def test_apply_many_flows():
"""
Expect multiple flows to operate independently and produce the correct final flow rate.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 900, "I": 100})
model.add_death_flow("infection_death", 0.1, "I")
model.add_universal_death_flows("universal_death", 0.1)
model.add_infection_frequency_flow("infection", 0.2, "S", "I")
model.add_sojourn_flow("recovery", 10, "I", "R")
model.add_transition_flow("vaccination", 0.1, "S", "R")
model.add_crude_birth_flow("births", 0.1, "S")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect the effects of all these flows to be linearly superimposed.
infect_death_flows = np.array([0, -10, 0])
universal_death_flows = np.array([-90, -10, 0])
infected = 900 * 0.2 * (100 / 1000)
infection_flows = np.array([-infected, infected, 0])
recovery_flows = np.array([0, -10, 10])
vaccination_flows = np.array([-90, 0, 90])
birth_flows = np.array([100, 0, 0])
expected_flow_rates = (infect_death_flows + universal_death_flows +
infection_flows + recovery_flows +
vaccination_flows + birth_flows)
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_functional_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
def get_non_infected(rec, pop):
return rec / pop
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("population", ["S", "I", "R"])
model.request_function_output("recovered_prop", get_non_infected, ["recovered", "population"])
dos = model._calculate_derived_outputs()
assert_array_equal(dos["recovered_prop"], np.array([0, 0.005, 0.01, 0.015, 0.020, 0.025]))
def test_derived_outputs_whitelist():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("not_infected", ["S", "R"])
model.request_output_for_compartments("infected", ["I"])
model.request_output_for_compartments("total_population", ["S", "I", "R"])
model.request_cumulative_output(name="accum_infected", source="infected")
model.set_derived_outputs_whitelist(["recovered", "accum_infected"])
dos = model._calculate_derived_outputs()
assert "recovered" in dos # Included coz in whitelist (or dependency of)
assert "infected" in dos # Included coz in whitelist (or dependency of)
assert "accum_infected" in dos # Included coz in whitelist (or dependency of)
assert "not_infected" not in dos # Excluded coz not in whitelist
assert "total_population" not in dos # Excluded coz not in whitelist
assert_array_equal(dos["recovered"], np.array([0, 5, 10, 15, 20, 25]))
assert_array_equal(dos["infected"], np.array([10, 15, 20, 25, 30, 35]))
assert_array_equal(dos["accum_infected"], np.array([10, 25, 45, 70, 100, 135]))
def test_apply_flows__with_function_flow__expect_flows_applied(
inf_pop, sus_pop, exp_flow):
"""
Expect a flow to occur proportional to the result of `get_flow_rate`.
"""
def get_flow_rate(flow, comps, comp_vals, flows, flow_rates, time):
_, i_pop, _ = comp_vals
i_flow, _ = flow_rates
return i_pop + i_flow
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": sus_pop, "I": inf_pop})
model.add_transition_flow("infection", 0.1, "S", "I")
model.add_function_flow("treatment", get_flow_rate, "I", "S")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
expected_infected = sus_pop * 0.1
expected_flow_rates = np.array([
exp_flow - expected_infected,
expected_infected - exp_flow,
0,
])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_no_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
# 6 timesteps, 3 compartments.
assert model.outputs.shape == (6, 3)
assert model.derived_outputs == {}
def test_apply_universal_death_flow():
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
model.add_universal_death_flows("universal_death", 0.1)
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
expected_flow_rates = np.array([-99, -1])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_infect_death_flows(inf_pop, exp_flow):
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"I": inf_pop})
model.add_death_flow("infection_death", 0.1, "I")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect 0.1 * inf_pop = exp_flow
expected_flow_rates = np.array([0, -exp_flow])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_basic_get_infection_multiplier():
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
model._prepare_to_run()
model._prepare_time_step(0, model.initial_population)
c_src = model.compartments[0]
c_dst = model.compartments[1]
multiplier = model._get_infection_frequency_multiplier(c_src, c_dst)
assert multiplier == 10 / 1000
multiplier = model._get_infection_density_multiplier(c_src, c_dst)
assert multiplier == 10
def test_apply_replace_death_birth_flow():
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 900, "I": 100})
model.add_death_flow("infection_death", 0.1, "I")
model.add_universal_death_flows("universal_death", 0.05)
model.add_replacement_birth_flow("births", "S")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect 10 people to die and 10 to be born
exp_i_flow_rate = -0.1 * 100 - 0.05 * 100
exp_s_flow_rate = -exp_i_flow_rate # N.B births + deaths in the S compartment should balance.
expected_flow_rates = np.array([exp_s_flow_rate, exp_i_flow_rate])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_flows__with_sojourn_flow__expect_flows_applied(
inf_pop, exp_flow):
"""
Expect a flow to occur proportional to the compartment size and parameter.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"I": inf_pop})
model.add_sojourn_flow("recovery", 10, "I", "R")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect exp_flow = inf_pop * () exp_flow
expected_flow_rates = np.array([0, -exp_flow, exp_flow])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_flows__with_infection_density(inf_pop, sus_pop, exp_flow):
"""
Use infection density, expect infection multiplier to be proportional
to the infectious pop.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": sus_pop, "I": inf_pop})
model.add_infection_density_flow("infection", 0.02, "S", "I")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect 0.2 * sus_pop * inf_pop = exp_flow
expected_flow_rates = np.array([-exp_flow, exp_flow])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_flows__with_infection_frequency(inf_pop, sus_pop, exp_flow):
"""
Use infection frequency, expect infection multiplier to be proportional
to the proprotion of infectious to total pop.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": sus_pop, "I": inf_pop})
model.add_infection_frequency_flow("infection", 20, "S", "I")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect sus_pop * 20 * (inf_pop / 1000) = exp_flow
expected_flow_rates = np.array([-exp_flow, exp_flow])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_crude_birth_rate_flow(birth_rate, exp_flow):
"""
Expect births proportional to the total population and birth rate when
the birth approach is "crude birth rate".
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
model.add_crude_birth_flow("births", birth_rate, "S")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect birth_rate * total_population = exp_flow
expected_flow_rates = np.array([exp_flow, 0])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_apply_flows__with_transition_flow__expect_flows_applied(
inf_pop, sus_pop, exp_flow):
"""
Expect a flow to occur proportional to the compartment size and parameter.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": sus_pop, "I": inf_pop})
model.add_transition_flow("deliberately_infected", 0.1, "S", "I")
model._prepare_to_run()
actual_flow_rates = model._get_compartment_rates(model.initial_population,
0)
# Expect sus_pop * 0.1 = exp_flow
expected_flow_rates = np.array([-exp_flow, exp_flow])
assert_array_equal(actual_flow_rates, expected_flow_rates)
def test_flow_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 900, "I": 100})
# Constant entry.
model.add_importation_flow("imports", num_imported=2, dest="S")
model.request_output_for_flow(name="importation", flow_name="imports")
model.request_output_for_flow(name="importation_raw", flow_name="imports", raw_results=True)
# Linear entry.
model.add_importation_flow("imports_land", num_imported=lambda t: 3 * t, dest="S")
model.request_output_for_flow(name="importation_land", flow_name="imports_land")
# Quadratic entry.
model.add_importation_flow("imports_air", num_imported=lambda t: t ** 2, dest="S")
model.request_output_for_flow(name="importation_air", flow_name="imports_air")
# Fractional transition flow
model.add_transition_flow("recovery", 0.1, "I", "R")
model.request_output_for_flow(name="recovery_raw", flow_name="recovery", raw_results=True)
model.request_output_for_flow(name="recovery_delta", flow_name="recovery", raw_results=False)
model.run()
dos = model.derived_outputs
# Raw outputs are the instantaneous flow rate at a given time.
assert_allclose(dos["recovery_raw"], model.outputs[:, 1] * 0.1, rtol=0.01)
# Post-processed outputs reflect changes in compartment size.
recovered_count = np.zeros_like(model.outputs[:, 2])
recovered_count[1:] = np.diff(model.outputs[:, 2])
assert_allclose(dos["recovery_delta"][1:], recovered_count[1:], rtol=0.01)
# Good match for constant
assert_array_equal(dos["importation"][1:], np.array([2, 2, 2, 2, 2]))
assert_array_equal(dos["importation_raw"], np.array([2, 2, 2, 2, 2, 2]))
# Good match for linear
assert_allclose(dos["importation_land"][1:], np.array([1.5, 4.5, 7.5, 10.5, 13.5]))
# So-so match for quadratic
assert_allclose(dos["importation_air"][1:], np.array([0.5, 2.5, 6.5, 12.5, 20.5]), rtol=0.1)
def test_derived_outputs_with_no_save_results():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.add_importation_flow("imports", num_imported=2, dest="S")
# Expect np.array([0, 2, 2, 2, 2, 2]))
model.request_output_for_flow(name="importation", flow_name="imports", save_results=False)
# Expect np.array([0, 5, 10, 15, 20, 25]))
model.request_output_for_compartments("recovered", ["R"], save_results=False)
# Expect np.array([0, 5, 15, 30, 50, 75]))
model.request_cumulative_output(
name="recovered_cumulative", source="recovered", save_results=False
)
# Expect np.array([0, 7, 12, 17, 22, 227]))
model.request_aggregate_output(
name="some_aggregate", sources=["recovered", "importation"], save_results=False
)
# Expect np.array([ 0, 12, 27, 47, 72, 102])
model.request_aggregate_output(
name="final_aggregate", sources=["some_aggregate", "recovered_cumulative"]
)
model.run()
# Override outputs so the test is easier to write
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
dos = model._calculate_derived_outputs()
assert_array_equal(dos["final_aggregate"][1:], np.array([12, 27, 47, 72, 102]))
assert "importation" not in dos
assert "recovered" not in dos
assert "recovered_cumulative" not in dos
assert "some_aggregate" not in dos
def test_strat_get_infection_multiplier__with_age_strat_and_no_mixing():
"""
Check FoI when a simple 2-strata stratification applied and no mixing matrix.
Expect the same results as with the basic case.
"""
# Create a model
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
model.stratify_with(strat)
assert model._mixing_categories == [{}]
# Do pre-run force of infection calcs.
model._prepare_to_run()
assert_array_equal(model._compartment_infectiousness["default"],
np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]))
assert_array_equal(model._category_matrix,
np.array([[1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]))
assert model._category_lookup == {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
assert_array_equal(model._category_populations, np.array([1000]))
assert_array_equal(model._infection_density["default"], np.array([10.0]))
assert_array_equal(model._infection_frequency["default"], np.array([0.01]))
# Get multipliers
s_child = model.compartments[0]
s_adult = model.compartments[1]
i_child = model.compartments[2]
i_adult = model.compartments[3]
assert model._get_infection_density_multiplier(s_child, i_child) == 10.0
assert model._get_infection_density_multiplier(s_adult, i_adult) == 10.0
assert model._get_infection_frequency_multiplier(s_child, i_child) == 0.01
assert model._get_infection_frequency_multiplier(s_adult, i_adult) == 0.01
# Santiy check frequency-dependent force of infection
assert 1000.0 * 0.01 == 10
def test_cumulative_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_cumulative_output(name="recoved_cumulative", source="recovered")
model.request_cumulative_output(name="recoved_cumulative_2", source="recovered", start_time=2)
dos = model._calculate_derived_outputs()
assert_array_equal(dos["recoved_cumulative"], np.array([0, 5, 15, 30, 50, 75]))
assert_array_equal(dos["recoved_cumulative_2"], np.array([0, 0, 10, 25, 45, 70]))
def test_aggregate_derived_outputs():
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.set_initial_population(distribution={"S": 990, "I": 10})
model.run()
model.outputs = np.array(
[
[990, 10, 0],
[980, 15, 5],
[970, 20, 10],
[960, 25, 15],
[950, 30, 20],
[940, 35, 25],
]
)
model.request_output_for_compartments("recovered", ["R"])
model.request_output_for_compartments("not_infected", ["S", "R"])
model.request_output_for_compartments("total_population", ["S", "I", "R"])
model.request_aggregate_output(name="my_aggregate", sources=["recovered", "total_population"])
dos = model._calculate_derived_outputs()
assert_array_equal(dos["my_aggregate"], np.array([1000, 1005, 1010, 1015, 1020, 1025]))
def test_strat_get_infection_multiplier__with_double_strat_and_both_strats_mixing(
):
"""
Check FoI when a two 2-strata stratification applied and both stratifications have a mixing matrix.
"""
# Create the model
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
strat.set_population_split({"child": 0.3, "adult": 0.7})
am = np.array([[2, 3], [5, 7]])
strat.set_mixing_matrix(am)
model.stratify_with(strat)
strat = Stratification("location", ["work", "home"], ["S", "I", "R"])
lm = np.array([[11, 13], [17, 19]])
strat.set_mixing_matrix(lm)
model.stratify_with(strat)
expected_mixing = np.array([
[2 * 11, 2 * 13, 3 * 11, 3 * 13],
[2 * 17, 2 * 19, 3 * 17, 3 * 19],
[5 * 11, 5 * 13, 7 * 11, 7 * 13],
[5 * 17, 5 * 19, 7 * 17, 7 * 19],
])
assert_array_equal(model._get_mixing_matrix(0), expected_mixing)
assert model._mixing_categories == [
{
"age": "child",
"location": "work"
},
{
"age": "child",
"location": "home"
},
{
"age": "adult",
"location": "work"
},
{
"age": "adult",
"location": "home"
},
]
assert model.compartments == [
C("S", {
"age": "child",
"location": "work"
}),
C("S", {
"age": "child",
"location": "home"
}),
C("S", {
"age": "adult",
"location": "work"
}),
C("S", {
"age": "adult",
"location": "home"
}),
C("I", {
"age": "child",
"location": "work"
}),
C("I", {
"age": "child",
"location": "home"
}),
C("I", {
"age": "adult",
"location": "work"
}),
C("I", {
"age": "adult",
"location": "home"
}),
C("R", {
"age": "child",
"location": "work"
}),
C("R", {
"age": "child",
"location": "home"
}),
C("R", {
"age": "adult",
"location": "work"
}),
C("R", {
"age": "adult",
"location": "home"
}),
]
expected_comp_vals = np.array(
[148.5, 148.5, 346.5, 346.5, 1.5, 1.5, 3.5, 3.5, 0, 0, 0, 0])
assert_array_equal(model.initial_population, expected_comp_vals)
# Do pre-run force of infection calcs.
model._prepare_to_run()
exp_lookup = {
0: 0, # S age: child location: work -> children at work
1: 1, # S age: child location: home -> children at home
2: 2, # S age: adult location: work -> adults at work
3: 3, # S age: adult location: home -> adults at home
4: 0, # I age: child location: work -> children at work
5: 1, # I age: child location: home -> children at home
6: 2, # I age: adult location: work -> adults at work
7: 3, # I age: adult location: home -> adults at home
8: 0, # R age: child location: work -> children at work
9: 1, # R age: child location: home -> children at home
10: 2, # R age: adult location: work -> adults at work
11: 3, # R age: adult location: home -> adults at home
}
exp_matrix = np.array([
[1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], # children at work
[0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0], # children at home
[0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0], # adults at work
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], # adults at home
])
exp_mults = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0])
assert_array_equal(model._compartment_infectiousness["default"], exp_mults)
assert_array_equal(model._category_matrix, exp_matrix)
assert model._category_lookup == exp_lookup
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
exp_pops = np.array([
150, # children at work
150, # children at home
350, # adults at work
350, # adults at home
])
assert_array_equal(model._category_populations, exp_pops)
child_work_density = 2 * 11 * 1.5 + 2 * 13 * 1.5 + 3 * 11 * 3.5 + 3 * 13 * 3.5
child_home_density = 2 * 17 * 1.5 + 2 * 19 * 1.5 + 3 * 17 * 3.5 + 3 * 19 * 3.5
adult_work_density = 5 * 11 * 1.5 + 5 * 13 * 1.5 + 7 * 11 * 3.5 + 7 * 13 * 3.5
adult_home_density = 5 * 17 * 1.5 + 5 * 19 * 1.5 + 7 * 17 * 3.5 + 7 * 19 * 3.5
child_work_freq = (2 * 11 * 1.5 / 150 + 2 * 13 * 1.5 / 150 +
3 * 11 * 3.5 / 350 + 3 * 13 * 3.5 / 350)
child_home_freq = (2 * 17 * 1.5 / 150 + 2 * 19 * 1.5 / 150 +
3 * 17 * 3.5 / 350 + 3 * 19 * 3.5 / 350)
adult_work_freq = (5 * 11 * 1.5 / 150 + 5 * 13 * 1.5 / 150 +
7 * 11 * 3.5 / 350 + 7 * 13 * 3.5 / 350)
adult_home_freq = (5 * 17 * 1.5 / 150 + 5 * 19 * 1.5 / 150 +
7 * 17 * 3.5 / 350 + 7 * 19 * 3.5 / 350)
exp_density = np.array([
child_work_density, # children at work
child_home_density, # children at home
adult_work_density, # adults at work
adult_home_density, # adults at home
])
exp_frequency = np.array([
child_work_freq, # children at work
child_home_freq, # children at home
adult_work_freq, # adults at work
adult_home_freq, # adults at home
])
assert_array_equal(model._infection_density["default"], exp_density)
assert_allclose(model._infection_frequency["default"],
exp_frequency,
rtol=0,
atol=1e-9)
# Get multipliers
s_child_work = model.compartments[0]
s_child_home = model.compartments[1]
s_adult_work = model.compartments[2]
s_adult_home = model.compartments[3]
i_child_work = model.compartments[4]
i_child_home = model.compartments[5]
i_adult_work = model.compartments[6]
i_adult_home = model.compartments[7]
assert (abs(
model._get_infection_density_multiplier(s_child_work, i_child_work) -
child_work_density) <= 1e-9)
assert (abs(
model._get_infection_density_multiplier(s_child_home, i_child_home) -
child_home_density) <= 1e-9)
assert (abs(
model._get_infection_density_multiplier(s_adult_work, i_adult_work) -
adult_work_density) <= 1e-9)
assert (abs(
model._get_infection_density_multiplier(s_adult_home, i_adult_home) -
adult_home_density) <= 1e-9)
assert (abs(
model._get_infection_frequency_multiplier(s_child_work, i_child_work) -
child_work_freq) <= 1e-9)
assert (abs(
model._get_infection_frequency_multiplier(s_child_home, i_child_home) -
child_home_freq) <= 1e-9)
assert (abs(
model._get_infection_frequency_multiplier(s_adult_work, i_adult_work) -
adult_work_freq) <= 1e-9)
assert (abs(
model._get_infection_frequency_multiplier(s_adult_home, i_adult_home) -
adult_home_freq) <= 1e-9)
def test_strat_get_infection_multiplier__with_age_strat_and_mixing():
"""
Check FoI when a simple 2-strata stratification applied AND heteregeneous mixing.
Use a non-uniform mixing matrix
"""
# Create a model
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
mixing_matrix = np.array([[2, 3], [5, 7]])
strat.set_mixing_matrix(mixing_matrix)
strat.set_population_split({"child": 0.2, "adult": 0.8})
model.stratify_with(strat)
assert model._mixing_categories == [{"age": "child"}, {"age": "adult"}]
assert model.compartments == [
C("S", {"age": "child"}),
C("S", {"age": "adult"}),
C("I", {"age": "child"}),
C("I", {"age": "adult"}),
C("R", {"age": "child"}),
C("R", {"age": "adult"}),
]
assert_array_equal(model.initial_population,
np.array([198.0, 792.0, 2.0, 8.0, 0.0, 0.0]))
# Do pre-run force of infection calcs.
model._prepare_to_run()
assert_array_equal(model._compartment_infectiousness["default"],
np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]))
assert_array_equal(
model._category_matrix,
np.array([[1.0, 0.0, 1.0, 0.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0, 1.0]]),
)
assert model._category_lookup == {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1}
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
assert_array_equal(model._category_populations, np.array([200.0, 800.0]))
child_density = 28
adult_density = 66
assert child_density == 2 * 2.0 + 3 * 8.0
assert adult_density == 5 * 2.0 + 7 * 8.0
assert_array_equal(model._infection_density["default"],
np.array([child_density, adult_density]))
child_freq = 0.05
adult_freq = 0.12000000000000001
assert child_freq == 2 * 2.0 / 200 + 3 * 8.0 / 800
assert adult_freq == 5 * 2.0 / 200 + 7 * 8.0 / 800
assert_array_equal(model._infection_frequency["default"],
np.array([child_freq, adult_freq]))
# Get multipliers
s_child = model.compartments[0]
s_adult = model.compartments[1]
i_child = model.compartments[2]
i_adult = model.compartments[3]
assert model._get_infection_density_multiplier(s_child,
i_child) == child_density
assert model._get_infection_density_multiplier(s_adult,
i_adult) == adult_density
assert model._get_infection_frequency_multiplier(s_child,
i_child) == child_freq
assert model._get_infection_frequency_multiplier(s_adult,
i_adult) == adult_freq
def test_strat_get_infection_multiplier__with_double_strat_and_no_mixing():
"""
Check FoI when a two 2-strata stratificationz applied and no mixing matrix.
Expect the same results as with the basic case.
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
model.stratify_with(strat)
strat = Stratification("location", ["work", "home"], ["S", "I", "R"])
model.stratify_with(strat)
assert model._mixing_categories == [{}]
assert model.compartments == [
C("S", {
"age": "child",
"location": "work"
}),
C("S", {
"age": "child",
"location": "home"
}),
C("S", {
"age": "adult",
"location": "work"
}),
C("S", {
"age": "adult",
"location": "home"
}),
C("I", {
"age": "child",
"location": "work"
}),
C("I", {
"age": "child",
"location": "home"
}),
C("I", {
"age": "adult",
"location": "work"
}),
C("I", {
"age": "adult",
"location": "home"
}),
C("R", {
"age": "child",
"location": "work"
}),
C("R", {
"age": "child",
"location": "home"
}),
C("R", {
"age": "adult",
"location": "work"
}),
C("R", {
"age": "adult",
"location": "home"
}),
]
expected_comp_vals = np.array(
[247.5, 247.5, 247.5, 247.5, 2.5, 2.5, 2.5, 2.5, 0, 0, 0, 0])
assert_array_equal(model.initial_population, expected_comp_vals)
# Do pre-run force of infection calcs.
model._prepare_to_run()
expected_compartment_infectiousness = np.array(
[0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0])
expected_category_matrix = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
expected_lookup = {n: 0 for n in range(12)}
assert_array_equal(model._compartment_infectiousness["default"],
expected_compartment_infectiousness)
assert_array_equal(model._category_matrix, expected_category_matrix)
assert model._category_lookup == expected_lookup
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
assert_array_equal(model._category_populations, np.array([1000]))
assert_array_equal(model._infection_density["default"], np.array([10.0]))
assert_array_equal(model._infection_frequency["default"], np.array([0.01]))
s_child_work = model.compartments[0]
s_child_home = model.compartments[1]
s_adult_work = model.compartments[2]
s_adult_home = model.compartments[3]
i_child_work = model.compartments[4]
i_child_home = model.compartments[5]
i_adult_work = model.compartments[6]
i_adult_home = model.compartments[7]
assert model._get_infection_density_multiplier(s_child_work,
i_child_work) == 10.0
assert model._get_infection_density_multiplier(s_child_home,
i_child_home) == 10.0
assert model._get_infection_density_multiplier(s_adult_work,
i_adult_work) == 10.0
assert model._get_infection_density_multiplier(s_adult_home,
i_adult_home) == 10.0
assert model._get_infection_frequency_multiplier(s_child_work,
i_child_work) == 0.01
assert model._get_infection_frequency_multiplier(s_child_home,
i_child_home) == 0.01
assert model._get_infection_frequency_multiplier(s_adult_work,
i_adult_work) == 0.01
assert model._get_infection_frequency_multiplier(s_adult_home,
i_adult_home) == 0.01
def test_strat_get_infection_multiplier__with_age_strat_and_simple_mixing():
"""
Check FoI when a simple 2-strata stratification applied AND heteregeneous mixing.
Expect same frequency as before, different density.
Note that the mixing matrix has different meanings for density / vs frequency.
N.B Mixing matrix format.
Columns are the people who are infectors
Rows are the people who are infected
So matrix has following values
child adult
child child -> child adult -> child
adult child -> adult adult -> adult
"""
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
mixing_matrix = np.array([[0.5, 0.5], [0.5, 0.5]])
strat.set_mixing_matrix(mixing_matrix)
model.stratify_with(strat)
assert model._mixing_categories == [{"age": "child"}, {"age": "adult"}]
assert model.compartments == [
C("S", {"age": "child"}),
C("S", {"age": "adult"}),
C("I", {"age": "child"}),
C("I", {"age": "adult"}),
C("R", {"age": "child"}),
C("R", {"age": "adult"}),
]
assert_array_equal(model.initial_population,
np.array([495, 495, 5, 5, 0.0, 0.0]))
# Do pre-run force of infection calcs.
model._prepare_to_run()
assert_array_equal(model._compartment_infectiousness["default"],
np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]))
assert_array_equal(
model._category_matrix,
np.array([[1.0, 0.0, 1.0, 0.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0, 1.0]]),
)
assert model._category_lookup == {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1}
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
child_density = 5
adult_density = 5
assert child_density == 0.5 * 5 + 0.5 * 5
assert adult_density == 0.5 * 5 + 0.5 * 5
assert_array_equal(model._infection_density["default"],
np.array([child_density, adult_density]))
child_freq = 0.01
adult_freq = 0.01
assert child_freq == child_density / 500
assert adult_freq == adult_density / 500
assert_array_equal(model._infection_frequency["default"],
np.array([child_freq, adult_freq]))
# Get multipliers
s_child = model.compartments[0]
s_adult = model.compartments[1]
i_child = model.compartments[2]
i_adult = model.compartments[3]
assert model._get_infection_density_multiplier(s_child,
i_child) == child_density
assert model._get_infection_density_multiplier(s_adult,
i_adult) == adult_density
assert model._get_infection_frequency_multiplier(s_child,
i_child) == child_freq
assert model._get_infection_frequency_multiplier(s_adult,
i_adult) == adult_freq
# Santiy check frequency-dependent force of infection
assert 500.0 * child_freq + 500.0 * adult_freq == 10
def create_simple_model():
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
return model
def test_strat_get_infection_multiplier__with_double_strat_and_second_strat_mixing(
):
"""
Check FoI when a two 2-strata stratification applied and the second stratification has a mixing matrix.
"""
# Create the model
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
strat.set_population_split({"child": 0.3, "adult": 0.7})
model.stratify_with(strat)
strat = Stratification("location", ["work", "home"], ["S", "I", "R"])
location_mixing = np.array([[2, 3], [5, 7]])
strat.set_mixing_matrix(location_mixing)
model.stratify_with(strat)
assert model._mixing_categories == [{
"location": "work"
}, {
"location": "home"
}]
assert model.compartments == [
C("S", {
"age": "child",
"location": "work"
}),
C("S", {
"age": "child",
"location": "home"
}),
C("S", {
"age": "adult",
"location": "work"
}),
C("S", {
"age": "adult",
"location": "home"
}),
C("I", {
"age": "child",
"location": "work"
}),
C("I", {
"age": "child",
"location": "home"
}),
C("I", {
"age": "adult",
"location": "work"
}),
C("I", {
"age": "adult",
"location": "home"
}),
C("R", {
"age": "child",
"location": "work"
}),
C("R", {
"age": "child",
"location": "home"
}),
C("R", {
"age": "adult",
"location": "work"
}),
C("R", {
"age": "adult",
"location": "home"
}),
]
expected_comp_vals = np.array(
[148.5, 148.5, 346.5, 346.5, 1.5, 1.5, 3.5, 3.5, 0, 0, 0, 0])
assert_array_equal(model.initial_population, expected_comp_vals)
# Do pre-run force of infection calcs.
model._prepare_to_run()
exp_lookup = {
0: 0,
1: 1,
2: 0,
3: 1,
4: 0,
5: 1,
6: 0,
7: 1,
8: 0,
9: 1,
10: 0,
11: 1
}
exp_matrix = np.array([
[1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
])
exp_mults = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0])
assert_array_equal(model._compartment_infectiousness["default"], exp_mults)
assert_array_equal(model._category_matrix, exp_matrix)
assert model._category_lookup == exp_lookup
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
assert_array_equal(model._category_populations, np.array([500.0, 500.0]))
work_density = 25
home_density = 60
assert work_density == 2 * 5 + 3 * 5
assert home_density == 5 * 5 + 7 * 5
assert_array_equal(model._infection_density["default"],
np.array([work_density, home_density]))
work_freq = 2 * 5 / 500 + 3 * 5 / 500
home_freq = 5 * 5 / 500 + 7 * 5 / 500
assert_array_equal(model._infection_frequency["default"],
np.array([work_freq, home_freq]))
# Get multipliers
s_child_work = model.compartments[0]
s_child_home = model.compartments[1]
s_adult_work = model.compartments[2]
s_adult_home = model.compartments[3]
i_child_work = model.compartments[4]
i_child_home = model.compartments[5]
i_adult_work = model.compartments[6]
i_adult_home = model.compartments[7]
assert model._get_infection_density_multiplier(
s_child_work, i_child_work) == work_density
assert model._get_infection_density_multiplier(
s_child_home, i_child_home) == home_density
assert model._get_infection_density_multiplier(
s_adult_work, i_adult_work) == work_density
assert model._get_infection_density_multiplier(
s_adult_home, i_adult_home) == home_density
assert model._get_infection_frequency_multiplier(s_child_work,
i_child_work) == work_freq
assert model._get_infection_frequency_multiplier(s_child_home,
i_child_home) == home_freq
assert model._get_infection_frequency_multiplier(s_adult_work,
i_adult_work) == work_freq
assert model._get_infection_frequency_multiplier(s_adult_home,
i_adult_home) == home_freq
def test_strat_get_infection_multiplier__with_age_split_and_simple_mixing():
"""
Check FoI when a simple 2-strata stratification applied AND heteregeneous mixing.
Unequally split the children and adults.
Expect same density as before, different frequency.
Note that the mixing matrix has different meanings for density / vs frequency.
"""
# Create a model
model = CompartmentalModel(times=[0, 5],
compartments=["S", "I", "R"],
infectious_compartments=["I"])
model.set_initial_population(distribution={"S": 990, "I": 10})
strat = Stratification("age", ["child", "adult"], ["S", "I", "R"])
mixing_matrix = np.array([[0.5, 0.5], [0.5, 0.5]])
strat.set_mixing_matrix(mixing_matrix)
strat.set_population_split({"child": 0.2, "adult": 0.8})
model.stratify_with(strat)
assert model._mixing_categories == [{"age": "child"}, {"age": "adult"}]
assert model.compartments == [
C("S", {"age": "child"}),
C("S", {"age": "adult"}),
C("I", {"age": "child"}),
C("I", {"age": "adult"}),
C("R", {"age": "child"}),
C("R", {"age": "adult"}),
]
assert_array_equal(model.initial_population,
np.array([198.0, 792.0, 2.0, 8.0, 0.0, 0.0]))
# Do pre-run force of infection calcs.
model._prepare_to_run()
assert_array_equal(model._compartment_infectiousness["default"],
np.array([0.0, 0.0, 1.0, 1.0, 0.0, 0.0]))
assert_array_equal(
model._category_matrix,
np.array([[1.0, 0.0, 1.0, 0.0, 1.0, 0.0],
[0.0, 1.0, 0.0, 1.0, 0.0, 1.0]]),
)
assert model._category_lookup == {0: 0, 1: 1, 2: 0, 3: 1, 4: 0, 5: 1}
# Do pre-iteration force of infection calcs
model._prepare_time_step(0, model.initial_population)
child_density = 5
adult_density = 5
assert child_density == 0.5 * 5 + 0.5 * 5
assert adult_density == 0.5 * 5 + 0.5 * 5
assert_array_equal(model._infection_density["default"],
np.array([child_density, adult_density]))
child_freq = 0.01
adult_freq = 0.01
assert child_freq == 0.5 * 2 / 200 + 0.5 * 8 / 800
assert adult_freq == 0.5 * 2 / 200 + 0.5 * 8 / 800
assert_array_equal(model._infection_frequency["default"],
np.array([child_freq, adult_freq]))
# Get multipliers
s_child = model.compartments[0]
s_adult = model.compartments[1]
i_child = model.compartments[2]
i_adult = model.compartments[3]
assert model._get_infection_density_multiplier(s_child,
i_child) == child_density
assert model._get_infection_density_multiplier(s_adult,
i_adult) == adult_density
assert model._get_infection_frequency_multiplier(s_child,
i_child) == child_freq
assert model._get_infection_frequency_multiplier(s_adult,
i_adult) == adult_freq
# Santiy check frequency-dependent force of infection
assert 200.0 * child_freq + 800.0 * adult_freq == 10