541391196:0.9379521953670432
70:28:3.856798574421678:0.2557608578207182
6:59:5.678890997712748:0.3530777753051588
37:67:9.131898601767697:0.858499260774226
52:74:9.986369997027031:0.3156120836947629
31:94:1.507816879463777:0.750555105240906
99:93:2.121959362802376:0.09703892296171557
37:94:5.400780286788532:0.4299044759629851
15:81:4.75977897522104:0.3232455011774399
51:63:8.55510185721477:0.31288557896015257
4:59:5.9222428832641425:0.12157485621270103
43:73:6.795602865846028:0.06744185332954145
59:1:2.7021747467696375:0.050538725122540984
60:93:2.943901539081496:0.4982339562937653
3:84:4.133634564366487:0.3075102931608772
73:44:8.134038990032534:0.8418295238039117
17:86:0.542806560857374:0.7588119151895625
85:77:0.2859974668290466:0.7241059456194887
14:16:5.329831905451478:0.9195203828276385
1:90:2.211130035725443:0.23639098557775062
8:70:1.5406578056068143:0.4262130830791522
41:0:1.1579089653984453:0.7851213807119591
50:37:4.484956048115092:0.3060348403762886
89:88:8.043811518556971:0.4727074037001201
58:95:0.8868990689387879:0.3467640517252304
3:86:6.190128446233331:0.8176423375406843
19:22:6.298136491836642:0.5649230836250814
64:54:4.536312531879237:0.7989489195824466
1:61:7.833473806936388:0.650092667048976
43:76:3.44149914401369:0.8746721680294639
26:4:6.373337730771791:0.17592940756741804
60:35:9.934829550819842:0.5345586622312622
16:51:1.5764360886713513:0.6770030520140922
91:75:9.528950938813134:0.6204530683653462
39:42:7.840684207317652:0.6089954987487575
61:65:3.319567445374041:0.5478018921036643
52:46:4.646705033209998:0.537254045209896
95:50:1.7647409730344588:0.02688151759300872
51:35:4.156824965180649:0.7123780511350246
75:78:3.9226537983258227:0.596842377897293
74:58:7.563226282263413:0.8062241896776831
7:84:0.7120251049265447:0.9302238734157992
47:95:8.024189071209314:0.2387216724542346
57:28:6.2859322129180475:0.1556546337774144
70:30:2.530642559415648:0.25633238312842377
88:16:9.06713427487879:0.41516872075378486
32:35:8.09237307848392:0.5857892642742539
17:65:7.8134917233836285:0.28104998118729463
58:79:6.223781312011015:0.012229676686173363
90:90:4.692384410823149:0.46217556562178697
71:62:9.08294852508194:0.7287708959264037
33:20:1.542102652267403:0.8389929454049759
90:74:4.633437833538752:0.65537480210047
97:11:8.325837108492266:0.08894675467827051
10:72:6.557672074227439:0.8651050525879831
2:73:4.872758588552571:0.09357856657833297
57:65:6.828660337659751:0.48495229529925665
95:47:0.7466953354057215:0.6603079823678228
92:49:2.0077342778968554:0.9189494309774255
82:53:0.24061611784117698:0.9198471939191474
64:6:0.5767175723374529:0.5800677289768271
56:50:2.8103728644720705:0.7661027286936849
88:65:5.495737535254017:0.13475930874418607
39:26:4.520441829762211:0.7752928037603906
62:15:2.527874839657712:0.1705345466390138
75:37:6.949647409442933:0.5105509864259511
4:20:2.97399456614634:0.11289483289257596
41:6:4.273511532203043:0.09718585800915025
4:46:7.362236247628521:0.04058096586641702
43:36:8.117474485572878:0.14526881403519276
56:47:3.855901848144012:0.06033216610055803
82:62:7.36834872994402:0.7504447501739113
34:37:6.940059348353467:0.9688323772735252
80:84:1.746381666139043:0.7823637521600265
11:48:7.364223469449296:0.36985754872005294
24:63:8.322818469697424:0.26920941824660416
95:93:8.565298534711246:0.36630815362030034
55:98:8.261077047953284:0.24720488756898507
67:32:8.928969126003851:0.2768960055237545
61:35:8.836832665518946:0.8525592430588332
4:79:9.86527008653734:0.5241984425341051
15:84:7.895868992916919:0.9243365521462359
4:44:8.716228021549393:0.9307245519590631
96:55:6.888764694183994:0.0792443519258833
72:90:7.406214716467515:0.8859191527012792
16:14:4.69851133682884:0.19881286599667536
61:63:3.380324616991465:0.8268422238613041
52:16:0.19216831328206507:0.8647705352734371
45:84:1.2697886559236449:0.6513461320262995
50:83:6.159864427212866:0.23346664207112955
69:30:3.4770888522834897:0.36611239444780763
70:90:3.5286680285590846:0.061422783069107645
93:39:0.9637079127853909:0.807360297039483
59:97:7.043532289504258:0.12207223530271005
43:84:2.227673296719175:0.8300830010185617
47:42:1.833397217400502:0.21499046911985964
76:1:7.8677237896729535:0.1308918842693122
36:55:1.3055580188425742:0.7277279779721338
62:0:0.6371814138898102:0.739520928356319
9:66:3.6931844772562252:0.49433217498267423
5:88:1.486445418424901:0.7994315842241301
67:44:4.389739626245813:0.7501063883906391
87:66:1.5283456139385265:0.933656123136008
23:48:6.137030114523152:0.9235785663451955
55:39:5.593904911357448:0.7918698321362837
6:98:8.573685590443334:0.6643971331641266
58:40:9.370090787366754:0.16062179115642827
9:7:5.99552450577316:0.22398865033159399
78:78:1.6985126182973498:0.23431129397478678
27:36:1.7553777448861463:0.9571656933455291
10:2:3.340403747124703:0.266154426065234
20:32:9.04965093388158:0.7998074320401931
62:43:4.48824832171709:0.19692009534109944
66:68:1.044013500391131:0.17518002535790944
81:91:2.032022113548353:0.590068376259575
79:5:3.6678672963264725:0.8583172648269328
88:25:2.7906016247631857:0.3417441357034283
48:22:8.274301104789616:0.6180500643903922
37:34:4.895008043767715:0.470617322501378
65:98:3.9939963178189073:0.7597707393883737
8:99:4.344354090436447:0.08000834895567921
33:14:2.721637241356134:0.5469317967238905
97:61:4.480139087307686:0.9370228932878472
20:89:5.052620560783816:0.2537641232093184
77:4:7.5548012755079155:0.7647336265540196
34:53:2.3637671040815222:0.8631258102243161
97:97:1.1517561038645152:0.6080198126602016
47:4:4.764917096374596:0.5152616692151232
56:7:0.7673797837521523:0.05295581093999868
12:65:5.820115979848362:0.6738384932399847
22:33:8.160077472113436:0.35575801582167244
0:24:2.5976244236881127:0.07228477664392474
97:67:1.3349001353042622:0.5467070433629635
4:12:5.412935866383392:0.11638905261774857
95:64:4.328183987891099:0.5038839316589997
91:91:1.8006275689774043:0.7069818589813456
33:64:9.979922860196544:0.1730588104192059
82:14:5.625659929969839:0.427124084779392
80:66:0.12556782560516622:0.6318204731180151
84:81:8.965005435252678:0.1329683648749237
7:7:2.7621652564272834:0.22081572672352956
60:97:1.6189519082706272:0.5054371256483898
61:47:1.001552280836816:0.44870613684494454
12:28:1.7394787076567975:0.15123469338547757
92:66:1.9575307012529986:0.5143410029855222
35:60:5.974833467758106:0.18060226652566203
19:47:2.4143782472302853:0.7229634935521948
32:33:4.084222023013261:0.8052825167982911
61:42:3.880567198086904:0.33785485991760156
9:7:4.797609189111243:0.6557842812818759
60:17:6.786175767301016:0.855473866429063
87:80:6.840555606112167:0.10932325733796955
14:84:5.475907747014465:0.23291755605347986
91:29:9.17560043418257:0.17718783157595752
28:78:3.4425085921579193:0.8242337800670188
60:34:7.2394297827991005:0.8755428155410877
54:64:2.2671905891345414:0.7334964590961794
67:84:5.948336450041793:0.3098471787427216
11:56:6.700839762851763:0.3207358083318562
38:77:0.9674523146234348:0.6023850656632548
9:44:4.69793570668195:0.5029890933381677
47:20:5.319830568980307:0.38707577261884796
26:86:4.7817534968627555:0.2976309298220806
41:47:0.20291653431164902:0.4554712242013542
17:64:7.3452775056010955:0.6483550983905965
8:2:2.016021005062094:0.47350242384381125
27:52:5.803404417975604:0.9532995101175942
29:20:9.704176176125863:0.08789736542495086
50:87:3.1885515636171515:0.9367769520102764
93:29:2.0395613845066887:0.4946398943812982
76:97:9.019353389326039:0.1445289893279038
99:46:6.582281848173897:0.5425316978102004
86:6:1.6825724869426617:0.22432772872032958
90:29:7.500546164660645:0.30905885655729015
16:78:2.424664767871816:0.18813093908551015
87:61:3.2116382773342567:0.8869057054380951
27:0:6.159493456473577:0.2276835244003813
90:78:7.329164728333016:0.26167298307249465
76:42:6.930905362346148:0.9096883575644671
46:6:8.782850221647784:0.3169474488876317
62:40:1.370250262759245:0.5553566618394318
81:15:5.902292977530621:0.0556777547804056
32:33:2.3285018362586083:0.22805846655926876

monday_events.txt

17
Location Index:Agents:Time Interval
5:27,68:10
9:34:60
6:12,16,55,23,59,24,96,9,94,63,65,23,13,86,49,98,78,41,89,45:10
4:51,26,83,88,43,59,17,67,21,9,26,54,85:45
2:56,69,37,67,50,3,28:30
0:57,30,66,27,37,58,42,82,47,90,86,89,86,75:30
2:99,59,82,38,38,31,61,23,10,70,62,47,66:10
4:69,24,63,26,87,28,36,91,28,86,80:10
1:49,78,77,56,26,26,92,38,47,64,54,45,46,74,46,8,86,50,55,76:30
2:91,9,88,70,2,19,72,0,43,88,15,26,93,81:60
7:77,62,88,76,28,37,29,92,97,55,2,19:30
5:4:30
8:74,3,31,48,6,32,63,79,99,32,95,1,50,16,29,7,99:10
7:89,75,36,95,17,40,58,64,53,11,69,70,34,21,12,53,26,23,38:45
8:4,8,42,46,82,18,58,38,33,76:45
7:97,93,67,45,10,4,89,55,43,42,86,65,28,77,44,63:60
2:46,18,6,75,17,6,15,69,22,75,58,36:10

odd_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
66,0,1.8172369246500197,0.006012368176620986
74,92,4.063473307316592,0.44764113765276414
10,28,2.5078058170016004,0.9556666937503718
28,6,5.0663925880424,0.030157397136076347
14,34,0.9023473288248696,0.40762532229169424
82,17,4.409706397800109,0.8581145623573369
43,2,4.156581552776811,0.2765556802005179
62,27,1.2914385251065508,0.009513514017428504
39,15,4.862643162759489,0.3406184083074584
37,61,6.003686928238557,0.7615036747316247
26,25,0.75888883318599,0.7458770745925443
31,38,6.903844182398593,0.9278421364738013
78,11,3.956008580201522,0.6503839273684523
90,48,6.769446426997564,0.10888629056775989
85,91,0.7232151612815041,0.5467854056064144
48,61,2.2328510614263974,0.44718384957966695
78,96,0.7063783545238811,0.8896588296914104
6,48,9.293321418971457,0.8244605077577327
59,60,1.346910814314961,0.1075512883150157
91,18,4.159645334446736,0.9035452588829113
49,29,6.086884750360499,0.3383082213819574
49,65,6.530330988330888,0.6544708521532662
22,21,2.359281478670309,0.6932914366816318
36,11,0.6214541407681629,0.277228132146625
68,35,1.384050140411951,0.9858517993039427
27,3,8.941691191907072,0.8676675804245452
21,20,1.805147298856169,0.8465608015601254
84,86,2.4225104203737757,0.43038948235552
26,63,6.483320436344188,0.6967437250929029
29,96,0.8799624974435949,0.49727731725706925
14,27,9.114149895145916,0.3607316314698501
56,94,3.436609544657556,0.03583350304170885
92,83,7.224940852275099,0.796582912719368
36,14,1.9585817639216085,0.5523849902001531
31,13,4.484949871338362,0.26768807176332765
23,83,0.015340968768530194,0.10036970882600071
56,88,9.644090451403999,0.4768964287882195
49,31,7.09441961853263,0.06924208901127449
93,71,1.9071767620603308,0.1034237044014571
47,60,7.971199482192748,0.18391554416077116
26,87,4.1078464708973215,0.27716258035987584
4,84,1.187407367890705,0.591748889516374
40,49,1.5983463783361906,0.7862069123228235
35,37,9.477098230814514,0.6785201377250942
55,46,4.4392432669147865,0.8755928204327293
78,45,3.267941991588039,0.8380363457231991
54,81,7.164718663466019,0.1620363873309948
5,92,5.380116701363553,0.20282298378753127
52,93,8.344482466024907,0.8636854730280744
62,23,6.701569229007836,0.5040441233693438
73,98,8.118088212815017,0.28909432113979583
58,27,1.8661553338044412,0.950297468551335
66,1,6.492082007243933,0.46419110664867524
39,48,5.019406802332588,0.8247073034288753
14,50,1.4376340264666165,0.5119841394352085
99,26,1.8385790775577826,0.3252535154166645
56,24,3.81520574287756,0.9493833892557094
12,85,4.590511092408569,0.8830225997371698
86,49,2.256171992717796,0.23012674462476623
88,49,4.176669994920607,0.29142550506828013
78,12,7.834379855411703,0.7857791594264615
32,25,5.98743219341273,0.6017363643492531
22,64,6.920059245930357,0.40418357976938335
61,67,7.614317747527228,0.1194139638185292
49,48,5.287355461807298,0.6925352138956495
92,33,1.0903989744713527,0.7393398513964201
67,19,3.4874417270160754,0.7594694251871744
19,47,0.16504452846147655,0.6950781665506448
23,53,3.9361428532544496,0.2553940611426446
23,35,3.5450049204356415,0.24938808055450234
90,68,8.123188697688832,0.5077132957074547
45,38,8.489594860937716,0.8518278364060441
31,98,0.7355476804641636,0.4276085779375872
94,67,6.91245915282636,0.2578313271797378
69,66,1.4941880378682837,0.48289983567784056
30,14,8.822005482549379,0.6672125252010723
94,45,6.8878032268713305,0.4353270629542777
92,56,9.772394909616143,0.6595689986069757
98,37,7.6484985184170124,0.3917151822749264
70,59,3.702798635248433,0.061744383601446184
24,86,0.9885849505381816,0.46973558683739525
20,2,0.04912805020394151,0.6576301400716553
90,77,7.622215907800679,0.45040666331175994
63,97,2.5762955502800877,0.06689564467549869
40,22,3.184498179260392,0.4423936808955783
23,85,3.913729685022269,0.9982090099271226
19,45,2.038237218553247,0.8103181976684201
60,1,9.762520396551164,0.5486821038416273
0,29,7.192738832418542,0.6131583807313137
84,17,8.749307528435297,0.5225577012900724
83,48,7.086116913452992,0.9900778680704334
74,40,2.2821555707866126,0.35018334993217504
3,13,0.36995864315547355,0.11846637314579411
50,78,3.941694960962491,0.6034833071010233
37,56,1.2302019767572725,0.4601094370224549
25,82,8.558629082547203,0.18125777696742207
56,50,5.666529201636958,0.34450397914279596
46,23,6.008069289249349,0.8728988105709846
2,52,1.7097510664836613,0.13851074831486443
7,78,5.960466354795643,0.397621705031562

odd_events.txt

17
Location Index:Agents:Time Interval
5:27,68:10
9:34:60
6:12,16,55,23,59,24,96,9,94,63,65,23,13,86,49,98,78,41,89,45:10
4:51,26,83,88,43,59,17,67,21,9,26,54,85:45
2:56,69,37,67,50,3,28:30
0:57,30,66,27,37,58,42,82,47,90,86,89,86,75:30
2:99,59,82,38,38,31,61,23,10,70,62,47,66:10
4:69,24,63,26,87,28,36,91,28,86,80:10
1:49,78,77,56,26,26,92,38,47,64,54,45,46,74,46,8,86,50,55,76:30
2:91,9,88,70,2,19,72,0,43,88,15,26,93,81:60
7:77,62,88,76,28,37,29,92,97,55,2,19:30
5:4:30
8:74,3,31,48,6,32,63,79,99,32,95,1,50,16,29,7,99:10
7:89,75,36,95,17,40,58,64,53,11,69,70,34,21,12,53,26,23,38:45
8:4,8,42,46,82,18,58,38,33,76:45
7:97,93,67,45,10,4,89,55,43,42,86,65,28,77,44,63:60
2:46,18,6,75,17,6,15,69,22,75,58,36:10

saturday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
67,66,9.857117310137038,0.5920888806387263
39,40,2.9606587904019332,0.28799857726311495
19,48,9.933376750841031,0.5563869797724015
47,14,2.707792838677613,0.11910322049310873
97,93,7.330200583028975,0.05479876188162769
36,52,5.8053943720215395,0.5087501005692888
16,29,2.9259700624959297,0.7450077742936179
24,76,6.615452592843672,0.23066023231585764
98,1,0.6129732778252273,0.03079626452546913
52,51,4.991234253861199,0.11029211445761467
49,64,4.281211334785262,0.09369780403627248
76,15,3.110100778578777,0.16882508544803732
4,82,5.018950780821032,0.4528425446162895
25,23,1.523435869691594,0.8698275706188255
69,26,5.982815427997005,0.8195210582557968
6,69,6.002756213947941,0.47565948855915063
51,92,0.9693831534702857,0.8545362381598459
4,54,8.926690297564479,0.19650516555917197
96,37,1.0761711632578574,0.2856646294271543
56,11,6.206036904245246,0.025636240561556556
63,45,9.243970250499935,0.8459046432706034
18,80,7.21308647170134,0.18639074663509203
22,25,8.566766221711488,0.9194158046579632
46,78,9.436676144535742,0.3066038278097547
55,33,0.36786224695660175,0.46598870340260434
80,31,4.295593090845587,0.7887243574197714
74,96,9.079412546074366,0.2008470536485456
93,11,1.811367363309666,0.24854787128323197
17,45,1.3031037142786772,0.736713001093112
54,77,6.689236468914333,0.18914544406995604
48,55,6.561686523837922,0.8415382745176084
95,24,2.1199045466842223,0.3191748832651885
0,43,6.361037571365211,0.054785174673453896
0,86,0.05530006267194132,0.44722966677561227
67,8,5.108232528265227,0.0780720907027247
6,57,9.073223985113284,0.6305218263588744
16,8,6.875884563883529,0.7889755826254277
35,48,3.63455049866231,0.23653026160724466
93,72,2.726782980498262,0.7473684674491506
43,65,0.5285308238378339,0.8079994482515851
30,84,5.435250398044084,0.22794249938945577
68,66,5.812705194521447,0.3987768568223644
37,30,5.462713140719986,0.2651939540942704
17,49,9.554281145866424,0.2854383038592877
3,1,7.129363264331846,0.6332198675617925
34,66,3.7040828036302886,0.15835840507645926
39,53,8.70713280127657,0.7521303893855847
85,42,2.9065301405226784,0.4061131567948121
4,71,7.566520295016451,0.4702871038447024
25,72,3.3524932671715546,0.991241688877458

saturday_contacts.txt

50
Agent Index:Interacting Agent Index:Time Interval:Intensity
78:67:6.687995944426314:0.4033605962521598
60:25:9.766242543855506:0.06792652175390823
18:90:3.76439853374662:0.9833305943472379
4:19:6.506721718246754:0.47110824328789613
73:26:2.5400982744608336:0.40140270897106645
68:57:0.7095173223023121:0.2328466079907241
57:54:1.545474789816078:0.6593172582955799
90:25:7.843174009450287:0.8268544568417224
28:52:1.3914812455190517:0.8024812081343093
9:71:1.5518951054968155:0.9318507773242553
39:28:8.476359135606552:0.7763246795933149
49:50:4.7211099258761555:0.2950133516599971
52:12:1.6886708754449598:0.27197127175998526
19:20:5.6781326401719046:0.9590963962294797
22:77:1.530950907625851:0.3754295473613327
89:88:5.658452775120387:0.0803874959012173
4:12:7.636759575481204:0.2831880696193816
58:57:8.601430915297016:0.5434659477112249
4:32:6.147275128372893:0.856773362268219
75:95:3.3575411423184542:0.46747804925146763
48:73:4.294729247064641:0.050670872480034546
87:31:0.20788603769580694:0.06469788200264015
50:44:2.983096090063986:0.02735893758806829
12:73:3.5332686474645323:0.6308187130898412
59:18:6.507883109382785:0.8679178454556692
90:76:0.6431511136089729:0.38181470878945245
93:55:0.688031974008203:0.02165710753326533
93:54:0.5174288624109147:0.4172063304023521
90:55:2.351244324988293:0.20155062301576254
6:69:3.204281646873234:0.6853704116954752
37:45:4.207020809561934:0.5062926163922263
50:85:3.3126767901315013:0.11439025603353281
18:98:0.5483540344431337:0.5831224256744627
3:20:8.466441890639738:0.15531801936817402
70:84:4.252674278698835:0.1021412687119303
84:96:9.65755023003075:0.9104362499713785
85:30:4.477617210548132:0.2798872009598208
27:81:3.4726517979202596:0.6867846228811487
33:5:9.418615513088076:0.3089650238414625
82:19:5.969411859418942:0.9201045742327656
16:63:4.806229721717362:0.9196419859150736
29:41:7.958618332948535:0.44643319464470743
32:85:7.863072004177757:0.8397192112109673
64:63:2.8034310396456483:0.5005219190796429
22:53:8.636229353574626:0.5573464209207643
20:81:1.3492272749291145:0.5759296391255372
72:1:3.170961659254469:0.864395776604293
51:5:6.983939525485372:0.3998399286929163
6:11:2.828084537368467:0.7569874076404247
84:6:3.225352755378524:0.13692972894786104

saturday_events.txt

10
Location Index:Agents:Time Interval
1:63,91,41,97,7,44,73,88,6,36,22:45
5:70,76,47:60
1:50,91,48,78:10
9:60,55,98,50,0,11,22,13,92,54,33,84,14:60
5:7,76,83,54,90,94,79,2,75,34,7,96,50,10,23,54,94,15,41,87,6:30
2:44,90,20,90,40,99,25,95,5,3,31,31,50,31,4,48,40,80,6:10
1:99,53,83,90,63,45,12,82,28,96,68:10
2:61,99,34,2,53,88,77,16,90,28,43,63,62,88,42:30
3:93,29,68,43,29,0,67,49,9,92:10
6:91,31,55,9,57,71,39,25,76,76,17,6,18,34,27,4,75,4,62,29,6:60

sunday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
88,99,8.486887539457896,0.6304214594796798
22,98,3.2976061937586563,0.781281314136626
75,99,4.430757248996154,0.1192555052707921
1,16,7.048835094335958,0.13157790618351306
64,56,4.655156331391267,0.6998835788082481
52,23,0.40055384199715993,0.75098379425079
57,7,1.5925582330513355,0.18543205571638421
48,53,6.490406524470783,0.2868193033569312
29,51,4.493661262409669,0.5815858845665998
43,75,0.6573777627777944,0.48978674189411686
50,69,1.5519768951778778,0.054714720400359296
83,70,9.975574102080033,0.29285241977157617
50,61,3.8682067678943124,0.4261808134423466
75,33,6.740529736522591,0.2455572388334618
64,22,5.975520317563916,0.339408352827008
12,25,8.123063683302792,0.5784864212678281
26,1,1.4395665451710782,0.9527690571698493
14,60,1.4430820158919855,0.39247712631007536
2,85,3.8006454199690043,0.2921826762943228
67,47,4.673025133711266,0.8086927544736627
90,80,0.6743678721890989,0.8176138774275091
0,56,8.047433934786856,0.2559808833574637
95,81,5.215697774477086,0.9006825144319155
30,35,3.990730095641025,0.5069325770991455
39,65,2.3811238779362887,0.30447260187846537
70,7,7.576691410586301,0.09327402375535143
98,41,2.769033142970916,0.052943430372137
40,9,1.53999525068522,0.3637708934017183
25,26,7.337572932448316,0.9914045891898488
85,24,0.6183737562734581,0.12280394449800236
92,64,2.7937380809337697,0.38492439656092525
54,3,0.358907903107174,0.6108859228588672
88,13,5.20066638591457,0.6428277196598772
58,14,5.132696666075683,0.7770071741679037
96,37,8.951072527951148,0.15909392193657212
19,5,2.667024065530701,0.16760141110060367
38,65,4.776675413849936,0.6358673983241082
33,43,9.388244029403698,0.2677659863381495
74,3,1.920978437228692,0.8181512011809285
4,22,1.1168095443666715,0.49875323614048606
33,78,5.247483529457279,0.1808059413598897
27,55,1.4802711152787895,0.2644792206222256
13,47,9.879205440524398,0.8262467807502241
91,41,6.006774424396097,0.36409489715815657
91,18,6.618327483739249,0.808798335582276
28,70,1.2853107381027506,0.07098388830301883
59,30,7.047303701965818,0.6403449892486467
38,14,6.185026335954712,0.299405530751356
90,4,8.716153438777326,0.8234338404902319
31,81,2.4693304094766266,0.08279562712445887
72,3,5.256897773525066,0.6592241405225215
6,20,6.098476239562825,0.7645209043837615
86,36,0.08937541961729134,0.35642021687049374
75,94,8.854515849420165,0.9606419685383933
54,98,4.638295722864876,0.5073037465495944
98,46,2.1629311222063374,0.27936623481924505
56,65,4.647708874943114,0.3714395293129583
76,77,5.891978882816718,0.22901678369513967
8,24,7.3415194739594805,0.16260056535985934
6,95,7.261061146157749,0.521374883996052
3,57,8.603304779092946,0.5139482482796789
8,9,7.012907842879529,0.4997630558711238
66,69,7.715026519996071,0.8236282668323782
87,9,4.079938354014608,0.4660587190357144
64,48,0.8764088012451265,0.2749338760438347
75,12,7.643239456414168,0.6320420632107933
44,35,8.966995279467467,0.24014856201649082
13,30,7.402636843700636,0.022401560118258
57,73,2.820428173846705,0.943722059836342
61,73,9.41610911071566,0.5783995362749058

sunday_contacts.txt

70
Agent Index:Interacting Agent Index:Time Interval:Intensity
29:74:9.16372608230145:0.796954964751048
6:51:7.883900864399966:0.4086674799106108
80:96:0.14815771583060466:0.9598774863311844
74:97:4.308374781710823:0.5469594486352
51:13:9.042079023826053:0.4951854351944386
80:59:1.194192610139505:0.1806780778265884
29:52:8.298008123594673:0.6055655082754013
11:99:7.398529511198307:0.8423463023583696
94:96:0.8114992155173495:0.6185711886621139
32:66:2.3703816553952883:0.35420962128864475
39:99:3.855243189079789:0.19683280636170353
14:20:6.993801240807475:0.9924238391700014
69:5:5.368207898395178:0.3296239694354882
22:82:5.141505384517688:0.6680939851970867
16:14:4.381681285284316:0.13324126477503573
63:73:1.1377617766820813:0.016765931895245334
53:42:4.331827823657975:0.560247114997433
74:72:8.487044883624892:0.44857507061224955
48:53:8.142210501452041:0.22185756589964178
39:80:0.919320244588725:0.0066855429075109996
3:21:9.289809772081991:0.48205140669390023
68:46:4.536474019847931:0.6855403269937351
58:90:0.8335888281998161:0.5829499953459416
5:56:8.623750119046413:0.6850571684408665
37:46:2.19563994708889:0.32831432045342446
43:16:6.54823708272073:0.7241818732660742
55:95:1.356326584676878:0.8593548177807079
33:51:5.72041717661566:0.524825828129498
56:97:0.05304038966948088:0.9396784738557239
39:25:1.6057401908444169:0.7729036122188875
75:49:5.1930718546095:0.6210496845686397
50:72:6.400159461397456:0.4083531569308648
16:36:3.095902490181952:0.7697749975800223
98:36:8.736666708714022:0.9269722338361613
25:4:0.1996745353487972:0.3912838723392581
54:37:4.379634943637777:0.14566373998141358
92:55:0.019798603565377437:0.7045448754207068
36:91:1.6904254816989417:0.9488514792773272
75:0:9.344337089059099:0.31555189896437286
98:93:5.920573213081997:0.0028759470703639822
73:27:6.323996215279141:0.9268743088920766
10:26:0.3335853333487415:0.22327795239905257
83:6:1.8792327407608012:0.5844054293798739
80:39:8.666561745531057:0.6341570680368916
85:64:8.279808342659074:0.10714932227380469
51:80:2.597488396875214:0.0753952752053868
36:39:5.6836065070360675:0.6191528017058452
72:11:4.7881710302403455:0.7655770299526184
46:90:1.0209935816785076:0.3288165509174008
44:3:3.9645570905388516:0.9433763441413141
20:19:4.608574167399386:0.879494666634502
5:30:3.2439699423921597:0.1099899352310858
82:95:2.2446524075665786:0.3730883570466935
36:47:7.523082303216631:0.46842401692247715
67:80:0.46591410875939654:0.43019339282451874
86:1:4.153321745378494:0.8744248883962594
32:87:3.740524247725736:0.3904419258374895
4:88:7.107645004605604:0.971958195970546
80:46:6.087211809269826:0.5332140334707597
20:16:9.743912977300749:0.06325827742806112
11:68:3.451500943176198:0.1235586691736077
7:86:1.3489846728692534:0.9380271839281606
98:76:2.3319468321605408:0.23635317254322508
59:48:0.5527503420919422:0.9217009076588242
45:94:8.674582793785653:0.6210440386455588
24:38:1.587140836439298:0.7617430249347842
66:36:1.1370077466599082:0.7580769247367519
92:66:5.149056575604316:0.20909269304442213
96:32:7.470321654585279:0.14446721285379327
57:32:5.236089542116535:0.012270130984272543

sunday_events.txt

11
Location Index:Agents:Time Interval
1:0,91,50,25,99,16,48,67,28,96,50,4,80:10
0:74,4,62,93,73,44,56:60
0:7,21,89,8,27,80,40,21,5,85,48,40,47,44,77,28,83:30
0:45,2,58,75,53,99,82,23,42,63,9:45
0:70,79,66,53,46,99,73,66,31,61,49,14,21,54,55,37,57,23,72:45
0:17,13,47,80,92,35,39,66,86,75,83,14,25,97,65,21,33:30
1:53,69,20,0,61,11,55,39,69,35,58:45
1:80,7,55:30
1:49,34,43,54,73,35,38,82,39,50,66,41,90,93,3,96,90,40,83:45
1:8,63,58,69,43,4,78,97,20,14,9,74,37,29,12,58,29,63:45
0:21,39,32,72,31,7,55,40,76,38,75,69:45

thursday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
9,72,2.914227778302505,0.6826991464281399
4,23,1.7626573477813823,0.1867575121778262
96,10,6.188166718934582,0.5343467721947005
0,58,7.089036604140549,0.10775487424959562
9,11,9.67302895627405,0.8980069940395575
93,82,3.5711730462818903,0.8470185341812573
44,20,7.492920372324567,0.8767819235925547
81,59,8.877288545955315,0.5726311240333285
13,58,7.855807841441584,0.943416360883798
55,41,8.615477261049776,0.28432691622917616
3,25,4.451749865199233,0.8913642362995232
11,22,2.9730546580604997,0.29981855155001325
76,71,3.8844120704576577,0.7286341972286404
48,36,1.5232199132644864,0.5145761295979717
98,22,2.718914273776586,0.7076840108883607
26,15,2.1845358786259785,0.7831605938784265
88,80,7.478995841823769,0.6409935415627314
41,71,4.602269400448483,0.9921185937661067
9,72,3.4198736933186646,0.9166824813396153
57,54,5.300520828275738,0.6892351426205671
54,56,5.816201895124036,0.8659727417260997
84,47,4.094474084211179,0.3997958008433463
15,45,7.993018008393561,0.8704458892221738
17,51,6.13897007916667,0.9648374061250926
21,97,1.8012852087460351,0.36901019639081134
69,22,2.416538351050921,0.9864639695301223
74,60,4.362163305391271,0.3712260589076263
94,87,9.872530116893952,0.38401371533485174
72,19,6.7283068678525755,0.32881118325285585
22,29,6.68050847751975,0.9697631261691214
61,46,4.930428144808266,0.4172336148672652
56,7,8.72630136004489,0.21157918158984157
8,23,2.9498170927352128,0.6137918778944923
16,3,6.210064056150158,0.5153153524864713
34,15,1.3792770232659568,0.9517064711431299
67,94,9.48326600196943,0.9458851803129598
39,8,4.438852003317232,0.5900555758968664
88,92,0.024873423364663028,0.024753883423453193
91,41,4.98413218316343,0.847102271146868
46,92,2.681589592840341,0.35983462243833575
33,27,5.404912374265333,0.6721719225515167
26,88,4.635528942368791,0.9482013513281706
83,78,6.094650214477303,0.7149120805237638
8,38,3.792069398035318,0.057350140158731966
69,26,5.28691302773999,0.003072222774669897
70,4,2.9854952898326124,0.23203797932624926
67,69,0.6928829210410559,0.07763575576610571
40,84,2.5102336158353697,0.8960826689678371
33,31,3.977974273644035,0.5884174903951659
11,54,0.08465506592757133,0.6803844913904717
13,81,1.5661540838375687,0.34374535391176464
70,32,4.390982356838365,0.4622252382126143
80,11,6.4996703453849864,0.915809779678598
60,1,2.0111652888340013,0.9496414908132494
72,5,1.3238998665651902,0.49677849650953776
76,76,0.21973026748088764,0.9611826411513842
26,19,6.599848837190305,0.6225975506807934
97,84,7.907613923056919,0.1741259596155258
10,47,7.94232102985182,0.417089659665809
68,95,8.71933405152487,0.4730314036714953
87,7,2.273143230465149,0.941884830527986
78,95,0.7959663612567724,0.7807901682100586
43,64,8.268799497585611,0.9771738202667778
69,39,5.785017466876434,0.7661568652822905
83,23,2.157365752619306,0.6391250192403155
1,39,7.83307894998671,0.7311494293277346
45,70,2.623901876474808,0.2777724331353346
16,60,4.989352872396161,0.10707498471394494
11,17,2.729649547935531,0.8204342446345623
80,77,6.588965105333038,0.2867543980568842
46,98,6.693191757579374,0.40880065076358685
91,65,2.903597713934033,0.3931442047883065
98,47,3.8489534597418995,0.41097128089722157
12,3,6.854179151003045,0.04163972350503187
86,28,2.9960330864729148,0.8641050432288885
72,76,2.990552826012225,0.22519385952105397
80,4,5.530859821727081,0.38228882244996387
95,84,0.7199327596161764,0.036165290628866864
42,47,3.9865585053480834,0.6858625250509734
98,3,2.5758390391803863,0.4244230074059112
17,63,4.812652214934011,0.3686661931997619
14,78,2.6316982476663964,0.05211874976005215
22,94,0.6972111448385587,0.48403632622951254
42,43,9.515787037107701,0.009831527561575903
62,88,0.007127490780595158,0.2768895268006344
67,71,0.6313198403195397,0.041723659955730574
66,25,2.2595588525399313,0.3722392929544631
58,46,0.41879041384945803,0.08989144881754885
58,95,7.9549707494999815,0.2616410486899894
26,90,8.137043823209007,0.5491274799034928
34,25,3.5311620347403028,0.22867874954885714
53,2,3.3618329417577018,0.6602261848682114
48,86,1.9010065638418583,0.5222332913436765
13,69,5.81815018391909,0.9740220343398174
28,24,7.023058277512709,0.7600614904391981
72,63,5.96743005840613,0.7802621400177376
19,14,4.847806924816364,0.0602715099349338
4,90,5.268067740613599,0.10222213350582032
36,27,1.8701202916050896,0.8153445538596785
63,39,3.7330060909108207,0.49651367627585763
98,22,3.562740173389175,0.584405499328503
63,58,2.1213512446978147,0.36973486712719394
44,99,9.011727052172976,0.10133689707492555
17,96,6.0744716926653926,0.7885691635402455
68,5,7.739478589670432,0.44061535046167766
92,84,2.1120488722262367,0.74843764299737
42,47,3.1066401119398446,0.11698435709564625
24,22,2.9126818485457227,0.3062502402593752
70,75,7.2640730025786295,0.629716149201804
31,18,7.330169099939212,0.8053978568791952
93,4,6.636463080011932,0.9417900029895555
39,36,7.258963395972463,0.5027535711183595
67,8,8.3118070759105,0.40722458815879303
58,18,3.6859792198408057,0.02514212638286395
19,67,6.436887485383876,0.5813921401237467
55,20,6.006766646751819,0.766861522605022
56,37,4.369580401099724,0.7545066063955993
76,1,3.090850736286833,0.9860410422879425
20,2,2.2122538751064735,0.3612336038689087
34,12,6.975430024529682,0.624613234003694
32,96,3.050587676311365,0.28375970412714413
75,41,7.85166045809857,0.5441072252205454
40,17,4.941838509462828,0.6923794762394322
96,10,5.122349273134413,0.1602343210968591
22,0,0.29742176804522846,0.07935201348428711
38,75,8.115305318521397,0.1872478777553287
55,58,9.866422364691775,0.799434072835278
7,49,0.1231596065458973,0.006019103130803227
7,66,0.32837469341490233,0.09775416603535003
11,90,1.9215277216415894,0.3608638863999387
95,58,1.6549052827063115,0.7388836894644241
55,89,3.4918943509555342,0.501100680228859
33,47,2.063003195825238,0.6254691951767559
14,17,0.2866509121548366,0.5602100703307697
73,44,8.314534585649568,0.6427245865074744
64,8,6.914977634415856,0.5303422875831179
92,29,4.618361545030978,0.4025247887701302
78,52,8.394118930513107,0.8428192212566906
8,9,7.154540591525763,0.7681419593535012
16,78,5.870375376288676,0.2516987688916821
45,15,7.22290052422495,0.5789335840015365
30,70,0.9304791318675509,0.4358317276440241
49,58,3.6431043294920187,0.04814527874300567
58,7,9.247696072347372,0.6255585831106495
57,37,1.2072493171597976,0.3312372284986912
55,53,5.920168404176785,0.021570225426554734
93,95,9.270889095454812,0.990510265653494
81,21,0.0194679340984949,0.25879309573438447
14,68,7.476177081485364,0.5803638849832051
48,12,5.871094693209179,0.016679144323287654
31,68,4.764475343220857,0.5689332577292193
72,42,5.742546755802016,0.63520652056623
2,77,4.337892096324857,0.6947518771064183
49,70,2.689481916031582,0.2346540171653292
4,73,7.586705427025796,0.9864978247869635
0,55,7.520409724267434,0.5196642784240297
55,29,6.569064747805761,0.8986552476757952
96,39,0.4746965956888305,0.981056242780302
83,27,4.201419237470898,0.11884356937492535
49,43,2.002194361076721,0.21120283217260039
11,18,7.816131132470934,0.8352094974903228
75,78,1.7360727049404534,0.5661042498991689
66,18,4.324707202155118,0.9011730389581124
44,62,5.963336327035632,0.590330831248187
86,5,5.694819517099006,0.9624636894458778
86,32,8.635331937347214,0.42298712237986
59,37,9.119485006460856,0.04608631468556046
65,55,8.96179606900532,0.03603698154347401
53,57,3.169648036370525,0.3497514597548387
33,32,5.0467562135871615,0.6017709305141903
31,84,2.7072533056392634,0.47866019548591976
82,10,5.102039222186429,0.11077315602194338
20,59,0.4262601008151967,0.8410589832717688
77,61,6.402821803711511,0.3226962865941335
72,79,5.309314703913507,0.047961724848451626
0,47,8.776170972386415,0.032613562673203145
73,53,0.4579456252085079,0.22056235957099768
64,43,1.5696490819412223,0.0337971025431677
68,96,5.0651056351305845,0.0023909162306597898
48,77,6.4972649113014285,0.1609880879803035
89,79,5.287643091155157,0.9218511948809394
46,65,9.890000965899008,0.2799321359444805
87,9,7.147425047713284,0.6823879887739884
83,53,8.643393817035033,0.12893600630222457
32,9,4.919093266911232,0.6053801243752489
89,5,0.18834847867871263,0.08013066781696387
3,97,2.8701443642449354,0.40692134454910356
50,62,6.655688252252143,0.5996512305554584
52,18,4.281205400374355,0.6759452861604844
76,71,8.61244360242969,0.5435611879375963
5,17,7.416150191560677,0.6566592232635489
41,7,8.476104785876835,0.7795940299564589
19,20,1.5760014696603353,0.4746578912950198
80,32,4.164773840970143,0.6471675445446147
76,23,1.057749908595711,0.17445219531009748
12,24,6.31441926298651,0.029933610271667965
71,48,0.1112348878803271,0.7676168247170366
5,41,5.523272358997677,0.8850282091920424
76,40,1.2565672000595307,0.5671999232754229

thursday_contacts.txt

200
Agent Index:Interacting Agent Index:Time Interval:Intensity
9:80:7.0382921246333705:0.9556820598269046
44:69:8.573011972540401:0.07924998376906311
10:57:1.5325958641775272:0.3093902519850843
42:10:8.701123888970558:0.962585163449696
92:17:6.157994099854182:0.7080803028223787
30:59:9.725783500251366:0.7314558080633391
75:50:3.8058557518124214:0.3168807779694327
40:62:6.43557676676667:0.9763099081805485
92:27:0.04439101149650737:0.417023535886784
9:96:4.064443587754805:0.3627068026184982
57:97:2.9937333519214326:0.04831281631023343
15:10:5.893399813507008:0.47319793911862373
4:58:2.189181419606845:0.6803283844589387
71:62:1.7525285905496146:0.13896567010484562
79:36:8.506352552863355:0.8691976202423097
76:53:6.137904467285217:0.07106840002404269
84:36:2.560188707141876:0.7771950084819546
52:43:7.668274637265973:0.7312993690534784
54:93:7.939847618469336:0.7776433290453468
81:68:5.384535911579822:0.23525255976832482
93:16:9.920757993127333:0.8934543441881765
1:72:7.758134739731689:0.24703891010135515
94:31:4.736159938253104:0.279716657877369
11:91:6.604975713945529:0.2980626079316899
22:89:7.307471438634465:0.8331314885020186
66:92:5.074020217973926:0.5867164552992925
46:20:7.872054213346798:0.07697026703119414
68:84:0.014172442899552662:0.3614765333867669
82:9:1.0094025461973999:0.7513051533741296
88:35:8.916625728448652:0.13212679832443874
3:61:8.879634677276655:0.16048328962110692
30:63:0.889562352523886:0.14175368432296465
60:41:4.057982037703882:0.9460153048461237
27:8:3.4522820993929315:0.263099965041526
76:33:5.350168457592791:0.4817138552741015
1:85:6.03291472992156:0.7362304926092693
47:46:5.151177915023272:0.524784652285693
43:80:6.3260521909884115:0.20238179959236602
46:6:1.4188793789888487:0.9554001282463949
68:2:8.760839211072776:0.5522656652793739
71:87:9.866051901497453:0.050298054713874385
90:11:5.444448491136552:0.9347109216145608
17:22:3.1981154659531565:0.21362937499069423
55:24:0.31133364718463397:0.9904127541387862
39:36:6.652772458814239:0.32511126196824647
51:17:0.1908445268782133:0.7776073952742899
38:27:4.9287219964583775:0.6766708526045345
43:68:2.345297470120279:0.4888662207402883
52:77:6.2704802068309515:0.9727888002522699
57:65:0.31410385209842073:0.5678620629870902
58:9:4.5043887132533875:0.9529991526026419
32:91:8.856779214806078:0.6205084549405729
36:9:2.99684211320568:0.20084350883099966
52:42:2.234391573182888:0.7936651279874264
66:97:5.497317602861229:0.8037936396717074
43:75:8.631273282189033:0.6467101601379966
91:93:5.899984231792686:0.5589976222351022
89:98:3.4357223028901385:0.28650955658054356
32:69:7.035480596894519:0.715682876100546
36:47:0.702440123848932:0.37014161233455
97:35:7.795073444954294:0.690234242431429
49:26:8.188641044378683:0.2854457891281498
23:48:5.8860192391972745:0.9858892118609247
94:66:4.8331558942120045:0.9392247213322765
1:28:3.940617896767412:0.8119157936104054
94:9:9.511243180423108:0.4866582339311317
16:13:1.3706419546146398:0.4606816198641699
4:94:6.079250764705122:0.664159392552753
8:95:9.367600455819955:0.28978090388795374
87:94:0.009281959133973228:0.9533789826774827
58:89:1.9742256941383884:0.12038943184213302
84:58:8.2927498262796:0.6289705093238441
96:78:6.145813134167143:0.8797391678218863
15:29:7.304166478831061:0.37660257369721994
33:10:8.415998407750381:0.3884970977718254
44:83:1.5842806601754167:0.8979250123521796
0:89:2.301629122139719:0.07010399854914007
56:69:2.2033530452009864:0.0386290184572774
39:21:8.365740266584353:0.8769992573517463
35:8:0.6039725358473025:0.7231100758772858
16:29:1.1869583871524414:0.7934164894804442
13:60:1.1083952560802401:0.8160926988887836
0:76:7.296924179379242:0.04145973049986551
96:10:7.083416807339589:0.31052447378674664
67:95:7.3778033130326035:0.4699849766808416
14:60:6.545948462503307:0.28553558881248886
43:46:9.73653464036593:0.08518557315885533
39:55:8.001834086567442:0.1449534039528918
96:29:3.3347114644129827:0.4751405480035604
1:3:3.9843061077162076:0.2904432377787004
22:86:0.7737914282516112:0.9949485827235354
39:54:9.44229177563296:0.7555177614543236
13:8:2.9093184369975322:0.4074161100925633
90:56:6.3954756331314115:0.3630477505326515
95:59:7.741332185095796:0.0686127194562044
63:6:7.329546037754219:0.047602827898245326
57:55:2.2058024651955788:0.5420130616536876
45:64:7.972444005640122:0.9346612569555631
17:96:0.5028192989338531:0.602861775981457
19:47:1.1593911790613642:0.3294905796595542
5:73:5.053371460984918:0.43453735386468306
47:14:1.671267644260136:0.7617381750767552
5:17:3.8521114646242918:0.9027839801336598
30:95:8.155368057480747:0.3039655272261964
42:0:5.301894475432239:0.12825534758882506
59:93:0.867332721346481:0.8543727044000994
61:24:4.545866060225478:0.29454914795747555
85:51:7.265989073625477:0.9771268329383078
19:63:4.114022700622767:0.40193113111336387
45:47:1.0866634685103327:0.6439994336060687
36:54:3.3080183883197356:0.05478430344551444
27:43:4.491839482154164:0.34138799298187006
96:83:3.2537132537203695:0.4690103280088781
3:17:8.210480957200954:0.6785354918047558
60:0:8.166288756067337:0.009825187110891531
80:57:3.6405957600181194:0.9731578767400095
4:12:5.167714502627886:0.08442010394327959
36:60:1.5436074012458656:0.38231602955762645
13:81:7.487919848355968:0.7720185857814735
86:81:9.832572756034889:0.932466012208083
85:43:2.3720109674290333:0.8201010230106307
32:21:9.953311147163454:0.011674606541613675
41:94:0.5708653782110851:0.8171371735366577
80:62:7.826269089129624:0.8054818446439931
59:42:3.4752456682963286:0.16962944629105137
35:30:8.950010240117711:0.7321501698872532
36:68:8.049175109984219:0.8751769192948862
90:78:8.313662977740753:0.021380973721606078
4:60:0.3863981340857503:0.64324289181507
92:5:1.5821836994859562:0.6832838495910474
92:35:5.759253574243678:0.6722529014859097
63:52:0.3248004646469238:0.889428065406194
76:58:4.657899334607473:0.1269797853566087
20:23:2.030506718629682:0.623571833876037
67:56:5.721392098935411:0.9740022252292175
39:71:2.3800650310864215:0.39527668002658867
57:68:8.743679973789662:0.11559725358582273
9:57:4.426655660307315:0.3831574189336966
90:35:4.500662171278994:0.6457649176495883
11:15:3.610230581472095:0.7749786563954427
78:30:3.537127278785983:0.672334116297062
94:51:8.461216862609769:0.5454202707832448
75:26:7.878760507607612:0.23953114774140905
62:21:8.03728326258731:0.7317362756887453
41:28:9.881900293689839:0.5158710625716576
55:59:5.187576120008606:0.09037421440274218
66:30:0.2822708894510517:0.8239935690953065
14:86:2.4350591158958035:0.0451732234158444
10:13:7.540167443077943:0.9128796041719606
11:11:2.5872234084376977:0.2745810085653637
63:18:5.8349459811306135:0.2628000302629594
8:70:7.389502622983511:0.33781675668735134
94:92:9.716335252977252:0.7560749193156806
46:6:6.697843308826012:0.3071889852788805
14:56:3.0115810056757883:0.019309987557965647
43:59:7.70335287625792:0.07801146422444183
13:19:7.1531177132632635:0.46172693965817924
32:58:0.0947302768753655:0.4435550454724231
65:41:7.846459075221219:0.20486103557339408
79:30:4.855795600531242:0.6324741482930949
94:72:9.192984245755726:0.47308851194268364
1:19:2.5189273919855326:0.5697138720443463
43:89:5.623253504764232:0.3205680634577276
23:49:5.996101075819311:0.3092468469691083
24:77:1.1575274845932815:0.29449933851511156
97:14:1.542998906358647:0.5324160705560573
24:34:2.8547843066335075:0.8823443962826881
88:94:3.3126621067162567:0.36194029301617625
59:89:8.775881914064165:0.8253261132282622
6:80:3.9288893586194327:0.10275972449442927
71:59:1.6479602486244127:0.04174936740649626
55:54:1.5015663207731555:0.06830746730647275
52:2:0.9008968763444658:0.4046997291491362
53:73:1.8688136024961355:0.5830614775602793
36:8:3.9728441806543637:0.135649600130743
64:44:3.9868053958346583:0.12018302290146932
20:81:2.3295936709442913:0.16782594204362378
70:98:2.4625686349731533:0.7447068575679722
83:68:3.0500910480749255:0.18871352909901662
12:36:1.3232525955055607:0.6301253264588437
24:4:8.511899833302598:0.03323414664925628
50:77:7.491775365021729:0.2614312397444818
77:8:8.606105243413465:0.6596871488477454
41:11:3.3624084193448764:0.5842044609543943
98:13:5.435720234282897:0.07937243127913918
21:65:0.5322387729059574:0.38013044360335524
11:81:1.473579773422019:0.4182034100396437
71:6:4.054832359579801:0.994191633021709
91:44:3.9765231844593063:0.6317211275561101
93:14:5.805280716375303:0.662059365871648
86:69:3.1533754603139252:0.6050328383845425
36:22:4.280692953835742:0.7771808522245846
76:78:7.500105794724697:0.5285135775933644
78:73:9.218428037446689:0.6502891286543955
77:24:9.141627497458963:0.6764185110368829
99:20:2.9402718121860625:0.94933964891294
51:60:5.440972736761095:0.08167094302929112
53:46:1.4756695868875869:0.5147498179121255

thursday_events.txt

12
Location Index:Agents:Time Interval
4:77,31,38,72,99:10
5:82,32,83,3,34,99,17,14,16,63,9,71,30,4,55,33,74:10
9:69,63:60
0:1,33:10
2:56,84,16,7,32,61,16,91,4,82,78,62,61,56:30
9:51,93,72,82,73,58,39,80,29,12,42,49,30,47,58,71,20:10
8:44,68,84,13,76,11,20,3,0,46,15:10
2:64,53,99,84,91,79,25,20,4,11,60,53,56,28,77,81,9,8:30
3:13,99,8,72,12,60,21,51,23,21,40,7,60,15,54,93,62,25:60
9:57,86,23,14,99,44,86,56,38,57,6,1,71:30
6:56,53,13,83,76:30
4:48,44,36,38,69,60,52,96,56:60

tuesday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
66,0,1.8172369246500197,0.006012368176620986
74,92,4.063473307316592,0.44764113765276414
10,28,2.5078058170016004,0.9556666937503718
28,6,5.0663925880424,0.030157397136076347
14,34,0.9023473288248696,0.40762532229169424
82,17,4.409706397800109,0.8581145623573369
43,2,4.156581552776811,0.2765556802005179
62,27,1.2914385251065508,0.009513514017428504
39,15,4.862643162759489,0.3406184083074584
37,61,6.003686928238557,0.7615036747316247
26,25,0.75888883318599,0.7458770745925443
31,38,6.903844182398593,0.9278421364738013
78,11,3.956008580201522,0.6503839273684523
90,48,6.769446426997564,0.10888629056775989
85,91,0.7232151612815041,0.5467854056064144
48,61,2.2328510614263974,0.44718384957966695
78,96,0.7063783545238811,0.8896588296914104
6,48,9.293321418971457,0.8244605077577327
59,60,1.346910814314961,0.1075512883150157
91,18,4.159645334446736,0.9035452588829113
49,29,6.086884750360499,0.3383082213819574
49,65,6.530330988330888,0.6544708521532662
22,21,2.359281478670309,0.6932914366816318
36,11,0.6214541407681629,0.277228132146625
68,35,1.384050140411951,0.9858517993039427
27,3,8.941691191907072,0.8676675804245452
21,20,1.805147298856169,0.8465608015601254
84,86,2.4225104203737757,0.43038948235552
26,63,6.483320436344188,0.6967437250929029
29,96,0.8799624974435949,0.49727731725706925
14,27,9.114149895145916,0.3607316314698501
56,94,3.436609544657556,0.03583350304170885
92,83,7.224940852275099,0.796582912719368
36,14,1.9585817639216085,0.5523849902001531
31,13,4.484949871338362,0.26768807176332765
23,83,0.015340968768530194,0.10036970882600071
56,88,9.644090451403999,0.4768964287882195
49,31,7.09441961853263,0.06924208901127449
93,71,1.9071767620603308,0.1034237044014571
47,60,7.971199482192748,0.18391554416077116
26,87,4.1078464708973215,0.27716258035987584
4,84,1.187407367890705,0.591748889516374
40,49,1.5983463783361906,0.7862069123228235
35,37,9.477098230814514,0.6785201377250942
55,46,4.4392432669147865,0.8755928204327293
78,45,3.267941991588039,0.8380363457231991
54,81,7.164718663466019,0.1620363873309948
5,92,5.380116701363553,0.20282298378753127
52,93,8.344482466024907,0.8636854730280744
62,23,6.701569229007836,0.5040441233693438
73,98,8.118088212815017,0.28909432113979583
58,27,1.8661553338044412,0.950297468551335
66,1,6.492082007243933,0.46419110664867524
39,48,5.019406802332588,0.8247073034288753
14,50,1.4376340264666165,0.5119841394352085
99,26,1.8385790775577826,0.3252535154166645
56,24,3.81520574287756,0.9493833892557094
12,85,4.590511092408569,0.8830225997371698
86,49,2.256171992717796,0.23012674462476623
88,49,4.176669994920607,0.29142550506828013
78,12,7.834379855411703,0.7857791594264615
32,25,5.98743219341273,0.6017363643492531
22,64,6.920059245930357,0.40418357976938335
61,67,7.614317747527228,0.1194139638185292
49,48,5.287355461807298,0.6925352138956495
92,33,1.0903989744713527,0.7393398513964201
67,19,3.4874417270160754,0.7594694251871744
19,47,0.16504452846147655,0.6950781665506448
23,53,3.9361428532544496,0.2553940611426446
23,35,3.5450049204356415,0.24938808055450234
90,68,8.123188697688832,0.5077132957074547
45,38,8.489594860937716,0.8518278364060441
31,98,0.7355476804641636,0.4276085779375872
94,67,6.91245915282636,0.2578313271797378
69,66,1.4941880378682837,0.48289983567784056
30,14,8.822005482549379,0.6672125252010723
94,45,6.8878032268713305,0.4353270629542777
92,56,9.772394909616143,0.6595689986069757
98,37,7.6484985184170124,0.3917151822749264
70,59,3.702798635248433,0.061744383601446184
24,86,0.9885849505381816,0.46973558683739525
20,2,0.04912805020394151,0.6576301400716553
90,77,7.622215907800679,0.45040666331175994
63,97,2.5762955502800877,0.06689564467549869
40,22,3.184498179260392,0.4423936808955783
23,85,3.913729685022269,0.9982090099271226
19,45,2.038237218553247,0.8103181976684201
60,1,9.762520396551164,0.5486821038416273
0,29,7.192738832418542,0.6131583807313137
84,17,8.749307528435297,0.5225577012900724
83,48,7.086116913452992,0.9900778680704334
74,40,2.2821555707866126,0.35018334993217504
3,13,0.36995864315547355,0.11846637314579411
50,78,3.941694960962491,0.6034833071010233
37,56,1.2302019767572725,0.4601094370224549
25,82,8.558629082547203,0.18125777696742207
56,50,5.666529201636958,0.34450397914279596
46,23,6.008069289249349,0.8728988105709846
2,52,1.7097510664836613,0.13851074831486443
7,78,5.960466354795643,0.397621705031562

tuesday_contacts.txt

100
Agent Index:Interacting Agent Index:Time Interval:Intensity
32:45:6.213436231456636:0.3731609728338897
89:81:4.579771459232793:0.11434815810612176
0:16:3.242934650368129:0.3141918044148243
15:82:7.975466857130672:0.6128656081719708
12:29:1.371000381482833:0.3302304647764601
14:7:7.821227878776517:0.02845995074557328
85:83:6.395632457075665:0.7751395265422735
76:14:3.5755744413957458:0.5574299399414683
5:62:9.58634391552728:0.7052359820907551
33:45:0.7076539116973712:0.29372417115115146
46:38:6.129146387692819:0.2609382873034033
25:66:7.924473535575229:0.5101208649146122
25:30:2.2435787778590233:0.34060744129158593
34:60:4.927953728053525:0.6700587895933668
47:5:2.684771776287241:0.2574115092392507
89:58:9.625734441309854:0.5968114997896796
3:61:5.220803442701763:0.8620230034643891
29:91:2.4686173240322318:0.6230867759670403
2:95:1.5754431658870482:0.6105753814387738
99:4:7.691047725707579:0.3413757611023611
78:93:2.9318192055348415:0.4648065055530436
51:24:8.197221547880316:0.9718876331351113
92:27:9.198396797255626:0.16543165396821713
60:99:1.9852917319237706:0.6754749253049644
47:60:2.926189356051805:0.9833964216145247
62:53:8.777231457375235:0.7284431458587418
7:93:9.19597804582149:0.36088453244829266
18:16:6.90568418912085:0.47964664098450793
39:75:8.829446299574954:0.8566338306237319
79:27:0.30209164979904113:0.9162862348077758
41:47:2.8150099179100363:0.2202297658034008
34:87:5.121262434689546:0.34522516985978635
52:69:8.376611116331295:0.9721430380048826
31:7:8.568390078953934:0.2609747271152305
90:39:3.3323074765201985:0.3738473348752833
8:48:9.61161080916259:0.11255940822682353
29:42:1.046490193853703:0.9703762943992277
8:52:1.3703006046548538:0.06626361915074641
75:91:1.4840408750275702:0.6152611616346282
37:26:7.825349435322081:0.8239794472248464
22:84:4.940400200673159:0.7619456896388551
59:90:5.784623371260322:0.10666187082198009
21:86:0.3471548050285156:0.6173979174975259
42:90:8.178704998755766:0.4609972690917916
29:95:4.8406036225093:0.09106867309864752
53:58:2.9237402820948066:0.8880798506295625
64:21:9.633445470500215:0.3795317805234453
62:36:1.3372576343073461:0.9333367878739499
40:29:6.759730185924982:0.40384662674309035
20:56:0.9484204437096111:0.2700243168712728
91:13:5.593847444401174:0.14251730023029452
8:38:0.8118455773477451:0.15534571880671544
9:58:1.9613155867738086:0.9964979966329512
28:44:8.039543224362301:0.92149645738936
88:93:6.612640065946129:0.7475311567942813
40:19:7.620144079622059:0.3182416084415045
57:79:4.092781780687574:0.7267655904498955
11:49:5.677218509537134:0.620710266950606
87:9:0.5998969632207896:0.9462038643902423
79:52:0.3517405280543384:0.4570039635821589
98:32:3.0513911846409467:0.11192808504123763
48:26:2.144916542978387:0.7649703267181862
19:60:2.5378878697564122:0.5125815380642406
82:21:6.355984386247059:0.7538837278153913
76:82:9.092557531602177:0.74210496101346
18:7:9.409297527034369:0.9147893913873082
19:22:1.2673823917478577:0.3677686846010142
6:64:4.108843727499912:0.9996670729666973
59:39:3.968592643367236:0.6153826071593608
31:3:4.102429735302982:0.8091549649634372
75:67:6.408176908148027:0.4632439420199308
34:51:3.29285916399488:0.07362754325129339
96:44:0.8606828956909396:0.6380526760438952
32:55:7.632667913997516:0.8151815459035505
92:96:9.988069711163364:0.4201982040837776
76:55:2.7540455819170298:0.2728983302205812
71:69:0.2404635910591657:0.7019088464354047
83:7:3.7833428461767804:0.5059053649402946
54:81:3.347201881226524:0.6078784506485931
78:11:2.377145199017725:0.7250049576896354
28:25:0.6506573295260609:0.36397213695897845
71:55:5.388066989872032:0.7128044897824171
92:36:3.198366280780307:0.22223364625950692
87:38:1.1459230613858307:0.7737585259260796
30:20:4.088682224830098:0.9133713693060721
61:96:0.8000325343560455:0.9509401081347084
64:96:4.276249476121806:0.5545140479374902
30:46:5.864776032398824:0.9878159000860662
41:70:5.026586660801719:0.2196825484242737
5:2:9.153538434547116:0.6408688000252408
94:57:1.5258929617167616:0.3422495707436871
3:89:7.383070659810189:0.502938336341504
97:34:9.399292391381532:0.24970335740592198
16:30:3.8929316804406353:0.22739535842351322
69:8:2.1316090927287434:0.49286696627785687
74:15:8.485630359109985:0.11702412916620186
27:7:2.1637920715821535:0.18835559198252183
69:82:7.987112691829116:0.8388020770869082
66:3:4.0359322253773:0.8073362451987808
64:21:0.4793067219400504:0.8144255386406495

tuesday_events.txt

14
Location Index:Agents:Time Interval
1:73,50,39,25,14,89,73,35,67,97,80,81,13,65,3,41,71,33,10,93,72:10
2:44,5,88:45
6:46,48,82,17,74,98,75,50,71,66,80,62,54,30,16,4,88,9:10
1:36,65:30
4:94,43,78,9,76:10
3:84,73,44,97,4,70,95,77,74,99,18,6:30
5:16,28,88,88,12,5,62,65,83,26,8,40,11,10,6,21,59,21,5,80:10
7:69,18,55,49,68,69,16:45
0:97,73:10
3:39,36,99,78,15,63,48,86,67,48,73,31,41:45
3:86,56:45
9:79,20,8,63,28:10
1:49,81,39,95,54,48,86,62,97,20:60
3:5,68,87,94,53,83,0,53,65,99:45

wednesday_contacts.csv

Agent Index,Interacting Agent Index,Time Interval,Intensity
61,65,4.328046668612032,0.985927575919051
84,10,7.620089774123224,0.6894406168171453
85,1,0.49660140501557426,0.17113663902996323
66,61,5.911171322512088,0.8477257541038907
35,91,8.445849141778336,0.18743157653601095
25,36,3.6993149390935107,0.19610294657542104
20,52,7.652041015033962,0.593720732276105
38,37,8.776325054105284,0.44489657060726107
39,14,7.182823185974644,0.5994661734860806
56,84,6.1841616156809724,0.5447149497146385
41,12,3.2364163292005967,0.053084247009199115
70,51,1.0121018670684523,0.9439448340792216
96,31,2.3861925565270967,0.2077179825704527
30,15,2.7503704544016703,0.6584814644942648
91,57,7.701924871045484,0.5625320422657776
72,88,7.966795926989233,0.765764986379312
49,98,9.395141283755242,0.4936464057057941
45,21,2.554777957118036,0.19138996793090324
13,75,2.0523722473181785,0.1172558789366045
49,11,4.510140468018347,0.6685610259751634
67,76,0.0892268472350799,0.7595045810121461
79,81,6.905425986319638,0.28652218091888504
92,41,8.965797259331046,0.1111830483334203
57,88,1.6077903602598542,0.23666508265851227
94,18,2.9623650828995207,0.8286818654503474
7,43,3.5320340020252647,0.4900135259408406
70,23,6.9623452942853215,0.5989422292851269
46,90,1.2278281431609717,0.9809655525147104
88,82,3.6256539203044325,0.652968150803639
58,98,5.840166797355345,0.08341004682389153
46,92,1.9089385990892749,0.7922878921350877
48,4,6.5047384781314985,0.8623586866547435
97,17,4.859962975118499,0.26880367017223195
52,54,4.117961501993132,0.5779175887357123
44,44,6.3947200819473515,0.5191303061352239
17,20,8.77562936067531,0.5443645239302862
83,2,9.291858605237366,0.0553401914041618
96,38,4.392063292685494,0.7924637198155704
91,89,0.8634506601414438,0.8852301102932261
65,12,1.506487876908037,0.7230459861844032
73,9,6.040549612355375,0.48445441990887483
66,80,7.6930935709725325,0.35808034570668446
21,23,0.6893080651209738,0.6919562246517463
26,73,9.453648282840796,0.6472476848303975
80,15,0.9429433783029362,0.7874479617653929
67,30,9.713077959999799,0.10013524617085334
73,93,4.430791012289528,0.642167550276483
93,69,3.5884355542121726,0.30318563816332755
89,37,4.286112169752502,0.5006202264717081
91,69,0.7687621918845333,0.07230042085270683
62,81,6.654205605701176,0.045457620301632806
9,55,5.672536509959393,0.8385270018459005
68,51,0.24483441268529527,0.25713405068918527
14,99,7.621207045483196,0.8167099553391018
76,4,7.024219118326971,0.9943720801149437
49,69,1.3221231617256168,0.5328342003072312
66,43,4.887747382953863,0.20394253573112664
94,98,7.4126996949549815,0.29367037924723427
37,99,6.286106627722551,0.2619126643141313
63,6,0.832812218767901,0.9470207041963251
76,70,1.342204503326736,0.2937613524203415
98,37,6.626794154267101,0.9018444572054648
61,39,8.09451181666891,0.1964693047685787
42,99,3.3919911278057735,0.29072383449269557
44,45,1.2717686725097233,0.20486822969594776
50,69,7.69505963695207,0.6525106814545318
3,59,7.885329446684147,0.06484688289840057
67,70,3.253587218808436,0.11322294635609997
27,59,1.3979048716689257,0.888610582901755
16,15,9.725008138989033,0.6695882206932914
14,97,7.050542723813264,0.2280615100325062
71,3,3.099115781837357,0.4945560296945123
79,10,0.873045716297951,0.062218995783478004
72,32,3.837300145160926,0.34717495460382664
81,40,1.2811957884594871,0.20557462707871643
64,5,7.560186810066449,0.038954161433137124
44,51,7.547289644141214,0.6010275473664155
23,21,4.476984832575663,0.5376453780892193
34,32,9.401593609226843,0.02609028242161371
88,73,5.109582358750917,0.2502588811398663
50,20,0.2841553213291781,0.7347148282160669
53,49,0.7949851053601231,0.6723526724414021
11,59,3.1526743438539437,0.038438264425936564
17,92,4.954915369342104,0.5765202209730164
97,11,0.7430553059434564,0.07048154609904489
97,74,2.2559935402409836,0.19891247729586103
72,34,7.1045669560761215,0.7861633793033821
26,12,7.775725464106991,0.731181169906113
33,97,1.8568769510888994,0.5079134227431802
68,75,3.630914775760127,0.20061235101170882
16,94,1.2526813019006278,0.14847667968699463
57,67,8.877938195945346,0.9337752911284403
65,73,2.7637284496203307,0.841993871379342
43,6,6.325645938549286,0.7258209512232121
22,17,7.307170412248213,0.5234005003613431
73,51,3.13037572001468,0.6477820575490124
93,32,5.505042987502752,0.35835921674752025
99,78,2.6454288356693367,0.3164982894755156
57,92,9.812289350517588,0.5623329686119016
1,37,2.0648879347633176,0.24980254796571322
35,16,1.891855439278518,0.3822843921947797
22,8,7.762475116667491,0.17771103329866533
38,11,0.5324183508455382,0.034949273885693244
76,77,4.261420288493316,0.37360747330332966
81,41,7.164236487952083,0.7101325593900163
66,43,0.8935357969412583,0.8657665711676644
76,50,3.428200761017467,0.25890478593172395
99,46,0.8468548979927304,0.3191644147119663
9,22,5.089410865355925,0.1669946263236376
86,65,6.166238548237065,0.6861667846158687
5,40,9.952574759084564,0.5740822008793284
31,61,6.196867190769683,0.5013408241986123
1,31,9.786293451410323,0.7493047221140382
32,57,5.955203040682413,0.5154476797308712
53,16,7.899925691111145,0.6717976199569338
44,89,2.2887770907730607,0.5437877431298486
74,3,6.927331092250767,0.7711408817356543
43,85,0.3502160717277869,0.25177275481441486
50,68,3.191663096305529,0.7859630170530432
31,90,2.466720863401598,0.4172613934183278
70,65,5.890885485744323,0.7187113099960679
99,17,4.516599984695651,0.1336996859828995
13,53,8.345028778119513,0.42274493851882244
70,81,4.843046108814995,0.29882949899115174
33,96,5.086940545320671,0.5693637966446243
71,54,8.642047367290424,0.8744982799729936
17,61,2.340219219631605,0.31956414897879293
26,57,6.074845384645643,0.5409699197771384
69,21,2.1294574890971907,0.2709643921783146
49,55,2.800586970486286,0.9267876256478454
26,42,2.044916946626083,0.16778512659495093
14,32,4.404038389281584,0.45118631201309967
23,73,4.896898976924822,0.6552105197229967
14,40,7.208749458944464,0.4052460999354285
74,34,5.2590702640617675,0.3484693657371496
57,4,2.284649090331927,0.9101126726234063
97,45,5.31945308320389,0.9879016506375161
99,6,1.7667532909232442,0.981717913265498
14,21,6.949122932005802,0.3766728232278491
15,83,7.233802451412097,0.33188304141952363
27,53,9.16598064470289,0.5799604444665396
95,61,7.772594985484835,0.23932845465875385
50,1,9.12755457213107,0.7933504476682264
49,71,3.751756909835703,0.7044445200349657
99,0,0.9529698943986609,0.1734518386196756
17,9,8.368977669400305,0.6747855066510295
38,8,7.922329631466242,0.17003065570665266
63,66,0.08248394411642601,0.025204125415014267
18,14,0.35037893477147697,0.2570432890998742
20,12,1.1093342026532693,0.8637866257748087
48,9,1.4385408523522425,0.6011023785605366
27,82,0.8959795585527275,0.8263894883421955
49,29,9.912928347878655,0.1687802962067968
70,31,4.826779819867998,0.7976424167280071
11,94,5.4073162992369515,0.9911025818470419
12,15,0.18557679011843575,0.34219655525217973
35,91,8.388443772655819,0.6190861620759786
37,41,7.3793540838599405,0.27457526357482476
81,21,9.990002615945281,0.15798023159047025
2,68,4.7421748908980526,0.7041157877982428
46,9,3.4582306462648438,0.0982649453911274
12,35,5.7297343030678425,0.8156908469788527
21,80,3.1452009426140846,0.36813466433394093
84,77,0.8745670685564488,0.462304690992159
73,79,5.466148061501178,0.03678380515291757
82,11,4.212045965447891,0.5243474989886192
21,28,8.780982456238265,0.9291271311944286
12,39,7.352457920431851,0.31530648470158695
42,22,6.701632453253513,0.4388446954693722
47,66,7.604844544393275,0.8250566829750561
15,70,7.194567032478998,0.8641595346076059
24,97,2.7169131094048824,0.4711799178846521
21,18,5.221192538365581,0.8151971747381243
84,4,6.8823319278684565,0.550435690861847
75,75,0.8186212924814562,0.23191936021034265
71,19,1.6874259567261274,0.6506780037830153
73,47,3.334031924182553,0.39297451944792383
29,79,9.355379226552895,0.9853684666915478
4,11,1.570152606523092,0.3513192118111663
63,37,0.041291099445380786,0.4773016708317034
21,20,6.483665285102375,0.10154344154574824
19,84,9.518422615895078,0.4711081541121517
83,74,6.048261008286692,0.4656840049930332
7,56,9.5918004379995,0.7993214229516233
22,97,2.6271345433182547,0.735793220201614
75,9,9.04822832573235,0.24573275248001913
93,26,9.969699154601656,0.30249560798448794
91,19,1.105952908729575,0.7445486552483508
47,38,4.32787900607325,0.6363361898649607
31,21,2.801506738243793,0.47238990052666696
34,34,6.227871652666583,0.7127394795977807
15,36,3.9270782124055383,0.20733096743112944
29,23,6.319658248786027,0.45740263269721837
58,26,4.676403660977934,0.7337944076762869
83,27,2.901365627284349,0.7166612123493752
28,73,6.9471069326973245,0.22598254492640868
57,62,3.6176705454479565,0.25728408216306686
69,25,1.594419559468615,0.39618628353300334
3,56,6.452354020091264,0.5262731669374942

wednesday_contacts.txt

500
Agent Index:Interacting Agent Index:Time Interval:Intensity
35:35:7.326649613582364:0.01880486356965283
35:53:7.365575713168727:0.07226775243298234
55:76:1.7425302606537474:0.4079740971798641
30:95:4.307720149563242:0.5889349769548973
98:69:6.544728451402593:0.48025388395194957
6:88:5.072512252507798:0.8389958044296311
45:47:3.8194943780736357:0.06756325928583062
60:36:3.4337954746207435:0.780608576755907
28:58:3.9573951790562067:0.8814937045907152
46:4:3.762262794683556:0.16848724165974238
97:11:8.693414441617387:0.7460650092914961
92:75:0.660206718405888:0.07335924898540769
46:82:3.8637328951746017:0.5951215759777483
28:22:9.800867101130882:0.20271952020628436
37:49:7.060494204085794:0.5366861962135869
30:86:0.33597015028233757:0.10758491189019004
46:77:1.2885169728264612:0.10729300794480534
10:50:5.187607731001368:0.6703832544752654
74:34:8.93544026576964:0.04549012455350221
73:68:9.591015242175292:0.6962332597341828
10:44:0.5823210493669018:0.9689789406714906
33:32:8.436879771291043:0.021557896227508477
22:11:3.438680750408264:0.5398584734054681
91:60:7.210324988424461:0.8787960159091067
36:78:6.454046898476056:0.18222360148833006
44:28:0.883667185292053:0.922569686376188
64:47:8.689647085944292:0.1418009831093382
62:89:2.7679253812446216:0.6435851301783628
78:1:3.352951970635684:0.4754766055188496
69:62:8.345007894678666:0.23798159130601404
50:92:4.481913480277774:0.006233696946746248
98:26:1.1338952630227828:0.673962261965644
37:99:1.0643710496145387:0.5908347291714198
28:4:9.920590767349442:0.6963377903623542
18:50:1.3809350166002898:0.9872588601452001
24:49:2.094116142874566:0.5748045544228527
25:42:5.693608169443762:0.2879191230183471
54:42:2.4086948504552073:0.02547746034366105
51:2:4.451408189223509:0.6032579558452265
60:20:9.005811670608857:0.9856136917050264
65:67:3.19976980590579:0.9186950562480023
3:47:7.181260983583534:0.3040250437725214
68:57:0.7383565151524951:0.723133921333527
55:66:8.367449475616333:0.07980719114975277
9:11:9.97517977101612:0.6127447997868063
31:33:9.975729093568951:0.1045218021830403
83:27:6.640381396448985:0.15661340183018102
95:37:0.726266659582897:0.8758271106350278
17:27:3.731549646431953:0.3229035252474013
86:73:9.8942767252751:0.3990519563823193
3:78:8.720720998194327:0.264559175826406
74:46:5.4153651996120225:0.4173530824931695
95:95:9.484928224637814:0.022785741619000488
82:9:9.340319131198878:0.7952612413547455
8:42:2.475879993476423:0.3886327787838908
82:97:9.866336760171512:0.7188202155620746
56:34:4.302447425405743:0.28633873882974836
81:45:3.724740927783703:0.9167028205119899
25:29:9.199922961967527:0.835041826144438
94:68:3.8825993503747194:0.1168420663032067
94:13:0.42404086307323285:0.7521313232183118
49:11:8.607236167340723:0.6186404850761289
20:25:2.005728328797962:0.05980946246418717
59:91:2.961636869772523:0.1292205857836043
52:74:5.743776860036771:0.9984405022263465
75:72:8.15665662213246:0.8856720647622587
52:74:7.689955305723457:0.24021318982090323
59:44:1.6743181640329041:0.1378869655341155
72:12:7.638936582230792:0.8289049815374233
16:20:5.910006486116654:0.8403350173657851
80:0:9.524612020268266:0.9890454406593926
46:85:0.1978619520777969:0.7542955579200908
94:45:8.182315486473957:0.5539970521800692
26:68:2.410142904138397:0.831822112439275
12:59:4.111959521312285:0.2511690380469713
16:41:8.11653477037407:0.4864135139348226
7:6:8.41408841840117:0.5166597024174876
82:88:7.539262351768925:0.4934590077378854
23:87:2.0191699791040407:0.4650993959348293
14:42:7.959803809049787:0.3859274394432979
72:39:4.988558132860267:0.9719137961462334
36:87:7.904796353087814:0.9033328489705758
49:41:6.441286834271189:0.8596066636809503
26:45:1.5494084880416248:0.21286592285078343
69:84:2.68713252804031:0.6352927537106948
92:50:8.125056564396175:0.964640333194231
25:50:5.745327231004454:0.4996238922147982
48:58:9.430548312366577:0.9662240771532448
51:7:1.5407008662168198:0.43018447051424324
94:85:0.33708865480739925:0.3999028227241679
8:42:0.4676328396476981:0.24681536908527746
3:54:2.082373656993525:0.8211009673756547
47:5:4.176150167437918:0.0068115940272549524
13:18:1.9920879889202947:0.4530248033751796
75:58:4.656733076780746:0.11008410284813352
87:66:5.736523536915983:0.714940200457393
0:99:5.146933211148694:0.14934608064069932
14:37:8.826800764399827:0.36862157844671417
44:89:8.617130159606878:0.629612083711045
76:62:3.808269227488712:0.720616750357066
85:88:6.012459883150846:0.16207079061869079
70:45:8.460933196001442:0.03484670155649383
91:61:9.052509705425388:0.8533345550696678
72:53:7.626567323195205:0.6547255311759907
14:87:6.123814961834867:0.10799512631697394
47:38:9.703366381545296:0.7861481672561152
50:2:6.921877657912186:0.0901922793109946
55:69:8.194239438728719:0.25190470736562665
98:56:2.4951109730602985:0.313928578913919
4:36:2.5502983171221585:0.13611513898232153
79:69:3.7285925099089035:0.17979490568657208
52:95:8.298231631001554:0.2418250870038332
54:74:0.683477780477264:0.12947423620560494
88:35:1.449078957263129:0.9971706032369194
42:6:0.35682089991305177:0.8220981329520314
6:81:5.059336042688641:0.06759370944002929
41:79:9.740240133624962:0.3483763795581215
19:82:6.050535299281256:0.2265648321824162
46:47:1.6596588569359982:0.1808366243887748
18:26:1.7194269810771157:0.469856221060189
40:25:4.586799772966139:0.011566746615992107
44:85:7.874474589247804:0.21940431108028746
21:86:2.022571555919419:0.0267482489112576
55:1:2.4888121465990034:0.6949916683725545
6:65:2.0048251474987087:0.6007769598222094
46:99:5.203358357806579:0.49351952534458354
16:89:7.784970962656438:0.3795554828381028
83:56:4.564802235334828:0.30456464371028225
19:79:1.6659490128697307:0.09406007012891293
77:23:8.95317196939301:0.861612362884061
94:93:5.5446862725779695:0.6345596358727227
17:81:0.8694353708834501:0.8610458639123718
42:77:1.0046753749712778:0.8361913703426427
80:82:7.898528087560851:0.7837288261770342
99:67:5.6795968227766185:0.37214616970155145
57:36:2.7938124820144363:0.10872997346449254
45:73:5.180491151160156:0.183795261793106
10:7:7.565100574481845:0.033325877812489635
79:38:3.158002500478579:0.8762332523999679
46:13:0.8163960575888862:0.11687793448566342
21:98:6.981821368222011:0.6207591025489955
73:8:2.3297846497396932:0.38686431622259887
78:63:0.4436434330479888:0.3894958140703567
34:59:7.191388054443503:0.20095769579977418
73:30:6.053525772644528:0.5967585140005333
41:40:1.3838754859326574:0.502405241786681
49:60:8.295110054329713:0.4305993322400177
83:63:0.48547541285623885:0.32805016959748434
93:31:3.6848854616150293:0.35643612817628656
12:78:0.2273456842385202:0.953077640082888
30:89:7.153927919430508:0.43586809846112506
56:81:0.09816756643055147:0.6295484364565437
83:7:7.4085537764271745:0.033305634133947604
92:42:9.988928688371795:0.2761232871677961
70:88:6.140537017964541:0.9795273172673395
2:90:6.461894038111831:0.1185444682840926
72:46:9.953506502098817:0.15329163492922593
23:90:1.9471840933452211:0.7386518914419935
10:35:6.373387939809772:0.7171440826931259
62:22:0.5596942604876298:0.1432896719544473
38:92:0.9481383578775293:0.7677431462939046
74:22:0.24856782036149716:0.42812724703916316
14:83:8.576386817177024:0.9396215064528413
99:2:9.30095330632649:0.5066561439508125
63:22:6.403955394913:0.1571691479302042
78:87:8.344142685105586:0.2861576111801998
71:55:1.4476785659120595:0.33708688327676783
36:87:0.21348728262420402:0.28711138205567277
4:65:5.67218610317065:0.36711320844191897
2:81:9.67653850599029:0.03156219872649857
61:88:5.724543360415383:0.7932661225893052
53:59:9.185083874031697:0.026215655980081132
7:40:2.8312580779681875:0.49196082108715233
76:96:2.7315283363582776:0.6519656627078148
86:93:8.443020347066351:0.1248034896150344
20:95:3.9697649231974808:0.04606324434996534
73:11:2.5528837643963884:0.6948268362453378
50:68:5.756463265683402:0.995685718348466
41:92:6.3336884749824645:0.1621356000106734
43:18:9.664694397742583:0.8045465791810523
2:39:3.9394795994181777:0.5867371111778215
1:82:4.841485437297484:0.49371708023469907
65:53:2.7498717037820644:0.20583454120465094
73:90:1.121137779062057:0.7937686270961586
53:87:6.2353544374513845:0.015946094567476976
44:63:7.393381816549878:0.3472456996800237
15:90:6.9611182251442685:0.8039647151078758
88:27:8.01133506522802:0.4130507216742557
54:87:0.23852744551902894:0.6264091805644847
21:16:1.081879835206111:0.7790590360105223
22:69:4.123929864290275:0.9702498443951281
9:97:3.0488474948409316:0.677209238072471
67:83:4.86032668987452:0.27054126955750546
62:20:7.499374514038265:0.003010254325323869
24:69:0.3027367484925514:0.42734474246664456
90:30:8.329167767245044:0.13749033066927274
84:33:5.994927378398588:0.3756720097493309
9:76:7.402293740792216:0.05790492058061891

wednesday_events.txt

20
Location Index:Agents:Time Interval
1:24,85,69,73,76,29:10
4:92,10,68,85,96,4,99,56,54,56,52,41,36:45
8:2,72,22:10
1:63,20,94,35,35,18,15,1,36,26,38,35,12:10
4:97,40,12,26,17,84,60,87,99,44,79,15,53:60
0:39,60,6,4:45
5:35,14,24,51,29,41,21,65:30
9:78,8,6,51,18,2,4,53,31,50:30
6:67,97,1,68,59,78,59:60
6:49,61,22,82:30
0:0,79,83,18,77,31,7:60
3:83,15,51,85,22,78,61,33,22,26,17,28,38,58,69,5,36,5,58:60
2:49,60,2:10
9:16,37,29,82,95,68,71,14,12,92,2,11,62,12,9,4,77:60
0:28,88:30
3:72,38,92,9,71,10,41,15,58,85,76,13,48,87,71,18,73,86:45
5:4,56,3:45
5:6,12,70,86,41,36,70,49,14,78,38,15,67,75,32,24,5,60:30
1:23,55:10
6:21,25,65,44,7,57,44,89,54,37,46,93,50,24:45

Scheduled_SEYAR_custom_distribution

This example builds over the basic Scheduled SEYAR model by introducing the concept of custom distributions.

Custom distribution provides flexibility in terms of scheduling changes in state for user defined distributions.

In real life scenarios, not all distributions are symmetric about a mean (not all are Normal distributions). Thus, for Scheduled models, Custom distributions provide flexibility in terms of scheduling changes in state for user defined distributions.

It helps us understand that there may exist a population in which, for instance, there is a greater chance of a person recovering in x number of days and a lower chance of them recovering in y or z number of days. We are essentially including a likelihood factor with respect to the duration of recovery. This is in comparison to stating that the person essentially recovers in x days with a variation of 1 day.

This way, the user has the liberty of customising the distribution in the number of time steps to represent the population under consideration in the best possible way.

More on custom distributions in Tutorial 2.1.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import random

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.6
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta

def fn1(current_time_step):
	r=random.random()
	if r<0.2:
		return 2
	elif r<0.8:
		return 3
	else:
		return 4


class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Exposed':1}),False,0.95)
		self.insert_state('Exposed',5,2,self.scheduled({'Symptomatic':0.3,'Asymptomatic':0.7}),False,0.02)
		self.insert_state('Symptomatic',11,5,self.scheduled({'Recovered':1}),True,0.02)
		self.insert_state('Asymptomatic',6,3,self.scheduled({'Recovered':1}),True,0.01)
		self.insert_state_custom('Recovered',fn1,self.scheduled({'Recovered':1}),False,0)

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Scheduled_SIR_External_Prevalence

This example builds over the basic Scheduled SIR model by introducing the concept of external prevalence.

Disease spread is contained using policies and restrictions that are enforced by the government or university management. However, it is not certain that all agents will comply with the enforced rules and standards.

Episimmer uses this example to study disease transmission involving such exceptions, i.e., creates simulations which are influenced by scenarios such as agents not following lockdown norms, not getting vaccinated, not getting tested, etc.

Hence, the students are associated with a “Compliance” factor which will determine their susceptibility to disease outside the defined simulation environment.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def external_prevalence(agent, current_time_step):
	if(agent.info['Compliance'] == 'High'):
		return 0.1
	elif(agent.info['Compliance'] == 'Medium'):
		return 0.2
	elif(agent.info['Compliance'] == 'Low'):
		return 0.35

class UserModel(model.ScheduledModel):
	def __init__(self):
		model.ScheduledModel.__init__(self)
		self.insert_state('Susceptible',None, None,self.p_infection({'Infected':1}),False,0.99)
		self.insert_state('Infected',6,3,self.scheduled({'Recovered':1}),True,0.01)
		self.insert_state('Recovered',0, 0,self.scheduled({'Recovered':1}),False,0)

		self.set_external_prevalence_fn(external_prevalence)

agents.txt

1000
Agent Index:Compliance
0:High
1:Medium
2:High
3:Low
4:High
5:High
6:Medium
7:Low
8:High
9:Medium
10:Low
11:High
12:Low
13:High
14:Low
15:Low
16:Low
17:Medium
18:Low
19:High
20:High
21:Low
22:High
23:Low
24:High
25:Low
26:Low
27:Low
28:High
29:Low
30:Medium
31:High
32:High
33:Low
34:Low
35:Low
36:High
37:High
38:Low
39:Low
40:Medium
41:Low
42:High
43:Medium
44:Low
45:Low
46:High
47:Low
48:High
49:High
50:Medium
51:High
52:Medium
53:High
54:Low
55:Low
56:Low
57:Low
58:Medium
59:Medium
60:Medium
61:Low
62:High
63:Medium
64:Medium
65:Low
66:Low
67:High
68:Low
69:High
70:Medium
71:High
72:Medium
73:High
74:Low
75:Medium
76:Low
77:Low
78:High
79:High
80:Low
81:Medium
82:High
83:Medium
84:High
85:High
86:Low
87:Medium
88:High
89:High
90:Medium
91:Low
92:Low
93:High
94:Medium
95:Low
96:Low
97:Low
98:Medium
99:High
100:Low
101:Medium
102:High
103:High
104:High
105:Medium
106:Medium
107:Medium
108:High
109:High
110:High
111:Medium
112:High
113:Low
114:High
115:Medium
116:Low
117:Low
118:High
119:Low
120:High
121:High
122:Low
123:Low
124:Medium
125:Medium
126:Medium
127:High
128:High
129:Medium
130:High
131:Low
132:Low
133:High
134:Low
135:High
136:High
137:Medium
138:Medium
139:High
140:Medium
141:Medium
142:High
143:Medium
144:Low
145:High
146:Medium
147:Medium
148:Low
149:Low
150:Low
151:Medium
152:High
153:High
154:High
155:Medium
156:Low
157:High
158:Medium
159:Medium
160:Low
161:Low
162:Medium
163:Low
164:Low
165:Low
166:Medium
167:High
168:Low
169:Medium
170:Medium
171:Low
172:Low
173:Low
174:Low
175:Low
176:Medium
177:Low
178:Medium
179:High
180:Medium
181:Low
182:Medium
183:Low
184:Medium
185:High
186:Medium
187:Low
188:High
189:Medium
190:Low
191:Low
192:Medium
193:Low
194:High
195:Medium
196:High
197:Medium

config.txt

Random Seed <>
Number of worlds <10>
Number of Days <50>
Agent Parameter Keys <Agent Index:Compliance>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <>
Location list filename <>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_files.py

import random
import sys

import numpy as np


def write_agents(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Compliance'
	info_dict['Compliance']=['High','Low','Medium']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)

Stochastic_SEYAR_External_Prevalence

The implementation is similar to that of Scheduled_SIR_External_Prevalence except we use a Stochastic model and here, instead of compliance, we take into account the student’s place of residence.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.5
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	beta=0.001
	return ambient_infection*beta

# Returns additional probability of an agent getting infected by external prevalence of the disease
def external_prevalence(agent, current_time_step):
	if(agent.info['Residence'] == 'Outside'):
		return 0.2
	return 0.0


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0.01
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection())
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.1))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.1))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)
		self.set_external_prevalence_fn(external_prevalence)

agents.txt

1000
Agent Index:Residence
0:Teacher Dorm
1:Dorm B
2:Outside
3:Teacher Dorm
4:Dorm A
5:Teacher Dorm
6:Dorm B
7:Teacher Dorm
8:Teacher Dorm
9:Dorm A
10:Outside
11:Dorm B
12:Dorm A
13:Dorm A
14:Outside
15:Teacher Dorm
16:Outside
17:Dorm B
18:Outside
19:Teacher Dorm
20:Outside
21:Teacher Dorm
22:Dorm A
23:Teacher Dorm
24:Outside
25:Dorm A
26:Dorm A
27:Dorm B
28:Teacher Dorm
29:Dorm B
30:Dorm A
31:Dorm B
32:Teacher Dorm
33:Dorm A
34:Dorm B
35:Dorm B
36:Dorm A
37:Outside
38:Dorm A
39:Dorm B
40:Dorm B
41:Dorm B
42:Dorm A
43:Teacher Dorm
44:Teacher Dorm
45:Dorm A
46:Dorm A
47:Teacher Dorm
48:Dorm B
49:Outside
50:Outside
51:Dorm B
52:Dorm B
53:Dorm B
54:Dorm B
55:Dorm A
56:Dorm A
57:Outside
58:Outside
59:Teacher Dorm
60:Teacher Dorm
61:Dorm B
62:Dorm B
63:Dorm A
64:Outside
65:Dorm B
66:Outside
67:Teacher Dorm
68:Dorm B
69:Outside
70:Dorm B
71:Teacher Dorm
72:Teacher Dorm
73:Dorm B
74:Dorm A
75:Teacher Dorm
76:Outside
77:Outside
78:Outside
79:Dorm A
80:Dorm A
81:Dorm B
82:Dorm A
83:Outside
84:Dorm B
85:Outside
86:Outside
87:Dorm A
88:Teacher Dorm
89:Outside
90:Outside
91:Outside
92:Teacher Dorm
93:Outside
94:Dorm B
95:Dorm A
96:Dorm A
97:Teacher Dorm
98:Teacher Dorm
99:Outside
100:Outside
101:Outside
102:Dorm A
103:Teacher Dorm
104:Teacher Dorm
105:Dorm A
106:Outside
107:Teacher Dorm
108:Teacher Dorm
109:Dorm A
110:Outside
111:Dorm A
112:Dorm A
113:Dorm B
114:Teacher Dorm
115:Outside
116:Dorm A
117:Dorm A
118:Dorm B
119:Dorm A
120:Teacher Dorm
121:Dorm A
122:Outside
123:Dorm B
124:Dorm B
125:Dorm B
126:Outside
127:Dorm A
128:Dorm A
129:Dorm B
130:Dorm A
131:Dorm B
132:Dorm A
133:Outside
134:Dorm B
135:Outside
136:Dorm B
137:Teacher Dorm
138:Outside
139:Dorm B
140:Dorm B
141:Dorm A
142:Teacher Dorm
143:Dorm A
144:Dorm B
145:Teacher Dorm
146:Outside
147:Teacher Dorm
148:Dorm B
149:Dorm A
150:Dorm B
151:Outside
152:Dorm B
153:Outside
154:Outside
155:Dorm A
156:Teacher Dorm
157:Teacher Dorm
158:Dorm A
159:Dorm A
160:Teacher Dorm
161:Teacher Dorm
162:Dorm B
163:Outside
164:Dorm A
165:Teacher Dorm
166:Dorm A
167:Outside
168:Dorm A
169:Dorm A
170:Dorm B
171:Dorm B
172:Dorm B
173:Outside
174:Dorm A
175:Teacher Dorm
176:Dorm B
177:Outside
178:Teacher Dorm
179:Outside
180:Outside
181:Outside
182:Dorm B
183:Teacher Dorm
184:Teacher Dorm
185:Outside
186:Outside
187:Dorm B
188:Teacher Dorm
189:Outside
190:Outside
191:Dorm B
192:Teacher Dorm
193:Dorm B
194:Dorm A
195:Dorm A
196:Dorm A
197:Outside

config.txt

Random Seed <>
Number of worlds <10>
Number of Days <50>
Agent Parameter Keys <Agent Index:Residence>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import random
import sys

import numpy as np


def write_agents(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Residence'
	info_dict['Residence']=['Dorm A','Dorm B','Outside','Teacher Dorm']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')


def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Policy

This subdirectory in Episimmer showcases the mitigation policies that can be enforced. Policies here refer to any kind of method to control the spread of the epidemic.

Current policies in Episimmer include :

1. Lockdown Policy

2. Testing Policy

3. Vaccination Policy

4. Contact Tracing Policy

For more information on these examples refer to Tutorial 3

To run an example :

            cd examples/Policy
            python ../../episimmer/main.py Sample_Campus

---

Contact_Tracing1

This example showcases the first kind of Contact Tracing policy in Episimmer. It first performs testing on random agents and then trace their contacts to lock them down. You can run multiple contact tracing policies concurrently for different types of agents. Since we want mitigation of disease spread, we implement a lockdown policy with contact tracing enabled to not only lock down the contacts of the positive agents but even the positive agents themselves.

---

Generate_policy.py

import random

from episimmer.policy import (contact_tracing_policy, lockdown_policy,
                              testing_policy)


def agents_per_step_fn(cur_time_step):
    return 7

def generate_policy():
    policy_list=[]
    Normal_Test = testing_policy.TestPolicy(agents_per_step_fn)
    Normal_Test.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 50, 5, 2)
    Normal_Test.set_register_agent_testtube_func(Normal_Test.random_testing())

    # Num of timesteps to store agents
    CT_object = contact_tracing_policy.CTPolicy(7, 'Type', ['Teacher'])
    CT_object2 = contact_tracing_policy.CTPolicy(3, 'Type', ['Student'])

    # do lockdown function, Num of days to lockdown based on test result, Contact tracing boolean
    Lockdown_object = lockdown_policy.TestingBasedLockdown(lambda x:True, 2, True)
    policy_list.append(Normal_Test)
    policy_list.append(CT_object)
    policy_list.append(CT_object2)
    policy_list.append(Lockdown_object)

    return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.01
	return ambient_infection*beta


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.01
	return 0


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.7,
							'Infected':0.3,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn, None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Contact_Tracing1'

agents.txt

100
Agent Index:Type
0:Student
1:Teacher
2:Student
3:Student
4:Student
5:Teacher
6:Student
7:Student
8:Teacher
9:Teacher
10:Teacher
11:Teacher
12:Teacher
13:Teacher
14:Student
15:Student
16:Teacher
17:Student
18:Teacher
19:Student
20:Student
21:Student
22:Teacher
23:Student
24:Teacher
25:Student
26:Student
27:Teacher
28:Teacher
29:Teacher
30:Student
31:Teacher
32:Student
33:Student
34:Teacher
35:Teacher
36:Teacher
37:Student
38:Student
39:Teacher
40:Teacher
41:Teacher
42:Student
43:Student
44:Student
45:Student
46:Teacher
47:Teacher
48:Teacher
49:Student
50:Student
51:Student
52:Student
53:Teacher
54:Student
55:Teacher
56:Teacher
57:Student
58:Student
59:Teacher
60:Teacher
61:Teacher
62:Student
63:Teacher
64:Student
65:Student
66:Teacher
67:Student
68:Student
69:Student
70:Teacher
71:Teacher
72:Teacher
73:Teacher
74:Teacher
75:Student
76:Student
77:Student
78:Teacher
79:Student
80:Teacher
81:Teacher
82:Student
83:Teacher
84:Student
85:Student
86:Student
87:Teacher
88:Teacher
89:Teacher
90:Teacher
91:Teacher
92:Student
93:Teacher
94:Student
95:Teacher
96:Student
97:Student
98:Student
99:Student

config.txt

Random Seed <3>
Number of worlds <10>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

empty_event.txt

0
Location Index:Agents

empty_interactions.txt

0
Agent Index:Interacting Agent Index

event_files_list.txt

<one_event.txt>
<empty_event.txt>

generate_agents.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type'
	info_dict['Type']=['Student','Teacher']

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		for j in info_dict.keys():
			f.write(':'+random.choice(info_dict[j]))
		f.write('\n')


write_to_file('agents.txt',100)

generate_agents_csv.py

import random
from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Type']
    writer = DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()
    for i in range(100):
        info_dict={}
        info_dict['Agent Index']=i
        info_dict['Type']=random.choice(['Student','Teacher'])
        writer.writerow(info_dict)

generate_events.py

import random

import numpy as np


def write_to_file(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'
	n=random.randint(10,20)
	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		line=str(random.randint(0,no_locations-1))+':'
		for i in range(random.randint(0,20)):
			line+=str(random.randint(0,no_agents-1))+','
		line+=str(random.randint(0,no_agents-1))
		line+='\n'

		f.write(line)

write_to_file('one_event.txt',10,100)

generate_interactions.py

import random
import sys

import numpy as np


def write_to_file(filename,n,no_contacts):
	#ID enumerates from 0 to n-1
	header='Agent Index:Interacting Agent Index'

	f=open(filename,'w')
	f.write(str(no_contacts)+'\n')
	f.write(header+'\n')

	for i in range(no_contacts):
		agent=random.randint(0,n-1)
		contact_agent=random.randint(0,n-1)
		f.write(str(agent)+':'+str(contact_agent)+'\n')


n = int(sys.argv[1])
num_interactions = int(sys.argv[2])
write_to_file('simple_interactions.txt',n,num_interactions)

generate_locations.py

import random

import numpy as np


def write_to_file(filename,n):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i))
		f.write('\n')


write_to_file('locations.txt',10)

interaction_files_list.txt

<empty_interactions.txt>
<simple_interactions.txt>

locations.txt

10
Location Index
0
1
2
3
4
5
6
7
8
9

one_event.txt

18
Location Index:Agents
3:58,27,92,6,81,24,44,27,26,98,18,5,50,3,82,42
3:99,45,24,34,95,56,37,66,30,97,89,80,78,19,77,67,72,51,93
5:36,91,60,80,47,72,13
3:24,90,23,4,18,53,53,59,15,82,29,42,62,26,11,4,45,95
6:21,92,48,59,5,54,33,4,88,6,40,26,21,94,23,41,14,11
0:14,65
4:19,39,63,32,30,99,21,28,71,81,92,13,73,36,31,32,1
9:71,22,40,35,26
4:75
7:90,81,17,68,56
6:92,78,8,59,70,5,78,82,61,10,65,81,65,86
6:59,63,75,53,32,91,98,51,97,6,61,18,78
9:97
4:64
3:81,52,83,12
7:88,85,82,89,2,22,42,2,23,0,12,56,87
5:49,87,26,99,44,91,24,59,70,3,98,82,3,39,79
6:5,26,81,33,75,51,11,42,22,41,31,15,88,0

simple_interactions.txt

5000
Agent Index:Interacting Agent Index
51:77
12:91
74:5
98:50
36:55
73:58
86:69
74:39
92:35
98:54
27:25
0:31
31:74
75:50
84:38
96:43
91:70
74:41
60:92
90:86
86:73
11:7
64:73
65:78
53:26
54:23
27:77
86:46
41:73
32:66
14:9
68:70
46:79
54:22
68:50
14:61
72:42
42:3
79:97
73:38
28:60
10:59
38:3
36:32
20:9
97:89
82:42
78:79
84:81
54:98
11:87
16:32
66:10
23:16
71:20
43:18
68:9
56:44
19:15
79:11
70:27
15:89
75:7
54:81
26:25
78:10
54:58
41:52
30:91
13:30
67:98
31:22
61:82
55:35
16:24
36:43
72:85
31:21
91:21
95:98
7:49
31:55
53:93
35:55
0:80
74:80
16:69
53:95
35:12
2:36
48:3
74:3
10:23
12:84
96:72
94:60
88:15
94:49
97:32
80:41
54:55
53:15
58:69
74:47
47:7
77:10
45:38
27:85
82:94
93:10
43:23
69:55
75:84
58:14
19:93
10:0
23:92
97:36
34:32
67:76
18:88
27:30
61:28
59:43
37:85
27:63
46:76
73:66
10:0
5:76
22:95
62:75
22:62
49:21
30:9
75:97
70:6
97:39
45:73
58:15
0:77
94:30
9:27
12:89
39:73
65:20
73:87
5:72
61:42
92:60
56:90
86:12
22:80
1:96
93:74
18:66
7:89
14:1
47:23
72:77
58:32
37:31
46:46
79:76
73:32
9:53
94:30
59:56
5:12
99:76
3:54
3:85
19:78
78:11
65:81
51:48
83:61
16:39
36:6
91:87
20:2
29:69
23:53
93:74
23:81
62:18
47:10
37:4
6:57
17:31
92:27
78:2
10:75
5:85
42:65
51:88
16:3
35:93

Contact_Tracing2

This example showcases the second kind of Contact Tracing policy in Episimmer. It first performs testing on random agents and then trace their contacts. The traced contacts are then tested for potential lock down, unlike the first kind where they are directly lock down. Only if the contacts test positive are they lock down for a fixed period of time. Once again, You can run multiple contact tracing policies concurrently for different types of agents.

---

Generate_policy.py

from episimmer.policy import (contact_tracing_policy, lockdown_policy,
                              testing_policy)


def agents_per_step_fn(cur_time_step):
    return 2

def generate_policy():
    policy_list=[]
    Normal_Test1 = testing_policy.TestPolicy(agents_per_step_fn)
    Normal_Test1.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 50, 2, 2)
    Normal_Test1.set_register_agent_testtube_func(Normal_Test1.random_testing())

    Normal_Test2 = testing_policy.TestPolicy(agents_per_step_fn)
    Normal_Test2.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 50, 2, 2)
    Normal_Test2.set_register_agent_testtube_func(Normal_Test2.contact_testing())

    # Num of timesteps to store agents
    CT_object = contact_tracing_policy.CTPolicy(7)


    # do lockdown function, Num of days to lockdown based on test result, Contact tracing boolean
    Lockdown_object = lockdown_policy.TestingBasedLockdown(lambda x: True, 2)
    policy_list.append(Normal_Test1)
    policy_list.append(Normal_Test2)
    policy_list.append(CT_object)
    policy_list.append(Lockdown_object)

    return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.01
	return ambient_infection*beta


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.01
	return 0


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.1,
							'Infected':0.9,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn, None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Contact_Tracing2'

agents.txt

100
Agent Index:Type
0:Student
1:Teacher
2:Student
3:Student
4:Student
5:Teacher
6:Student
7:Student
8:Teacher
9:Teacher
10:Teacher
11:Teacher
12:Teacher
13:Teacher
14:Student
15:Student
16:Teacher
17:Student
18:Teacher
19:Student
20:Student
21:Student
22:Teacher
23:Student
24:Teacher
25:Student
26:Student
27:Teacher
28:Teacher
29:Teacher
30:Student
31:Teacher
32:Student
33:Student
34:Teacher
35:Teacher
36:Teacher
37:Student
38:Student
39:Teacher
40:Teacher
41:Teacher
42:Student
43:Student
44:Student
45:Student
46:Teacher
47:Teacher
48:Teacher
49:Student
50:Student
51:Student
52:Student
53:Teacher
54:Student
55:Teacher
56:Teacher
57:Student
58:Student
59:Teacher
60:Teacher
61:Teacher
62:Student
63:Teacher
64:Student
65:Student
66:Teacher
67:Student
68:Student
69:Student
70:Teacher
71:Teacher
72:Teacher
73:Teacher
74:Teacher
75:Student
76:Student
77:Student
78:Teacher
79:Student
80:Teacher
81:Teacher
82:Student
83:Teacher
84:Student
85:Student
86:Student
87:Teacher
88:Teacher
89:Teacher
90:Teacher
91:Teacher
92:Student
93:Teacher
94:Student
95:Teacher
96:Student
97:Student
98:Student
99:Student

config.txt

Random Seed <>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

locations.txt

10
Location Index
0
1
2
3
4
5
6
7
8
9

one_event.txt

15
Location Index:Agents
4:29,92,49,38,74,60,54,43,20,84,1,35,15,29,95,98,10
2:51,89,60,39
9:26,43,14,82,77,36,47,99,71
0:68,66,3,3,74,12,5,63,13
4:55,20,64,3,49,18
2:44,84,73,99,57,7,18,67,53,7,91,57,33,83,96,77
2:28,77,65,26,83,63,3,67,33,52,57,48,83,78,33
6:32,8,92,16,93,87,86,55,92,61,14,80,98
7:90,31,37,91,93,19,3,67,8,6,71,63,84,11,71,39,24,69,24
8:72,62,74,51,49,7,94,46,83,41,45,88,19,70,4
9:81,84,82,76,32,39,31,62,10,97,62
8:1,67,9
3:93,98,11,42,96,80,85,8,37,48,9,69,70,39,31,92,98,31
6:97,60,55,30,50,42,36,36,37,36,5,23,62,45
6:2,89,12,55,98,19,89,8,86,65

Event Lockdown

This example showcases the event based lockdown with Episimmer. Events are lockdown or restricted from occurring during the simulation. The events attributes are defined in the event files, and we use them in the policy to decide whether to run the event or not. In the generate_policy file, a simple event lockdown procedure is implemented by restricting events that are ‘Low Priority’.

---

Generate_policy.py

from episimmer.policy import lockdown_policy, testing_policy


def generate_policy():
	policy_list=[]

	event_lockdown = lockdown_policy.EventLockdown('Type', ['Low Priority'], lambda x: True)
	policy_list.append(event_lockdown)

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.1
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.6,
							'Infected':0.4,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

10
Agent Index
0
1
2
3
4
5
6
7
8
9

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents:Type>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>
<two_event.txt>
<three_event.txt>
<four_event.txt>

four_event.txt

1
Location Index:Agents:Type
0:7,8,9:High Priority

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents:Type
0:0,1,2,3,4,5,6,7,8,9:Low Priority

three_event.txt

1
Location Index:Agents:Type
0:0,1,2,3:High Priority

two_event.txt

1
Location Index:Agents:Type
0:0,1,2,3,4:Low Priority

Pool Testing

This example showcases the flexibility of Testing available to the user with Episimmer. In the generate_policy file, there is both the Normal testing and Group testing variants. Since we want some mitigation of disease spread, we pair this policy with the lockdown policy by restricting agents that have tested Positive.

More on Testing Policy can be found in Tutorial 3

---

Generate_policy.py

import random

from episimmer.policy import lockdown_policy, testing_policy


def generate_policy():
	policy_list=[]

	# Normal Testing
	# Normal_Test = testing_policy.TestPolicy(lambda x:120)
	# Normal_Test.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 60, 5, 2)
	# Normal_Test.set_register_agent_testtube_func(Normal_Test.random_testing(1,1))
	# policy_list.append(Normal_Test)

	# Group/Pool Testing
	Pool_Testing = testing_policy.TestPolicy(lambda x:150)
	Pool_Testing.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 50, 3, 3)
	Pool_Testing.set_register_agent_testtube_func(Pool_Testing.random_testing(5,2))
	policy_list.append(Pool_Testing)

	ATP = lockdown_policy.TestingBasedLockdown(lambda x:random.random()<0.95,10)
	policy_list.append(ATP)

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Sample Campus

This example shows the user how a college environment can be simulated in Epsimmer. In this environment a college can strategise different lockdown policies to finally implement. Several policies can be imposed such as the number of days classes are to be conducted. In this example used here, you can see various policies that can be imposed such as monday_online only, monday_wednesday_online. This mostly involves combination of various policy based lockdowns within the campus.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def monday_online():
	#This function ensures classes are online on Monday
	policy_list=[]

	def lockdown_fn(time_step):
		if time_step%7 in [0]:
			return True
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

def tuesday_thursday_online():
	#This function ensures classes are online on Tuesday and Thursday
	policy_list=[]

	def lockdown_fn(time_step):
		if time_step%7 in [1,3]:
			return True
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

def monday_tuesday_online():
	#This function ensures classes are online on Monday and Tuesday
	policy_list=[]

	def lockdown_fn(time_step):
		if time_step%7 in [1,3]:
			return True
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

def monday_no_grade1():
	#This function ensures that there are no grade 1 students on Monday
	policy_list=[]

	def lockdown_fn(time_step):
		if time_step%7 in [0]:
			return True
		return False

	policy_list.append(lockdown_policy.AgentLockdown('Grade',['Grade 1'],lockdown_fn))

	return policy_list

def wednesday_no_grade1_grade2():
	#This function ensures that there are no grade 2 students on Wednesday
	policy_list=[]

	def lockdown_fn(time_step):
		if time_step%7 in [2]:
			return True
		return False

	policy_list.append(lockdown_policy.AgentLockdown('Grade',['Grade 1','Grade 2'],lockdown_fn))

	return policy_list

def mw_no_grade1_tf_no_grade3():
	#This function ensures that there are no grade 1 students on Monday,Wednesday and grade 3 students on tuesday,friday
	policy_list=[]

	def lockdown_mw(time_step):
		if time_step%7 in [0,2]:
			return True
		return False

	def lockdown_tf(time_step):
		if time_step%7 in [1,4]:
			return True
		return False

	policy_list.append(lockdown_policy.AgentLockdown('Grade',['Grade 1'],lockdown_mw))
	policy_list.append(lockdown_policy.AgentLockdown('Grade',['Grade 3'],lockdown_tf))

	return policy_list

def mt_no_grade2_wednesday_online():
	#This function ensures that there are no grade 1 students on Monday,tuesday and wednesday is online
	policy_list=[]

	def lockdown_mt(time_step):
		if time_step%7 in [0,1]:
			return True
		return False

	def lockdown_wednesday(time_step):
		if time_step%7 in [2]:
			return True
		return False

	policy_list.append(lockdown_policy.AgentLockdown('Grade',['Grade 1'],lockdown_mt))
	policy_list.append(lockdown_policy.FullLockdown(lockdown_wednesday))

	return policy_list


def generate_policy():
	#return monday_online()
	return tuesday_thursday_online()
	#return monday_tuesday_online()
	#return monday_no_grade1()
	#return wednesday_no_grade1_grade2()
	#return mw_no_grade1_tf_no_grade3()
	return mt_no_grade2_wednesday_online()

UserModel.py

import math

import episimmer.model as model

#User defined functions

def event_contribute_fn(agent,event_info,location,current_time_step):
	#Example 1
	if agent.state=='Symptomatic':
		return 0.7
	elif agent.state=='Asymptomatic':
		return 0.3
	else:
		return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	p=math.tanh(ambient_infection*0.3)
	return p




class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0.01,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection())
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.2))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.2))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

agents.txt

315
Agent Index:Type:Grade
0:Student:Grade 1
1:Student:Grade 1
2:Student:Grade 1
3:Student:Grade 1
4:Student:Grade 1
5:Student:Grade 1
6:Student:Grade 1
7:Student:Grade 1
8:Student:Grade 1
9:Student:Grade 1
10:Student:Grade 1
11:Student:Grade 1
12:Student:Grade 1
13:Student:Grade 1
14:Student:Grade 1
15:Student:Grade 1
16:Student:Grade 1
17:Student:Grade 1
18:Student:Grade 1
19:Student:Grade 1
20:Student:Grade 1
21:Student:Grade 1
22:Student:Grade 1
23:Student:Grade 1
24:Student:Grade 1
25:Student:Grade 1
26:Student:Grade 1
27:Student:Grade 1
28:Student:Grade 1
29:Student:Grade 1
30:Student:Grade 1
31:Student:Grade 1
32:Student:Grade 1
33:Student:Grade 1
34:Student:Grade 1
35:Student:Grade 1
36:Student:Grade 1
37:Student:Grade 1
38:Student:Grade 1
39:Student:Grade 1
40:Student:Grade 1
41:Student:Grade 1
42:Student:Grade 1
43:Student:Grade 1
44:Student:Grade 1
45:Student:Grade 1
46:Student:Grade 1
47:Student:Grade 1
48:Student:Grade 1
49:Student:Grade 1
50:Student:Grade 1
51:Student:Grade 1
52:Student:Grade 1
53:Student:Grade 1
54:Student:Grade 1
55:Student:Grade 1
56:Student:Grade 1
57:Student:Grade 1
58:Student:Grade 1
59:Student:Grade 1
60:Student:Grade 1
61:Student:Grade 1
62:Student:Grade 1
63:Student:Grade 1
64:Student:Grade 1
65:Student:Grade 1
66:Student:Grade 1
67:Student:Grade 1
68:Student:Grade 1
69:Student:Grade 1
70:Student:Grade 1
71:Student:Grade 1
72:Student:Grade 1
73:Student:Grade 1
74:Student:Grade 1
75:Student:Grade 1
76:Student:Grade 1
77:Student:Grade 1
78:Student:Grade 1
79:Student:Grade 1
80:Student:Grade 1
81:Student:Grade 1
82:Student:Grade 1
83:Student:Grade 1
84:Student:Grade 1
85:Student:Grade 1
86:Student:Grade 1
87:Student:Grade 1
88:Student:Grade 1
89:Student:Grade 1
90:Student:Grade 1
91:Student:Grade 1
92:Student:Grade 1
93:Student:Grade 1
94:Student:Grade 1
95:Student:Grade 1
96:Student:Grade 1
97:Student:Grade 1
98:Student:Grade 1
99:Student:Grade 1
100:Student:Grade 2
101:Student:Grade 2
102:Student:Grade 2
103:Student:Grade 2
104:Student:Grade 2
105:Student:Grade 2
106:Student:Grade 2
107:Student:Grade 2
108:Student:Grade 2
109:Student:Grade 2
110:Student:Grade 2
111:Student:Grade 2
112:Student:Grade 2
113:Student:Grade 2
114:Student:Grade 2
115:Student:Grade 2
116:Student:Grade 2
117:Student:Grade 2
118:Student:Grade 2
119:Student:Grade 2
120:Student:Grade 2
121:Student:Grade 2
122:Student:Grade 2
123:Student:Grade 2
124:Student:Grade 2
125:Student:Grade 2
126:Student:Grade 2
127:Student:Grade 2
128:Student:Grade 2
129:Student:Grade 2
130:Student:Grade 2
131:Student:Grade 2
132:Student:Grade 2
133:Student:Grade 2
134:Student:Grade 2
135:Student:Grade 2
136:Student:Grade 2
137:Student:Grade 2
138:Student:Grade 2
139:Student:Grade 2
140:Student:Grade 2
141:Student:Grade 2
142:Student:Grade 2
143:Student:Grade 2
144:Student:Grade 2
145:Student:Grade 2
146:Student:Grade 2
147:Student:Grade 2
148:Student:Grade 2
149:Student:Grade 2
150:Student:Grade 2
151:Student:Grade 2
152:Student:Grade 2
153:Student:Grade 2
154:Student:Grade 2
155:Student:Grade 2
156:Student:Grade 2
157:Student:Grade 2
158:Student:Grade 2
159:Student:Grade 2
160:Student:Grade 2
161:Student:Grade 2
162:Student:Grade 2
163:Student:Grade 2
164:Student:Grade 2
165:Student:Grade 2
166:Student:Grade 2
167:Student:Grade 2
168:Student:Grade 2
169:Student:Grade 2
170:Student:Grade 2
171:Student:Grade 2
172:Student:Grade 2
173:Student:Grade 2
174:Student:Grade 2
175:Student:Grade 2
176:Student:Grade 2
177:Student:Grade 2
178:Student:Grade 2
179:Student:Grade 2
180:Student:Grade 2
181:Student:Grade 2
182:Student:Grade 2
183:Student:Grade 2
184:Student:Grade 2
185:Student:Grade 2
186:Student:Grade 2
187:Student:Grade 2
188:Student:Grade 2
189:Student:Grade 2
190:Student:Grade 2
191:Student:Grade 2
192:Student:Grade 2
193:Student:Grade 2
194:Student:Grade 2
195:Student:Grade 2
196:Student:Grade 2
197:Student:Grade 2

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index:Type:Grade>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<monday.txt>
<tuesday.txt>
<wednesday.txt>
<thursday.txt>
<friday.txt>
<saturday.txt>
<sunday.txt>

friday.txt

3
Location Index:Agents
0:1,2,4,7,8,9,15,16,17,18,20,21,22,23,24,26,30,33,37,43,49,55,57,59,62,63,65,66,70,74,78,79,81,82,84,85,86,91,92,304
1:103,110,115,116,118,119,121,124,126,127,128,130,132,134,135,137,138,139,140,143,145,147,150,152,160,162,163,166,168,170,171,172,173,174,175,177,181,184,187,190,191,198,309
2:207,210,215,217,218,219,220,221,222,224,225,231,232,240,242,245,250,252,253,255,260,262,263,264,265,266,268,269,271,272,273,275,276,279,280,284,286,293,297,298,314

generate_files.py

import random
from itertools import combinations

import numpy as np


def write_to_file():
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Agent Index:Type:Grade'

	f=open('locations.txt','w')
	f.write(str(3)+'\n')
	f.write('Location Index'+'\n')
	for i in range(3):
		f.write(str(i)+'\n')

	f=open('agents.txt','w')
	f.write(str(315)+'\n')
	f.write(header+'\n')

	classes=[]
	for i in range(15):
		classes.append([])

	for i in range(300):
		f.write(str(i))
		grade=int(i/100 )+ 1
		f.write(':Student:Grade '+str(grade))
		class_list=random.choice(list(combinations([5*(grade-1),5*(grade-1)+1,5*(grade-1)+2,5*(grade-1)+3,5*(grade-1)+4],random.choice([1,2,3]))))
		for j in class_list:
			classes[j].append(i)
		f.write('\n')

	for i in range(15):
		f.write(str(i + 300))
		grade=int(i/5 )+ 1
		f.write(':Teacher:Grade '+str(grade))
		classes[i].append(300+i)
		f.write('\n')

	event_header='Location Index:Agents'
	for filename in ['saturday.txt','sunday.txt']:
		f=open(filename,'w')
		f.write(str(0)+'\n')
		f.write(event_header+'\n')
	for index,filename in enumerate(['monday.txt','tuesday.txt','wednesday.txt','thursday.txt','friday.txt']):
		f=open(filename,'w')
		f.write(str(3)+'\n')
		f.write(event_header+'\n')
		for j in range(3):
			line=str(j)+':'
			for agent_index in classes[index+5*j]:
				line+=str(agent_index)+','
			f.write(line[:-1]+'\n')


write_to_file()

locations.txt

3
Location Index
0
1
2

monday.txt

3
Location Index:Agents
0:2,4,10,11,12,14,15,19,20,23,25,27,33,34,35,36,42,44,46,48,49,55,60,62,64,69,72,77,81,83,84,87,98,300
1:103,104,106,112,113,116,117,119,120,123,124,125,126,130,131,132,133,134,136,137,142,143,144,145,146,147,149,150,151,153,154,159,160,165,166,167,170,181,183,186,187,189,199,305
2:203,204,207,210,212,213,219,220,221,222,223,225,226,228,232,233,235,237,239,247,252,253,254,258,270,274,278,280,281,283,285,286,288,290,294,299,310

saturday.txt

0
Location Index:Agents

sunday.txt

0
Location Index:Agents

thursday.txt

3
Location Index:Agents
0:0,5,8,13,17,19,31,33,34,36,39,42,45,47,48,53,54,55,56,58,61,68,69,71,74,76,78,79,80,81,83,86,88,89,90,93,94,95,96,97,303
1:100,101,103,107,108,109,113,119,120,121,123,128,132,135,137,141,143,145,150,164,165,167,170,171,173,175,179,180,182,184,185,186,187,188,189,191,192,193,194,195,196,197,308
2:205,206,209,211,215,217,220,223,228,236,237,243,244,245,246,248,251,254,255,257,259,260,265,266,273,276,282,284,285,287,291,293,294,296,313

tuesday.txt

3
Location Index:Agents
0:0,2,3,4,5,6,7,8,10,13,15,16,21,27,28,29,30,31,32,35,37,38,40,47,52,60,65,70,71,72,76,77,79,85,87,93,94,95,98,301
1:100,101,102,105,107,108,109,113,114,116,117,118,120,122,124,126,138,142,148,154,156,157,158,161,162,166,168,169,174,175,180,181,182,184,190,192,193,196,306
2:201,203,206,208,209,212,213,214,216,223,226,227,228,229,230,232,235,236,237,241,242,243,250,256,258,261,262,265,272,277,278,282,284,289,290,291,293,311

wednesday.txt

3
Location Index:Agents
0:0,7,9,10,12,14,17,19,20,24,25,27,30,31,35,36,39,41,42,43,44,45,47,48,49,50,51,54,56,62,63,66,67,70,71,73,75,78,83,84,85,86,89,91,92,95,98,99,302
1:101,105,107,110,111,112,117,122,123,128,129,130,131,134,140,146,147,148,152,154,155,157,158,161,162,163,165,168,171,174,176,178,179,180,185,188,190,191,192,194,195,199,307
2:200,201,202,205,207,208,211,212,215,216,217,226,227,229,230,234,236,238,241,242,243,244,246,247,249,250,251,253,254,255,257,258,262,263,266,267,268,269,271,274,275,277,280,282,283,285,286,287,292,295,296,299,312

Symptomatic Testing

This example shows the use of the Symptomatic Testing. It gives the agents of the simulation a chance to test themselves as they develop visible conditions of having the disease. Be sure to set the symptomatic states in the UserModel.py file for this to work. It will be ignored if the correct states are not passed.

This example shows this use case with the SEYAR model representing the Susceptible, Exposed, Symptomatic, Asymptomatic and Recovered states. Our symptomatic state is “Symptomatic” state in the disease model. In the testing policy, while registering the agent to a testtube, you can set the only_symptomatic parameter to true. This will choose only the agents that are part of the symptomatic states for testing.

Be careful while using this method because you should not pass the disease states that do not represent agents with visible symptoms. For example, asymptomatic agents do not show any symptoms of the disease and must not be passed in this list.

---

Generate_policy.py

import random

from episimmer.policy import lockdown_policy, testing_policy


def generate_policy():
	policy_list=[]

	# Testing only symptomatic agents.
	Normal_Testing = testing_policy.TestPolicy(lambda x:5)
	Normal_Testing.add_machine('Simple_Machine', 200, 0.0, 0.0, 0, 50, 3, 1)
	Normal_Testing.set_register_agent_testtube_func(Normal_Testing.random_testing(only_symptomatic=True))
	policy_list.append(Normal_Testing)

	ATP = lockdown_policy.TestingBasedLockdown(lambda x:random.random()<0.95,10)
	policy_list.append(ATP)

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Symptomatic':
		return 1
	elif agent.state=='Asymptomatic':
		return 0.5
	return 0

def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	#Example 1
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Exposed','Asymptomatic','Symptomatic','Recovered']
		infected_states=['Asymptomatic','Symptomatic']
		state_proportion={
							'Susceptible':0.99,
							'Exposed':0,
							'Recovered':0,
							'Asymptomatic':0,
							'Symptomatic':0.01
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Exposed', self.p_infection())
		self.set_transition('Exposed', 'Symptomatic', self.p_standard(0.15))
		self.set_transition('Exposed', 'Asymptomatic', self.p_standard(0.2))
		self.set_transition('Symptomatic', 'Recovered', self.p_standard(0.1))
		self.set_transition('Asymptomatic', 'Recovered', self.p_standard(0.1))

		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)
		self.set_symptomatic_states(['Symptomatic'])

agents.txt

1000
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197

config.txt

Random Seed <3>
Number of worlds <3>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Vaccination

This example showcases the flexibility of Vaccination available to the user with Episimmer. In the generate_policy file, we see we can implement vaccination by single dose or multi dose vaccination.

More on Vaccination Policy can be found in Tutorial 3

---

Generate_policy.py

from episimmer.policy import vaccination_policy


def agents_per_step_fn(time_step):

	n=100

	return n

def generate_policy():
	policy_list=[]

	# Single Dose Vaccination
	vp1= vaccination_policy.VaccinationPolicy(agents_per_step_fn)
	vaccines1 = {
		'cov_single_dose': {'cost': 40, 'count': 20, 'efficacy': 0.9, 'decay': 40},
		'cov_single_dose2': {'cost': 50, 'count': 15, 'efficacy': 0.5, 'decay': 30},
	}
	vp1.add_vaccines(vaccines1, 'Single')
	vp1.set_register_agent_vaccine_func(vp1.random_vaccination())

	# Multi Dose Vaccination
	vp2= vaccination_policy.VaccinationPolicy(agents_per_step_fn)
	vaccines2 = {
		'cov_multi_dose': {'cost': 40, 'count': 25, 'efficacy': 0.4, 'decay': [15, 14, 8], 'dose': 3, 'interval': [3, 2]},
		'cov_multi_dose2': {'cost': 30, 'count': 40, 'efficacy': 0.7, 'decay': [20, 25, 17, 5], 'dose': 4, 'interval': [12, 26, 14]},
		'cov_multi_dose3': {'cost': 30, 'count': 15, 'efficacy': 0.7, 'decay': [8], 'dose': 1, 'interval': []}
	}
	vp2.add_vaccines(vaccines2, 'Multi')
	vp2.set_register_agent_vaccine_func(vp2.multi_dose_vaccination())


	policy_list.append(vp1)
	policy_list.append(vp2)

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
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agents.txt

1000
Agent Index
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config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

Vulnerability_Detection

Vulnerabilities in Episimmer refers to the weakest links in the simulation environment. These vulnerabilities can arise from agents, locations, events and interactions. We shall specifically focus on agent based vulnerabilities.

Agent based vulnerabilities can be broadly classified into two groups :

1. Vulnerable agents : Agents who are most likely to reach a certain disease state. For example ‘agents most likely to die’ or ‘agents most likely to get infected’ or ‘agents most likely to be hospitalised’.

2. Agent vulnerability : Agents on removal or constrained for a duration of time from the ecosystem, lead to the largest reduction in risk or size of the epidemic. This is an optimization problem using a cost function that can encode priorities.

To run an example :

            cd examples/Policy
            python ../../episimmer/main.py Simple_Vulnerable_Agents -vul

---

Bandit_Algos_Agent_Vulnerability

An example of finding Agent vulnerabilities in the system. We first select an agent for removal based on either epsilon greedy or the UCB1 algorithm, then we run the simulation.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

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config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "mode":"UCB"
}

vd_config.txt

VD Target <Agent>
VD Algorithm <BanditAlgos>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Chunk_Agent_Vulnerability

An example of finding Agent vulnerabilities in the system. We first randomly select an agent, remove him from the environment and run the simulation. For higher stability, chunks of the entire simulation are considered instead of the entire simulation. The score of the agent is based on the severity of outbreak after the agent’s removal.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "num_agents_to_remove":1,
    "chunk_len_range":[1,30]
}

vd_config.txt

VD Target <Agent>
VD Algorithm <ChunkAgentVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Complex Graph

Showcases the Agent Vulnerability functionality with a complex graph.

---

Generate_policy.py

def generate_policy():
	policy_list=[]

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.2
	return 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.8,
							'Infected':0.2,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.02))


		self.name='Stochastic SIR on complete graph'

agents.txt

13
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

interaction_files_list.txt

<interactions_list.txt>

interactions_list.txt

50
Agent Index:Interacting Agent Index
0:1
1:0
0:2
2:0
0:3
3:0
1:2
2:1
1:3
3:1
2:3
3:2
0:4
4:0
0:5
5:0
0:6
6:0
0:7
7:0
4:5
5:4
4:6
6:4
4:7
7:4
5:6
6:5
5:7
7:5
6:7
7:6
6:8
8:6
7:8
8:7
8:9
9:8
9:10
10:9
9:11
11:9
9:12
12:9
10:11
11:10
10:12
12:10
11:12
12:11

locations.txt

1
Location Index
0

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "num_agents_to_remove":1
}

vd_config.txt

VD Target <Agent>
VD Algorithm <SimpleAgentVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Early_Vulnerable_Agents

An example of finding Vulnerable agents in the system. We run the simulation multiple times and score the agent based on the number of times he was infected and also given a higher score if he was infected earlier.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100
}

vd_config.txt

VD Target <Agent>
VD Algorithm <EarlyVulnerableAgent>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Multi_Cycle_Graph

Showcases the Agent Vulnerability functionality with the multi cycle graph.

---

Generate_policy.py

def generate_policy():
	policy_list=[]

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.4
	return 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.9,
							'Infected':0.1,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.name='Stochastic SIR on complete graph'

agents.txt

100
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

generate_rings.py

import csv


def write_interactions(filename,no_agents,num_rings):
	agent_set=set()

	if no_agents%num_rings!=0:
		raise ValueError('Number of agents is not divisible by number of rings')
	num_ring_agents = no_agents//num_rings

	for ring in range(num_rings):
		for agent_index in range(num_ring_agents):
			agent_set.add((agent_index + ring*num_ring_agents, (agent_index+1)%num_ring_agents+ ring*num_ring_agents))
			agent_set.add(((agent_index+1)%num_ring_agents+ ring*num_ring_agents, agent_index + ring*num_ring_agents))

		agent_set.add((ring*num_ring_agents,((ring+1)*num_ring_agents)%no_agents))
		agent_set.add((((ring+1)*num_ring_agents)%no_agents, ring*num_ring_agents))

	with open(filename, 'w', newline='') as file:
		fieldnames = ['Agent Index','Interacting Agent Index']
		writer = csv.DictWriter(file, fieldnames=fieldnames)
		writer.writeheader()
		for x,y in agent_set:
			writer.writerow({'Agent Index':x,'Interacting Agent Index':y})




write_interactions('interactions_list.csv', 30, 3)

interaction_files_list.txt

<interactions_list.csv>

interactions_list.csv

Agent Index,Interacting Agent Index
27,26
29,28
10,11
22,23
0,20
5,6
28,29
9,8
23,22
2,1
14,13
8,9
15,16
20,29
18,19
0,10
17,16
26,25
1,2
16,17
9,0
11,10
6,7
13,12
19,18
21,20
12,13
20,21
7,6
20,0
25,26
16,15
24,23
3,4
10,0
19,10
28,27
27,28
22,21
3,2
5,4
4,5
10,19
18,17
20,10
2,3
14,15
8,7
12,11
1,0
15,14
11,12
23,24
6,5
0,1
17,18
26,27
13,14
21,22
25,24
29,20
24,25
10,20
4,3
0,9
7,8

locations.txt

1
Location Index
0

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "num_agents_to_remove":1,
    "chunk_len_range":[1,30]
}

vd_config.txt

VD Target <Agent>
VD Algorithm <ChunkAgentVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Simple_Agent_Vulnerability

An example of finding Agent vulnerabilities in the system. We first randomly select an agent, remove him from the environment and run the simulation. The score of the agent is based on the severity of outbreak after the agent’s removal.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "num_agents_to_remove":1
}

vd_config.txt

VD Target <Agent>
VD Algorithm <SimpleAgentVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Simple Event Vulnerability

An example of finding Event vulnerabilities in the system. We first randomly select an event, put the rest of the events under lockdown and run the simulation with only that event. The score of the event is based on the severity of outbreak.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent, event_info, location, current_time_step):
    if agent.state == 'Infected':
        return 1
    return 0


def event_receive_fn(agent, ambient_infection, event_info, location, current_time_step):
    beta = 0.3
    return ambient_infection * beta


def probability_of_infection_fn(p_infected_states_list, contact_agent, c_dict, current_time_step):
    if contact_agent.state == 'Infected':
        return 0.001
    return 0


class UserModel(model.StochasticModel):
    def __init__(self):
        individual_types = ['Susceptible', 'Infected', 'Recovered']
        infected_states = ['Infected']
        state_proportion = {
            'Susceptible': 0.8,
            'Infected': 0.2,
            'Recovered': 0
        }
        model.StochasticModel.__init__(self, individual_types, infected_states, state_proportion)
        self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn, None))
        self.set_transition('Infected', 'Recovered', self.p_standard(0.2))

        self.set_event_contribution_fn(event_contribute_fn)
        self.set_event_receive_fn(event_receive_fn)

        self.name = 'Stochastic SIR'

agents.txt

20
Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interactions_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Id:Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <one_time_event.txt>

event_files_list.txt

<one_event.txt>
<two_event.txt>
<three_event.txt>
<four_event.txt>
<five_event.txt>
<six_event.txt>

five_event.txt

2
Id:Location Index:Agents
6:2:2,5,7,0
7:2:14,16,4,11,10,12,6

four_event.txt

2
Id:Location Index:Agents
4:1:12,17,9,7,19,11,4,1,13,3,16,2,8,10
5:0:14,2,9,10,4,19,3,18,11,13,15,16,7,5,6,17,0

generate_files.py

import sys
from csv import DictWriter
from random import randint, sample


def write_agents(filename, no_agents):
    header = 'Agent Index'

    with open(filename, 'w') as f:
        f.write(f'{no_agents}\n')
        f.write(f'{header}\n')

        for i in range(no_agents):
            f.write(f'{i}\n')


def write_locations(filename, no_locations):
    header = 'Location Index'

    with open(filename, 'w') as f:
        f.write(f'{no_locations}\n')
        f.write(f'{header}\n')

        for i in range(no_locations):
            f.write(f'{i}\n')


def write_interactions(filename, no_agents, no_interactions):
    with open(filename, 'w', newline='') as file:
        fieldnames = ['Agent Index', 'Interacting Agent Index']
        writer = DictWriter(file, fieldnames=fieldnames)

        writer.writeheader()
        for i in range(no_interactions):
            agent = randint(0, no_agents - 1)
            contact_agent = randint(0, no_agents - 1)
            if agent != contact_agent:
                writer.writerow({'Agent Index': agent, 'Interacting Agent Index': contact_agent})
                writer.writerow({'Agent Index': contact_agent, 'Interacting Agent Index': agent})


def write_events(files, no_locations, no_agents):
    header = 'Id:Location Index:Agents'

    event_id = 0
    for file in files:
        with open(file, 'w') as f:
            n = randint(1, 3)
            f.write(f'{n}\n')
            f.write(f'{header}\n')

            for _ in range(n):
                line = f'{event_id}:'
                event_id += 1
                line += f'{randint(0, no_locations - 1)}:'
                line += ','.join(str(agent) for agent in sample(range(no_agents), randint(1, 20)))
                f.write(line + '\n')


def main():
    no_agents, no_locations = int(sys.argv[1]), int(sys.argv[2])
    event_files = ['one_event.txt', 'two_event.txt', 'three_event.txt', 'four_event.txt', 'five_event.txt',
                   'six_event.txt']

    write_agents('agents.txt', no_agents)
    write_locations('locations.txt', no_locations)
    write_interactions('interactions.csv', no_agents, 100)
    write_events(event_files, no_locations, no_agents)


if __name__ == '__main__':
    main()

interactions.csv

Agent Index,Interacting Agent Index
1,2
2,1
15,14
14,15
18,19
19,18
11,19
19,11
14,1
1,14
17,10
10,17
0,11
11,0
6,7
7,6
9,6
6,9
15,10
10,15
5,17
17,5
14,5
5,14
5,12
12,5
1,18
18,1
11,10
10,11
6,2
2,6
9,18
18,9
0,7
7,0
19,17
17,19
7,19
19,7
19,17
17,19
10,9
9,10
10,11
11,10
11,16
16,11
9,6
6,9
14,2
2,14
5,16
16,5
1,16
16,1
18,4
4,18
13,3
3,13
10,3
3,10
19,16
16,19
14,8
8,14
8,9
9,8
10,1
1,10
13,19
19,13
6,0
0,6
0,16
16,0
4,19
19,4
8,4
4,8
5,15
15,5
6,18
18,6
2,5
5,2
5,15
15,5
13,19
19,13
17,15
15,17
9,17
17,9
8,19
19,8
8,18
18,8
0,3
3,0
8,9
9,8
5,7
7,5
13,8
8,13
0,9
9,0
12,4
4,12
18,1
1,18
1,18
18,1
12,3
3,12
2,4
4,2
6,0
0,6
0,1
1,0
2,18
18,2
13,10
10,13
4,2
2,4
6,2
2,6
6,2
2,6
12,5
5,12
10,9
9,10
2,17
17,2
14,9
9,14
3,11
11,3
7,19
19,7
0,17
17,0
18,9
9,18
8,0
0,8
1,16
16,1
3,5
5,3
2,4
4,2
18,8
8,18
14,18
18,14
8,10
10,8
15,11
11,15
11,18
18,11
10,0
0,10
7,5
5,7
12,4
4,12
6,10
10,6
5,19
19,5
4,9
9,4
3,8
8,3
13,11
11,13
2,13
13,2
18,6
6,18
14,0
0,14
2,6
6,2
15,3
3,15
1,14
14,1

interactions_files_list.txt

<interactions.csv>

locations.txt

3
Location Index
0
1
2

one_event.txt

1
Id:Location Index:Agents
0:0:2,5,18,16,13,17,11,15,12,14

one_time_event.txt

1
Time Step:Id:Location Index:Agents
1,3:10:0:0,2,16

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":1000,
    "event_identifier":"Id"
}

six_event.txt

2
Id:Location Index:Agents
8:1:7,9,5,10,11,12,8,13,0,1,17,2,4,3,18,14,6,15,16
9:0:0

three_event.txt

2
Id:Location Index:Agents
2:1:6,5,11,7,0,2,16
3:2:18,9,13,7,15,2,16,4,1

two_event.txt

1
Id:Location Index:Agents
1:2:18,5,3,12,7,2,9,1,0,13,19,16,15,8,6,17,14

vd_config.txt

VD Target <Event>
VD Algorithm <SimpleEventVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Simple_Vulnerable_Agents

An example of finding Vulnerable agents in the system. We run the simulation multiple times and score the agent based on the number of times he was infected.

---

Generate_policy.py

from episimmer.policy import lockdown_policy


def generate_policy():
	policy_list=[]

	def lockdown_fn(time_step):
		return False

	policy_list.append(lockdown_policy.FullLockdown(lockdown_fn))

	return policy_list

UserModel.py

import episimmer.model as model


def event_contribute_fn(agent,event_info,location,current_time_step):
	if agent.state=='Infected':
		return 1
	return 0


def event_receive_fn(agent,ambient_infection,event_info,location,current_time_step):
	beta=0.001
	return ambient_infection*beta


class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.99,
							'Infected':0.01,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection())
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.set_event_contribution_fn(event_contribute_fn)
		self.set_event_receive_fn(event_receive_fn)

		self.name='Stochastic SIR'

agents.csv

Agent Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <30>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.csv>
Interaction Info Keys <>
Interaction Files list filename <>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <Location Index:Agents>
Event Files list filename <event_files_list.txt>
One Time Event filename <>

event_files_list.txt

<one_event.txt>

generate_files.py

import sys

import numpy as np


def write_agents(filename,n):
	header='Agent Index'

	f=open(filename,'w')
	f.write(str(n)+'\n')
	f.write(header+'\n')

	for i in range(n):
		f.write(str(i)+'\n')

def write_events(filename,no_locations,no_agents):
	info_dict={}
	#ID enumerates from 0 to n-1
	header='Location Index:Agents'

	f=open(filename,'w')
	f.write(str(1)+'\n')
	f.write(header+'\n')

	line=str(0)+':'
	for i in range(no_agents):
		line+=str(i)
		if i!=no_agents-1:
			line+=','

	f.write(line)

number_of_agents=int(sys.argv[1])
write_agents('agents.txt',number_of_agents)
write_events('one_event.txt',1,number_of_agents)

generate_files_csv.py

from csv import DictWriter

with open('agents.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	writer.writerow({'Agent Index': i})

generate_interactions.py

from csv import DictWriter

with open('interactions.csv', 'w', newline='') as file:
    fieldnames = ['Agent Index','Interacting Agent']
    writer = DictWriter(file, fieldnames=fieldnames)

    writer.writeheader()
    for i in range(1000):
    	if i!=10:
    		writer.writerow({'Agent Index': i,'Interacting Agent':i+1})

locations.txt

1
Location Index
0

one_event.txt

1
Location Index:Agents
0:0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552,553,554,555,556,557,558,559,560,561,562,563,564,565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613,614,615,616,617,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648,649,650,651,652,653,654,655,656,657,658,659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701,702,703,704,705,706,707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735,736,737,738,739,740,741,742,743,744,745,746,747,748,749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773,774,775,776,777,778,779,780,781,782,783,784,785,786,787,788,789,790,791,792,793,794,795,796,797,798,799,800,801,802,803,804,805,806,807,808,809,810,811,812,813,814,815,816,817,818,819,820,821,822,823,824,825,826,827,828,829,830,831,832,833,834,835,836,837,838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872,873,874,875,876,877,878,879,880,881,882,883,884,885,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980,981,982,983,984,985,986,987,988,989,990,991,992,993,994,995,996,997,998,999

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100
}

vd_config.txt

VD Target <Agent>
VD Algorithm <SimpleVulnerableAgent>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>

Star_Graph

Showcases the Agent Vulnerability functionality with the star graph.

---

Generate_policy.py

def generate_policy():
	policy_list=[]

	return policy_list

UserModel.py

import episimmer.model as model


def probability_of_infection_fn(p_infected_states_list,contact_agent,c_dict,current_time_step):
	if contact_agent.state=='Infected':
		return 0.4
	return 0

class UserModel(model.StochasticModel):
	def __init__(self):
		individual_types=['Susceptible','Infected','Recovered']
		infected_states=['Infected']
		state_proportion={
							'Susceptible':0.5,
							'Infected':0.5,
							'Recovered':0
						}
		model.StochasticModel.__init__(self,individual_types,infected_states,state_proportion)
		self.set_transition('Susceptible', 'Infected', self.p_infection(probability_of_infection_fn,None))
		self.set_transition('Infected', 'Recovered', self.p_standard(0.2))


		self.name='Stochastic SIR on complete graph'

agents.txt

10
Agent Index
0
1
2
3
4
5
6
7
8
9

config.txt

Random Seed <3>
Number of worlds <1>
Number of Days <10>
Agent Parameter Keys <Agent Index>
Agent list filename <agents.txt>
Interaction Info Keys <Agent Index:Interacting Agent Index>
Interaction Files list filename <interaction_files_list.txt>
Probabilistic Interaction Files list filename <>
Location Parameter Keys <Location Index>
Location list filename <locations.txt>
Event Parameter Keys <>
Event Files list filename <>
One Time Event filename <>

interaction_files_list.txt

<interactions_list.txt>

interactions_list.txt

12
Agent Index:Interacting Agent Index
0:1
1:0
0:2
2:0
0:3
3:0
0:4
4:0
0:5
5:0
0:6
6:0

locations.txt

1
Location Index
0

parameter.json

{
    "states":["Infected","Recovered"],
    "num_runs":100,
    "num_agents_to_remove":1,
    "chunk_len_range":[1,30]
}

vd_config.txt

VD Target <Agent>
VD Algorithm <ChunkAgentVulnerability>
Algorithm Parameter File <parameter.json>
Pre Processing <>
Post Processing <>
Output Mode <Default>