def test_stratify_transition_flows__with_source_and_dest_stratified():
"""
Ensure transition flows are stratified correctly when both the flow source and dest are stratified.
"""
model = CompartmentalModel(
times=[0, 5], compartments=["S", "I", "R"], infectious_compartments=["I"]
)
model.add_infection_frequency_flow("infection", 0.03, "S", "I")
model.add_sojourn_flow("recovery", 7, "I", "R")
expected_flows = [
InfectionFrequencyFlow(
"infection", C("S"), C("I"), 0.03, model._get_infection_frequency_multiplier
),
SojournFlow("recovery", C("I"), C("R"), 7),
]
assert len(expected_flows) == len(model._flows)
assert all([a._is_equal(e) for e, a in zip(expected_flows, model._flows)])
# Apply stratification
strat = Stratification("location", ["urban", "rural"], ["S", "I", "R"])
model.stratify_with(strat)
expected_flows = [
InfectionFrequencyFlow(
"infection",
C("S", {"location": "urban"}),
C("I", {"location": "urban"}),
0.03,
model._get_infection_frequency_multiplier,
),
InfectionFrequencyFlow(
"infection",
C("S", {"location": "rural"}),
C("I", {"location": "rural"}),
0.03,
model._get_infection_frequency_multiplier,
),
SojournFlow("recovery", C("I", {"location": "urban"}), C("R", {"location": "urban"}), 7),
SojournFlow("recovery", C("I", {"location": "rural"}), C("R", {"location": "rural"}), 7),
]
assert len(expected_flows) == len(model._flows)
assert all([a._is_equal(e) for e, a in zip(expected_flows, model._flows)])
def _get_test_model(timestep=1, times=[0, 150]):
comps = ["S", "EE", "LE", "EA", "LA", "R"]
infectious_comps = ["LE", "EA", "LA"]
model = CompartmentalModel(
times=times,
compartments=comps,
infectious_compartments=infectious_comps,
timestep=timestep,
)
model.set_initial_population({"S": int(20e6), "LA": 100})
# Add flows
model.add_infection_frequency_flow(name="infection", contact_rate=0.03, source="S", dest="EE")
model.add_sojourn_flow(name="infect_onset", sojourn_time=7, source="EE", dest="LE")
model.add_sojourn_flow(name="incidence", sojourn_time=7, source="LE", dest="EA")
model.add_sojourn_flow(name="progress", sojourn_time=7, source="EA", dest="LA")
model.add_sojourn_flow(name="recovery", sojourn_time=7, source="LA", dest="R")
model.add_death_flow(name="infect_death", death_rate=0.005, source="LA")
model.add_transition_flow(name="warning_immunity", fractional_rate=0.01, source="R", dest="S")
# Stratify by age
age_strat = Stratification("age", AGE_STRATA, comps)
age_strat.set_population_split(AGE_SPLIT_PROPORTIONS)
age_strat.set_mixing_matrix(AGE_MIXING_MATRIX)
age_strat.add_flow_adjustments(
"infection", {s: Multiply(v) for s, v in AGE_SUSCEPTIBILITY.items()}
)
model.stratify_with(age_strat)
# Stratify by clinical status
clinical_strat = Stratification("clinical", CLINICAL_STRATA, infectious_comps)
clinical_strat.add_infectiousness_adjustments("LE", {**ADJ_BASE, "non_sympt": Overwrite(0.25)})
clinical_strat.add_infectiousness_adjustments("EA", {**ADJ_BASE, "non_sympt": Overwrite(0.25)})
clinical_strat.add_infectiousness_adjustments(
"LA",
{
**ADJ_BASE,
"non_sympt": Overwrite(0.25),
"sympt_isolate": Overwrite(0.2),
"hospital": Overwrite(0.2),
"icu": Overwrite(0.2),
},
)
clinical_strat.add_flow_adjustments(
"infect_onset",
{
"non_sympt": Multiply(0.26),
"icu": Multiply(0.01),
"hospital": Multiply(0.04),
"sympt_public": Multiply(0.66),
"sympt_isolate": Multiply(0.03),
},
)
model.stratify_with(clinical_strat)
# Request derived outputs.
model.request_output_for_flow(name="incidence", flow_name="incidence")
model.request_output_for_flow(name="progress", flow_name="progress")
for age in AGE_STRATA:
for clinical in NOTIFICATION_STRATA:
model.request_output_for_flow(
name=f"progressXage_{age}Xclinical_{clinical}",
flow_name="progress",
dest_strata={"age": age, "clinical": clinical},
)
hospital_sources = []
icu_sources = []
for age in AGE_STRATA:
icu_sources.append(f"progressXage_{age}Xclinical_icu")
hospital_sources += [
f"progressXage_{age}Xclinical_icu",
f"progressXage_{age}Xclinical_hospital",
]
model.request_aggregate_output(
name="new_hospital_admissions",
sources=hospital_sources,
)
model.request_aggregate_output(name="new_icu_admissions", sources=icu_sources)
# Get notifications, which may included people detected in-country as they progress, or imported cases which are detected.
notification_sources = [
f"progressXage_{a}Xclinical_{c}" for a in AGE_STRATA for c in NOTIFICATION_STRATA
]
model.request_aggregate_output(name="notifications", sources=notification_sources)
# Infection deaths.
model.request_output_for_flow(name="infection_deaths", flow_name="infect_death")
model.request_cumulative_output(name="accum_deaths", source="infection_deaths")
# Track hospital occupancy.
# We count all ICU and hospital late active compartments and a proportion of early active ICU cases.
model.request_output_for_compartments(
"_late_active_hospital",
compartments=["LA"],
strata={"clinical": "hospital"},
save_results=False,
)
model.request_output_for_compartments(
"icu_occupancy",
compartments=["LA"],
strata={"clinical": "icu"},
)
model.request_output_for_compartments(
"_early_active_icu",
compartments=["EA"],
strata={"clinical": "icu"},
save_results=False,
)
proportion_icu_patients_in_hospital = 0.25
model.request_function_output(
name="_early_active_icu_proportion",
func=lambda patients: patients * proportion_icu_patients_in_hospital,
sources=["_early_active_icu"],
save_results=False,
)
model.request_aggregate_output(
name="hospital_occupancy",
sources=[
"_late_active_hospital",
"icu_occupancy",
"_early_active_icu_proportion",
],
)
# Proportion seropositive
model.request_output_for_compartments(
name="_total_population", compartments=comps, save_results=False
)
model.request_output_for_compartments(name="_recovered", compartments=["R"], save_results=False)
model.request_function_output(
name="proportion_seropositive",
sources=["_recovered", "_total_population"],
func=lambda recovered, total: recovered / total,
)
return model