def test_basic_simulation_with_dampening(data_api):
network, _ = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition",
"compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
data_api.read_table("human/infectious-compartments",
"infectious-compartments"),
data_api.read_table("human/infection-probability",
"infection-probability"),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
data_api.read_table("human/movement-multipliers",
"movement-multipliers"),
)
df, issues = np.basicSimulationInternalAgeStructure(
network, {"S08000016": {
"[17,70)": 10.0
}}, numpy.random.default_rng(123))
result = calculateInfectiousOverTime(df, network.infectiousStates)
_assert_baseline(result)
assert not issues
def test_basic_simulation_stochastic(data_api_stochastic):
network, _ = np.createNetworkOfPopulation(
data_api_stochastic.read_table("human/compartment-transition",
"compartment-transition"),
data_api_stochastic.read_table("human/population", "population"),
data_api_stochastic.read_table("human/commutes", "commutes"),
data_api_stochastic.read_table("human/mixing-matrix", "mixing-matrix"),
data_api_stochastic.read_table("human/infectious-compartments",
"infectious-compartments"),
data_api_stochastic.read_table("human/infection-probability",
"infection-probability"),
data_api_stochastic.read_table("human/initial-infections",
"initial-infections"),
data_api_stochastic.read_table("human/trials", "trials"),
data_api_stochastic.read_table("human/start-end-date",
"start-end-date"),
data_api_stochastic.read_table("human/movement-multipliers",
"movement-multipliers"),
data_api_stochastic.read_table("human/stochastic-mode",
"stochastic-mode"),
)
seed = loaders.readRandomSeed(
data_api_stochastic.read_table("human/random-seed", "random-seed"))
result, issues = np.basicSimulationInternalAgeStructure(
network, {"S08000016": {
"[17,70)": 10
}}, numpy.random.default_rng(seed))
_assert_baseline_dataframe(result)
assert not issues
def test_basicSimulationInternalAgeStructure_no_infection_prob(data_api):
network = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition"),
data_api.read_table("human/population"),
data_api.read_table("human/commutes"),
data_api.read_table("human/mixing-matrix"),
data_api.read_table("human/infectious-compartments"),
pd.DataFrame([{
"Time": 0,
"Value": 0.0
}]),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
)
susceptibles = 0.0
for region in network.initialState.values():
for (age, state) in region.keys():
if state == "S":
susceptibles += region[(age, state)]
people_to_infect = 30
result = np.basicSimulationInternalAgeStructure(
network, 50, {"S08000024": {
"[0,17)": people_to_infect
}})
new_susceptibles = result[(result.time == result.time.max())
& (result.state == "S")].total.sum()
assert new_susceptibles + people_to_infect == susceptibles
def test_basic_simulation_100_runs(data_api):
network, _ = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition",
"compartment-transition"),
data_api.read_table("human/population", "population"),
data_api.read_table("human/commutes", "commutes"),
data_api.read_table("human/mixing-matrix", "mixing-matrix"),
data_api.read_table("human/infectious-compartments",
"infectious-compartments"),
data_api.read_table("human/infection-probability",
"infection-probability"),
data_api.read_table("human/initial-infections", "initial-infections"),
data_api.read_table("human/trials", "trials"),
data_api.read_table("human/start-end-date", "start-end-date"),
)
runs = []
rand = random.Random(1)
issues = []
for _ in range(100):
regions = rand.choices(list(network.graph.nodes()), k=1)
assert network.initialState[regions[0]][("[17,70)", "E")] == 0
df, new_issues = np.basicSimulationInternalAgeStructure(
network, {regions[0]: {
"[17,70)": 10.0
}}, numpy.random.default_rng(123))
result = calculateInfectiousOverTime(df, network.infectiousStates)
result.pop(
) # TODO: due to historical reasons we have to ignore the last entry
runs.append(result)
issues.extend(new_issues)
result = common.generateMeanPlot(runs)
_assert_baseline(result)
assert not issues
def test_basicSimulationInternalAgeStructure_invariants(
age_transitions,
demographics,
commute_moves,
compartment_names,
age_infection_matrix,
num_infected,
generic_infection,
seed,
):
age_to_trans = np.setUpParametersAges(
loaders.readParametersAgeStructured(age_transitions))
population = loaders.readPopulationAgeStructured(demographics)
graph = loaders.genGraphFromContactFile(commute_moves)
states = np.setupInternalPopulations(graph, compartment_names,
list(age_to_trans.keys()), population)
old_graph = copy.deepcopy(graph)
old_age_to_trans = copy.deepcopy(age_to_trans)
initial_population = sum(_count_people_per_region(
states[0])) + num_infected
np.basicSimulationInternalAgeStructure(
rand=random.Random(seed),
graph=graph,
numInfected=num_infected,
timeHorizon=50,
genericInfection=generic_infection,
ageInfectionMatrix=age_infection_matrix,
diseaseProgressionProbs=age_to_trans,
dictOfStates=states,
)
# population remains constant
assert all([
sum(_count_people_per_region(state)) == pytest.approx(
initial_population) for state in states.values()
])
# the graph is unchanged
assert nx.is_isomorphic(old_graph, graph)
# infection matrix is unchanged
assert age_to_trans == old_age_to_trans
def test_basicSimulationInternalAgeStructure_no_movement_of_people_invariants(
data_api, region, num_infected):
network = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition"),
data_api.read_table("human/population"),
data_api.read_table("human/commutes"),
data_api.read_table("human/mixing-matrix"),
data_api.read_table("human/infectious-compartments"),
data_api.read_table("human/infection-probability"),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
pd.DataFrame([{
"Time": 0,
"Movement_Multiplier": 0.0,
"Contact_Multiplier": 1.0
}]),
)
initial_population = sum(_count_people_per_region(network.initialState))
old_network = copy.deepcopy(network)
result = np.basicSimulationInternalAgeStructure(
network, 50, {region: {
"[0,17)": num_infected
}})
# population remains constant
populations = result.groupby("time").total.sum()
assert all([
total == pytest.approx(initial_population)
for node, total in populations.to_dict().items()
])
# the graph is unchanged
assert nx.is_isomorphic(old_network.graph, network.graph)
# infection matrix is unchanged
assert list(network.mixingMatrix) == list(old_network.mixingMatrix)
for a in network.mixingMatrix:
assert list(network.mixingMatrix[a]) == list(
old_network.mixingMatrix[a])
for b in network.mixingMatrix[a]:
assert network.mixingMatrix[a][b] == old_network.mixingMatrix[a][b]
# no spread across regions
assert result[(result.node != region) & (
result.state.isin(network.infectiousStates))].total.sum() == 0.0
def test_basicSimulationInternalAgeStructure_no_infection_prob_before_time_25(
data_api):
def count_susceptibles(state):
susceptibles = 0.0
for region in state.values():
for (age, state) in region.keys():
if state == "S":
susceptibles += region[(age, state)]
return susceptibles
network = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition"),
data_api.read_table("human/population"),
data_api.read_table("human/commutes"),
data_api.read_table("human/mixing-matrix"),
data_api.read_table("human/infectious-compartments"),
pd.DataFrame([{
"Time": 0,
"Value": 0.0
}, {
"Time": 25,
"Value": 1.0
}]),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
)
people_to_infect = 30
susceptibles = count_susceptibles(network.initialState) - people_to_infect
result = np.basicSimulationInternalAgeStructure(
network, 50, {"S08000024": {
"[0,17)": people_to_infect
}})
# no infection before time 25
for total in result[(result.time < 25) & (result.state == "S")].groupby(
"time").total.sum().to_list():
assert total == susceptibles
# infections happen after time 25
for total in result[(result.time >= 25) & (result.state == "S")].groupby(
"time").total.sum().to_list():
assert total != susceptibles
def test_basic_simulation(data_api):
network = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition"),
data_api.read_table("human/population"),
data_api.read_table("human/commutes"),
data_api.read_table("human/mixing-matrix"),
data_api.read_table("human/infectious-compartments"),
data_api.read_table("human/infection-probability"),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
)
result = calculateInfectiousOverTime(
np.basicSimulationInternalAgeStructure(
network, 200, {"S08000016": {
"[17,70)": 10.0
}}), network.infectiousStates)
_assert_baseline(result)
def test_basic_simulation_stochastic(data_api_stochastic):
network = np.createNetworkOfPopulation(
data_api_stochastic.read_table("human/compartment-transition"),
data_api_stochastic.read_table("human/population"),
data_api_stochastic.read_table("human/commutes"),
data_api_stochastic.read_table("human/mixing-matrix"),
data_api_stochastic.read_table("human/infectious-compartments"),
data_api_stochastic.read_table("human/infection-probability"),
data_api_stochastic.read_table("human/initial-infections"),
data_api_stochastic.read_table("human/trials"),
data_api_stochastic.read_table("human/movement-multipliers"),
data_api_stochastic.read_table("human/stochastic-mode"),
data_api_stochastic.read_table("human/random-seed"))
result = np.basicSimulationInternalAgeStructure(
network, 200, {"S08000016": {
"[17,70)": 10
}})
_assert_baseline_dataframe(result)
def test_basicSimulationInternalAgeStructure_no_node_infection_invariant(
data_api, num_infected):
nodes = pd.DataFrame([{
"source": "S08000016",
"target": "S08000016",
"weight": 0.0,
"delta_adjustment": 1.0
}])
population = pd.DataFrame([
{
"Health_Board": "S08000016",
"Sex": "Female",
"Age": "[0,17)",
"Total": 31950
},
{
"Health_Board": "S08000016",
"Sex": "Female",
"Age": "[17,70)",
"Total": 31950
},
{
"Health_Board": "S08000016",
"Sex": "Female",
"Age": "70+",
"Total": 31950
},
])
dampening = pd.DataFrame([{
"Time": 0,
"Movement_Multiplier": 1.0,
"Contact_Multiplier": 0.0
}])
network = np.createNetworkOfPopulation(
data_api.read_table("human/compartment-transition"),
population,
nodes,
data_api.read_table("human/mixing-matrix"),
data_api.read_table("human/infectious-compartments"),
data_api.read_table("human/infection-probability"),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
dampening,
)
initial_population = sum(_count_people_per_region(network.initialState))
result = np.basicSimulationInternalAgeStructure(
network, 50, {"S08000016": {
"[17,70)": num_infected
}})
# population remains constant
populations = result.groupby("time").total.sum()
assert all([
total == pytest.approx(initial_population)
for node, total in populations.to_dict().items()
])
# susceptibles are never infected
for total in result[result.state == "S"].groupby(
"time").total.sum().to_list():
assert total == 3 * 31950 - num_infected
def test_basic_simulation(age_transitions, demographics, commute_moves,
compartment_names, age_infection_matrix):
age_to_trans = np.setUpParametersAges(
loaders.readParametersAgeStructured(age_transitions))
population = loaders.readPopulationAgeStructured(demographics)
graph = loaders.genGraphFromContactFile(commute_moves)
states = np.setupInternalPopulations(graph, compartment_names,
list(age_to_trans.keys()), population)
result = np.basicSimulationInternalAgeStructure(
rand=random.Random(1),
graph=graph,
numInfected=10,
timeHorizon=200,
genericInfection=0.1,
ageInfectionMatrix=age_infection_matrix,
diseaseProgressionProbs=age_to_trans,
dictOfStates=states,
)
expected = [
0,
4.27,
5.959639,
7.495598979703686,
9.31292448442533,
11.550960285080539,
14.32076069530553,
17.750346856443436,
21.996595859880493,
27.253215606191763,
33.75958029276324,
41.81147883471944,
51.77434261313086,
64.09953959129047,
79.34443722167374,
98.19708895391234,
121.5065842268399,
150.32032333168627,
185.92974139403356,
229.92631464707316,
284.27004075722795,
351.3729944719362,
434.2010173334121,
536.3970957055917,
662.4304939975676,
817.7762023518806,
1009.1296689642635,
1244.6620222362512,
1534.3209088397605,
1890.1814875375615,
2326.8507704591293,
2861.926080186407,
3516.5045468063877,
4315.734994514654,
5289.3961276280115,
6472.475902589639,
7905.717433786824,
9636.08906655341,
11717.13446074682,
14209.16871256282,
17179.315999723975,
20701.439040274323,
24856.09052908778,
29730.707243910434,
35420.33217223618,
42029.128554931965,
49672.773024855385,
58481.44396269627,
68602.60309097261,
80202.27853153124,
93463.37789059673,
108579.96624726593,
125747.53880875603,
145150.90161910592,
166952.82966413427,
191287.47020551964,
218261.74745540041,
247965.3261465324,
280485.2367071461,
315916.38015373435,
354356.14564567205,
395872.69490304653,
440443.1364258809,
487868.5388265991,
537684.1860953799,
589091.5046792259,
640938.9095905811,
691769.9762481726,
739939.1843065555,
783772.9904113912,
821736.8679191938,
852567.1996643285,
875343.2246963251,
889499.9704796937,
894803.485637448,
891314.7525319818,
879359.4394061461,
859505.977536541,
832543.5198251844,
799448.8236543302,
761336.2708291251,
719393.6436135583,
674812.7866198674,
628726.0228195366,
582156.5631853839,
535986.5646189475,
490942.3244969345,
447593.5332414354,
406362.5717409869,
367540.01510870096,
331303.2145504978,
297735.6728639047,
266845.6949524783,
238583.40447859198,
212855.66475276518,
189538.74944848128,
168488.80761225612,
149550.28647197766,
132562.53827530914,
117364.86191178164,
103800.22980549172,
91717.93520231653,
80975.37134265121,
71439.12677319416,
62985.553271939556,
55500.93643588842,
48881.374979513224,
43032.45371205615,
37868.777124977256,
33313.415413891256,
29297.302334462536,
25758.61423633403,
22642.151601387908,
19898.738109895345,
17484.64737342592,
15361.063743396788,
13493.580801682876,
11851.739073032668,
10408.603009095552,
9140.37625068078,
8026.053473547509,
7047.106679898116,
6187.203546762776,
5431.955332005772,
4768.691828813375,
4186.260919647075,
3674.8503875840925,
3225.8297793633656,
2831.6102674926847,
2485.5206191429197,
2181.697540636489,
1914.9888235502897,
1680.8678687529227,
1475.3583061787265,
1294.9675597411838,
1136.6283280441162,
997.6470624276589,
875.6586246476982,
768.5863975872606,
674.6072044116167,
592.1204651473781,
519.721085459352,
456.1756310875024,
400.40139364220994,
351.44799986096695,
308.4812575877343,
270.76896818966605,
237.66846737351318,
208.61568486118352,
183.11553854396615,
160.73350093111426,
141.08819527652577,
123.8448960080496,
108.70982326635439,
95.42513472402354,
83.76452961376395,
73.52939023666997,
64.54539531517312,
56.65954754922215,
49.73756475976603,
43.66159017682611,
38.328182852791116,
33.64655394541582,
29.537018798984292,
25.929638426375362,
22.763027221103627,
19.98330656117008,
17.54318645356159,
15.401159551545785,
13.520793793496033,
11.870111594347737,
10.42104499749586,
9.148957491085977,
8.032224330272163,
7.051864205475331,
6.191215972979529,
5.435654933281273,
4.7723438175693484,
4.1900142350687,
3.678774853837169,
3.2299430438278187,
2.835897111408885,
2.4899466059017845,
2.186218487054244,
1.9195572129748524,
1.6854370455381997,
1.4798850786813753,
1.29941367791204,
1.1409611798590402,
1.001839841559402,
0.8796901527963475,
0.772440733292397,
0.6782731317724259,
0.5955909274692558,
0.5229926079757407,
0.45924776170425224,
0.40327617969564616,
0.35412951108933594,
0.3109751600700831,
0.2730821502880063,
]
assert result == pytest.approx(expected)