def test_createNetworkOfPopulation_transition_to_exposed(data_api):
progression = pd.DataFrame([
{
"age": "70+",
"src": "A",
"dst": "E",
"rate": 0.7
},
{
"age": "70+",
"src": "A",
"dst": "A",
"rate": 0.3
},
{
"age": "70+",
"src": "E",
"dst": "E",
"rate": 1.0
},
])
population = pd.DataFrame([
{
"Health_Board": "S08000016",
"Sex": "Female",
"Age": "70+",
"Total": 31950
},
])
commutes = pd.DataFrame([{
"source": "S08000015",
"target": "S08000015",
"weight": 100777.0,
"delta_adjustment": 1.0
}])
mixingMatrix = pd.DataFrame([{
"source": "70+",
"target": "70+",
"mixing": 1.0
}])
with pytest.raises(AssertionError):
np.createNetworkOfPopulation(
progression,
population,
commutes,
mixingMatrix,
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"),
)
def test_basicSimulationInternalAgeStructure_invalid_compartment(data_api):
with pytest.raises(AssertionError):
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"),
pd.DataFrame([{
"Compartment": "INVALID"
}]),
data_api.read_table("human/infection-probability"),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
)
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_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_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_createNetworkOfPopulation_invalid_infection_probability(
data_api, time, prob):
with pytest.raises(ValueError):
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": time,
"Value": prob
}]),
data_api.read_table("human/initial-infections"),
data_api.read_table("human/trials"),
)
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_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_createNetworkOfPopulation(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"),
)
assert network.graph
assert network.mixingMatrix
assert network.initialState
assert network.progression
assert network.movementMultipliers == {}
assert set(network.infectiousStates) == {"I", "A"}
assert network.infectionProb == {0: 1.0}
assert network.initialInfections == {"S08000016": {"[17,70)": 100}}
assert network.trials == 1
def test_randomlyInfectRegions(data_api, regions, age_groups, 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"),
)
random.seed(3)
infections = np.randomlyInfectRegions(network, regions, age_groups,
infected)
assert len(infections) == regions
assert list(age_groups[0] in infection
for infection in infections.values())
assert all(infection[age_groups[0]] == infected
for infection in infections.values())
def test_stochastic_seed_sequence(data_api_stochastic):
network, _ = network_of_populations.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"),
pd.DataFrame({"Value": [2]}),
data_api_stochastic.read_table("human/start-end-date", "start-end-date"),
data_api_stochastic.read_table("human/movement-multipliers", "movement-multipliers"),
pd.DataFrame({"Value": [True]}),
)
issues = []
r1, r2 = sampleUseOfModel.runSimulation(network, random_seed=123, issues=issues, max_workers=2)
df1 = r1.output
df2 = r2.output
# It's very unlikely these numbers would match unless both runs produce the same numbers
assert df1[df1.state == "D"].total.sum() != df2[df2.state == "D"].total.sum()
assert not issues
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
