def test_strains__with_two_symmetric_strains():
"""
Adding two strains with the same properties should yield the same infection dynamics and outputs as having no strains at all.
We expect the force of infection for each strain to be 1/2 of the unstratified model,
but the stratification process will not apply the usual conservation fraction to the fan out flows.
"""
params = {
"contact_rate": 0.2,
"recovery_rate": 0.1,
}
flows = (
{
"type": Flow.INFECTION_FREQUENCY,
"parameter": "contact_rate",
"origin": "S",
"to": "I",
},
{
"type": Flow.STANDARD,
"parameter": "recovery_rate",
"origin": "I",
"to": "R",
},
)
# Create an unstratified model
kwargs = {**MODEL_KWARGS, "parameters": params, "requested_flows": flows}
model = StratifiedModel(**kwargs)
# Do pre-run force of infection calcs.
model.prepare_to_run()
model.prepare_time_step(0, model.compartment_values)
# Check infectiousness multipliers
susceptible = model.compartment_names[0]
infectious = model.compartment_names[1]
assert model.get_infection_density_multiplier(susceptible, infectious) == 100.0
assert model.get_infection_frequency_multiplier(susceptible, infectious) == 0.1
model.run_model()
# Create a stratified model where the two strains are symmetric
strain_model = StratifiedModel(**kwargs)
strain_model.stratify(
stratification_name="strain",
strata_request=["a", "b"],
compartments_to_stratify=["I"],
# Use defaults - equally split compartments, flows, etc.
comp_split_props={},
flow_adjustments={},
infectiousness_adjustments={},
mixing_matrix=None,
)
strain_model.run_model()
merged_outputs = np.zeros_like(model.outputs)
merged_outputs[:, 0] = strain_model.outputs[:, 0]
merged_outputs[:, 1] = strain_model.outputs[:, 1] + strain_model.outputs[:, 2]
merged_outputs[:, 2] = strain_model.outputs[:, 3]
assert_allclose(merged_outputs, model.outputs, atol=0.01, rtol=0.01, verbose=True)
def test_strain__with_flow_adjustments():
"""
Test infectious multiplier and flow rate calculations for
3 strains which have different flow adjustments.
These flow adjustments would correspond to some physical process that we're modelling,
and they should be effectively the same as applying infectiousness multipliers.
"""
contact_rate = 0.2
params = {
"contact_rate": contact_rate,
}
flows = (
{
"type": Flow.INFECTION_FREQUENCY,
"parameter": "contact_rate",
"origin": "S",
"to": "I",
},
)
kwargs = {**MODEL_KWARGS, "parameters": params, "requested_flows": flows}
model = StratifiedModel(**kwargs)
model.stratify(
stratification_name="strain",
strata_request=["a", "b", "c"],
compartments_to_stratify=["I"],
comp_split_props={
"a": 0.7, # 70 people
"b": 0.2, # 20 people
"c": 0.1, # 10 people
},
flow_adjustments={
"contact_rate": {
"a": 0.5, # 0.5x as infectious
"b": 3, # 3x as infectious
"c": 2, # 2x as infectious
}
},
mixing_matrix=None,
)
# Do pre-run force of infection calcs.
model.prepare_to_run()
assert_array_equal(model.compartment_infectiousness["a"], np.array([0, 1, 0, 0, 0]))
assert_array_equal(model.compartment_infectiousness["b"], np.array([0, 0, 1, 0, 0]))
assert_array_equal(model.compartment_infectiousness["c"], np.array([0, 0, 0, 1, 0]))
assert model.category_lookup == {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
assert_array_equal(model.category_matrix, np.array([[1, 1, 1, 1, 1]]))
# Do pre-iteration force of infection calcs
model.prepare_time_step(0, model.compartment_values)
assert_array_equal(model.category_populations, np.array([[1000]]))
assert_array_equal(model.infection_density["a"], np.array([[70]]))
assert_array_equal(model.infection_density["b"], np.array([[20]]))
assert_array_equal(model.infection_density["c"], np.array([[10]]))
assert_array_equal(model.infection_frequency["a"], np.array([[70 / 1000]]))
assert_array_equal(model.infection_frequency["b"], np.array([[20 / 1000]]))
assert_array_equal(model.infection_frequency["c"], np.array([[10 / 1000]]))
# Get multipliers
susceptible = model.compartment_names[0]
infectious_a = model.compartment_names[1]
infectious_b = model.compartment_names[2]
infectious_c = model.compartment_names[3]
assert model.get_infection_density_multiplier(susceptible, infectious_a) == 70
assert model.get_infection_density_multiplier(susceptible, infectious_b) == 20
assert model.get_infection_density_multiplier(susceptible, infectious_c) == 10
assert model.get_infection_frequency_multiplier(susceptible, infectious_a) == 70 / 1000
assert model.get_infection_frequency_multiplier(susceptible, infectious_b) == 20 / 1000
assert model.get_infection_frequency_multiplier(susceptible, infectious_c) == 10 / 1000
# Get infection flow rates
flow_rates = model.get_flow_rates(model.compartment_values, 0)
sus_pop = 900
flow_to_a = sus_pop * contact_rate * (70 * 0.5 / 1000)
flow_to_b = sus_pop * contact_rate * (20 * 3 / 1000)
flow_to_c = sus_pop * contact_rate * (10 * 2 / 1000)
expected_flow_rates = np.array(
[-flow_to_a - flow_to_b - flow_to_c, flow_to_a, flow_to_b, flow_to_c, 0.0]
)
assert_allclose(expected_flow_rates, flow_rates, verbose=True)