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tests for incidence rate

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This commit is contained in:
Luis Aleixo 2023-05-15 15:53:57 +02:00
parent 77c0c5f3a6
commit df77bdd1b5
1 changed files with 54 additions and 15 deletions
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69
caimira/tests/models/test_dynamic_population.py
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@ -27,12 +27,16 @@ def full_exposure_model():
short_range=(), short_range=(),
exposed=models.Population( exposed=models.Population(
number=10, number=10,
presence=models.SpecificInterval(((8, 12), (13, 17), )), presence=models.SpecificInterval(((8, 12), (13, 17), )),
mask=models.Mask.types['No mask'], mask=models.Mask.types['No mask'],
activity=models.Activity.types['Seated'], activity=models.Activity.types['Seated'],
host_immunity=0. host_immunity=0.
), ),
geographical_data=(), geographical_data=models.Cases(
geographic_population=50_000,
geographic_cases=52,
ascertainment_bias=1,
),
) )
@ -51,11 +55,37 @@ def baseline_infected_population_number():
@pytest.fixture @pytest.fixture
def dynamic_single_exposure_model(full_exposure_model, baseline_infected_population_number): def baseline_exposed_population_number():
return models.Population(
number=models.IntPiecewiseConstant(
(8, 12, 13, 17), (10, 0, 10)),
presence=None,
mask=models.Mask.types['No mask'],
activity=models.Activity.types['Seated'],
host_immunity=0.,
)
@pytest.fixture
def dynamic_infected_single_exposure_model(full_exposure_model, baseline_infected_population_number):
return dc_utils.nested_replace(full_exposure_model, return dc_utils.nested_replace(full_exposure_model,
{'concentration_model.infected': baseline_infected_population_number, }) {'concentration_model.infected': baseline_infected_population_number, })
@pytest.fixture
def dynamic_exposed_single_exposure_model(full_exposure_model, baseline_exposed_population_number):
return dc_utils.nested_replace(full_exposure_model,
{'exposed': baseline_exposed_population_number, })
@pytest.fixture
def dynamic_population_exposure_model(full_exposure_model, baseline_infected_population_number ,baseline_exposed_population_number):
return dc_utils.nested_replace(full_exposure_model, {
'concentration_model.infected': baseline_infected_population_number,
'exposed': baseline_exposed_population_number,
})
@pytest.mark.parametrize( @pytest.mark.parametrize(
"time", "time",
[4., 8., 10., 12., 13., 14., 16., 20., 24.], [4., 8., 10., 12., 13., 14., 16., 20., 24.],
@ -91,16 +121,16 @@ def test_population_number(full_exposure_model: models.ExposureModel,
[4., 8., 10., 12., 13., 14., 16., 20., 24.], [4., 8., 10., 12., 13., 14., 16., 20., 24.],
) )
def test_concentration_model_dynamic_population(full_exposure_model: models.ExposureModel, def test_concentration_model_dynamic_population(full_exposure_model: models.ExposureModel,
dynamic_single_exposure_model: models.ExposureModel, dynamic_infected_single_exposure_model: models.ExposureModel,
time: float): time: float):
assert full_exposure_model.concentration(time) == dynamic_single_exposure_model.concentration(time) assert full_exposure_model.concentration(time) == dynamic_infected_single_exposure_model.concentration(time)
@pytest.mark.parametrize("number_of_infected",[1, 2, 3, 4, 5]) @pytest.mark.parametrize("number_of_infected",[1, 2, 3, 4, 5])
@pytest.mark.parametrize("time",[9., 12.5, 16.]) @pytest.mark.parametrize("time",[9., 12.5, 16.])
def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureModel, def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureModel,
dynamic_single_exposure_model: models.ExposureModel, dynamic_infected_single_exposure_model: models.ExposureModel,
time: float, time: float,
number_of_infected: int): number_of_infected: int):
@ -112,8 +142,8 @@ def test_linearity_with_number_of_infected(full_exposure_model: models.ExposureM
} }
) )
npt.assert_almost_equal(static_multiple_exposure_model.concentration(time), dynamic_single_exposure_model.concentration(time) * number_of_infected) npt.assert_almost_equal(static_multiple_exposure_model.concentration(time), dynamic_infected_single_exposure_model.concentration(time) * number_of_infected)
npt.assert_almost_equal(static_multiple_exposure_model.deposited_exposure(), dynamic_single_exposure_model.deposited_exposure() * number_of_infected) npt.assert_almost_equal(static_multiple_exposure_model.deposited_exposure(), dynamic_infected_single_exposure_model.deposited_exposure() * number_of_infected)
@pytest.mark.parametrize( @pytest.mark.parametrize(
@ -171,10 +201,19 @@ def test_dynamic_dose(full_exposure_model, time):
npt.assert_almost_equal(dynamic_exposure, np.sum(static_exposure)) npt.assert_almost_equal(dynamic_exposure, np.sum(static_exposure))
def test_dynamic_total_probability_rule(dynamic_single_exposure_model: models.ExposureModel): def test_dynamic_total_probability_rule(
with pytest.raises( full_exposure_model: models.ExposureModel,
NotImplementedError, dynamic_infected_single_exposure_model: models.ExposureModel,
match=re.escape("Cannot compute total probability " dynamic_exposed_single_exposure_model: models.ExposureModel,
"(including incidence rate) with dynamic occupancy") dynamic_population_exposure_model: models.ExposureModel):
):
dynamic_single_exposure_model.total_probability_rule() full_model_total_prob_rule = full_exposure_model.total_probability_rule()
npt.assert_almost_equal(full_model_total_prob_rule,
dynamic_population_exposure_model.dynamic_total_probability_rule())
npt.assert_almost_equal(full_model_total_prob_rule,
dynamic_infected_single_exposure_model.dynamic_total_probability_rule())
npt.assert_almost_equal(full_model_total_prob_rule,
dynamic_exposed_single_exposure_model.dynamic_total_probability_rule())