def test_acmr():
sim = InteractiveContext(
'src/vivarium_conic_lsff/model_specifications/india.yaml')
sim.step() # make sure everything is setup
pop = sim.get_population()
age_groups = pd.cut(pop.age,
bins=[0, 7 / 365, 28 / 365, 1, 5],
right=False)
mr_pipeline = sim.get_value('mortality_rate')
acmr_orig = mr_pipeline.source(pop.index).sum(axis=1)
acmr_w_risk = mr_pipeline(
pop.index).sum(axis=1) * 365 # convert back to "per person-year"
# confirm that they are *not* identical at the individual level
assert not np.allclose(
acmr_orig, acmr_w_risk,
rtol=.05), 'expect acmr to be quite different for some individuals'
# but close at pop level
assert np.allclose(
acmr_orig.groupby(age_groups).median(),
acmr_w_risk.groupby(age_groups).median(),
rtol=.1
), 'expect acmr to be within 10% of original at population level'
def test_incidence(base_config, base_plugins, disease):
year_start = base_config.time.start.year
year_end = base_config.time.end.year
time_step = pd.Timedelta(days=base_config.time.step_size)
healthy = BaseDiseaseState('healthy')
sick = BaseDiseaseState('sick')
key = f"sequela.acute_myocardial_infarction_first_2_days.incidence_rate"
transition = RateTransition(input_state=healthy,
output_state=sick,
get_data_functions={
'incidence_rate':
lambda _, builder: builder.data.load(key)
})
healthy.transition_set.append(transition)
model = DiseaseModel(disease,
initial_state=healthy,
states=[healthy, sick])
simulation = InteractiveContext(components=[TestPopulation(), model],
configuration=base_config,
plugin_configuration=base_plugins,
setup=False)
simulation._data.write(key, 0.7)
simulation.setup()
incidence_rate = simulation._values.get_value('sick.incidence_rate')
simulation.step()
assert np.allclose(from_yearly(0.7, time_step),
incidence_rate(simulation.get_population().index),
atol=0.00001)
def test_risk_deletion(base_config, base_plugins, disease):
time_step = base_config.time.step_size
time_step = pd.Timedelta(days=time_step)
year_start = base_config.time.start.year
year_end = base_config.time.end.year
base_rate = 0.7
paf = 0.1
healthy = BaseDiseaseState('healthy')
sick = BaseDiseaseState('sick')
key = "sequela.acute_myocardial_infarction_first_2_days.incidence_rate"
transition = RateTransition(input_state=healthy,
output_state=sick,
get_data_functions={
'incidence_rate':
lambda _, builder: builder.data.load(key)
})
healthy.transition_set.append(transition)
model = DiseaseModel(disease,
initial_state=healthy,
states=[healthy, sick])
class PafModifier:
@property
def name(self):
return 'paf_modifier'
def setup(self, builder):
builder.value.register_value_modifier(
'sick.incidence_rate.paf',
modifier=simulation._tables.build_table(
build_table(paf, year_start, year_end),
key_columns=('sex', ),
parameter_columns=['age', 'year'],
value_columns=None))
simulation = InteractiveContext(
components=[TestPopulation(), model,
PafModifier()],
configuration=base_config,
plugin_configuration=base_plugins,
setup=False)
simulation._data.write(key, base_rate)
simulation.setup()
incidence_rate = simulation._values.get_value('sick.incidence_rate')
simulation.step()
expected_rate = base_rate * (1 - paf)
assert np.allclose(from_yearly(expected_rate, time_step),
incidence_rate(simulation.get_population().index),
atol=0.00001)
def test_mortality_rate(base_config, base_plugins, disease):
year_start = base_config.time.start.year
year_end = base_config.time.end.year
time_step = pd.Timedelta(days=base_config.time.step_size)
healthy = BaseDiseaseState('healthy')
mort_get_data_funcs = {
'dwell_time':
lambda _, __: pd.Timedelta(days=0),
'disability_weight':
lambda _, __: 0.0,
'prevalence':
lambda _, __: build_table(0.000001, year_start - 1, year_end,
['age', 'year', 'sex', 'value']),
'excess_mortality_rate':
lambda _, __: build_table(0.7, year_start - 1, year_end),
}
mortality_state = DiseaseState('sick',
get_data_functions=mort_get_data_funcs)
healthy.add_transition(mortality_state)
model = DiseaseModel(disease,
initial_state=healthy,
states=[healthy, mortality_state])
simulation = InteractiveContext(
components=[TestPopulation(), model,
Mortality()],
configuration=base_config,
plugin_configuration=base_plugins)
mortality_rate = simulation._values.get_value('mortality_rate')
simulation.step()
# Folks instantly transition to sick so now our mortality rate should be much higher
assert np.allclose(
from_yearly(0.7, time_step),
mortality_rate(simulation.get_population().index)['sick'])
def test_dwell_time(assign_cause_mock, base_config, base_plugins, disease,
base_data):
time_step = 10
assign_cause_mock.side_effect = lambda population, *args: pd.DataFrame(
{'condition_state': 'healthy'}, index=population.index)
base_config.update(
{
'time': {
'step_size': time_step
},
'population': {
'population_size': 10
}
}, **metadata(__file__))
healthy_state = BaseDiseaseState('healthy')
data_function = base_data(0)
data_function['dwell_time'] = lambda _, __: pd.Timedelta(days=28)
data_function['disability_weight'] = lambda _, __: 0.0
event_state = DiseaseState('event', get_data_functions=data_function)
done_state = BaseDiseaseState('sick')
healthy_state.add_transition(event_state)
event_state.add_transition(done_state)
model = DiseaseModel(disease,
initial_state=healthy_state,
states=[healthy_state, event_state, done_state])
simulation = InteractiveContext(components=[TestPopulation(), model],
configuration=base_config,
plugin_configuration=base_plugins)
# Move everyone into the event state
simulation.step()
event_time = simulation._clock.time
assert np.all(simulation.get_population()[disease] == 'event')
simulation.step()
simulation.step()
# Not enough time has passed for people to move out of the event state, so they should all still be there
assert np.all(simulation.get_population()[disease] == 'event')
simulation.step()
# Now enough time has passed so people should transition away
assert np.all(simulation.get_population()[disease] == 'sick')
assert np.all(simulation.get_population().event_event_time ==
pd.to_datetime(event_time))
assert np.all(simulation.get_population().event_event_count == 1)
def test_dwell_time_with_mortality(base_config, base_plugins, disease):
year_start = base_config.time.start.year
year_end = base_config.time.end.year
time_step = 10
pop_size = 100
base_config.update(
{
'time': {
'step_size': time_step
},
'population': {
'population_size': pop_size
}
}, **metadata(__file__))
healthy_state = BaseDiseaseState('healthy')
mort_get_data_funcs = {
'dwell_time':
lambda _, __: pd.Timedelta(days=14),
'excess_mortality_rate':
lambda _, __: build_table(0.7, year_start - 1, year_end),
'disability_weight':
lambda _, __: 0.0
}
mortality_state = DiseaseState('event',
get_data_functions=mort_get_data_funcs)
done_state = BaseDiseaseState('sick')
healthy_state.add_transition(mortality_state)
mortality_state.add_transition(done_state)
model = DiseaseModel(disease,
initial_state=healthy_state,
states=[healthy_state, mortality_state, done_state])
mortality = Mortality()
simulation = InteractiveContext(
components=[TestPopulation(), model, mortality],
configuration=base_config,
plugin_configuration=base_plugins)
# Move everyone into the event state
simulation.step()
assert np.all(simulation.get_population()[disease] == 'event')
simulation.step()
# Not enough time has passed for people to move out of the event state, so they should all still be there
assert np.all(simulation.get_population()[disease] == 'event')
simulation.step()
# Make sure some people have died and remained in event state
assert (simulation.get_population()['alive'] == 'alive').sum() < pop_size
assert ((simulation.get_population()['alive'] == 'dead').sum() == (
simulation.get_population()[disease] == 'event').sum())
# enough time has passed so living people should transition away to sick
assert ((simulation.get_population()['alive'] == 'alive').sum() == (
simulation.get_population()[disease] == 'sick').sum())