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Merge branch 'feature/exposure_model_f_dep' into 'feature/MonteCarlo'

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Adding fraction_deposited (f_dep) to ExposureModel

See merge request cara/cara!191
This commit is contained in:
Nicolas Mounet 2021-06-01 19:40:50 +00:00
commit dfe1efe0e4
4 changed files with 24 additions and 11 deletions
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10
cara/models.py
View file

@ -616,7 +616,10 @@ _ExpirationBase.types = {
@dataclass(frozen=True)
class Activity:
#: Inhalation rate in m^3/h
inhalation_rate: _VectorisedFloat
#: Exhalation rate in m^3/h
exhalation_rate: _VectorisedFloat
#: Pre-populated examples of activities.
@ -671,6 +674,8 @@ class InfectedPopulation(Population):
"""
# Emission Rate (infectious quantum / h)
# Note on units: exhalation rate is in m^3/h, aerosols in mL/cm^3
# and viral load in virus/mL -> 1e6 conversion factor
aerosols = self.expiration.aerosols(self.mask)
ER = (self.virus.viral_load_in_sputum *
@ -855,6 +860,9 @@ class ExposureModel:
#: The number of times the exposure event is repeated (default 1).
repeats: int = 1
#: The fraction of viruses actually deposited in the respiratory tract
fraction_deposited: _VectorisedFloat = 0.6
def quanta_exposure(self) -> _VectorisedFloat:
"""The number of virus quanta per meter^3."""
exposure = 0.0
@ -870,7 +878,7 @@ class ExposureModel:
inf_aero = (
self.exposed.activity.inhalation_rate *
(1 - self.exposed.mask.inhale_efficiency()) *
exposure
exposure * self.fraction_deposited
)
# Probability of infection.

3
cara/tests/conftest.py
View file

@ -32,5 +32,6 @@ def baseline_exposure_model(baseline_model):
presence=baseline_model.infected.presence,
activity=baseline_model.infected.activity,
mask=baseline_model.infected.mask,
)
),
fraction_deposited = 1.,
)

19
cara/tests/models/test_exposure_model.py
View file

@ -55,22 +55,25 @@ populations = [
@pytest.mark.parametrize(
"population, cm, expected_exposure, expected_probability",[
[populations[1], KnownConcentrations(lambda t: 1.2),
"population, cm, f_dep, expected_exposure, expected_probability",[
[populations[1], KnownConcentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([99.6803184113, 99.5181053773])],
[populations[2], KnownConcentrations(lambda t: 1.2),
[populations[2], KnownConcentrations(lambda t: 1.2), 1.,
np.array([14.4, 14.4]), np.array([97.4574432074, 98.3493482895])],
[populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])),
[populations[0], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([98.3493482895, 99.9727534893])],
[populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])),
[populations[1], KnownConcentrations(lambda t: np.array([1.2, 2.4])), 1.,
np.array([14.4, 28.8]), np.array([99.6803184113, 99.9976777757])],
[populations[0], KnownConcentrations(lambda t: 2.4), np.array([0.5, 1.]),
28.8, np.array([98.3493482895, 99.9727534893])],
])
def test_exposure_model_ndarray(population, cm,
def test_exposure_model_ndarray(population, cm, f_dep,
expected_exposure, expected_probability):
model = ExposureModel(cm, population)
model = ExposureModel(cm, population, fraction_deposited = f_dep)
np.testing.assert_almost_equal(
model.quanta_exposure(), expected_exposure
)
@ -148,5 +151,5 @@ def test_exposure_model_integral_accuracy(exposed_time_interval,
10, presence_interval, models.Mask.types['Type I'],
models.Activity.types['Standing'],
)
model = ExposureModel(conc_model, population)
model = ExposureModel(conc_model, population, fraction_deposited=1.)
np.testing.assert_allclose(model.quanta_exposure(), expected_quanta)

3
cara/tests/test_known_quantities.py
View file

@ -349,7 +349,8 @@ def build_exposure_model(concentration_model):
presence=infected.presence,
activity=infected.activity,
mask=infected.mask,
)
),
fraction_deposited = 1.,
)