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Removed exposure_between_bounds and exposure methods; created a deposited_exposure_between_bounds that considers the previous inf_aero; created deposited_exposure method that calls the deposited_exposure_between_bounds within start and stop times during the day; consider these methods to calculate the cumulative dose

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Luis Aleixo 2022-02-02 11:27:46 +01:00
parent 584031e006
commit fbe47fa4d4
2 changed files with 22 additions and 44 deletions
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2
cara/apps/calculator/report_generator.py
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@ -109,7 +109,7 @@ def calculate_report_data(model: models.ExposureModel):
exposed_occupants = model.exposed.number
expected_new_cases = np.array(model.expected_new_cases()).mean()
cumulative_doses = np.cumsum([
np.array(model.exposure_between_bounds(float(time1), float(time2))).mean()
np.array(model.deposited_exposure_between_bounds(float(time1), float(time2))).mean()
for time1, time2 in zip(times[:-1], times[1:])
])

64
cara/models.py
View file

@ -1104,11 +1104,6 @@ class ExposureModel:
elif time1 <= start and stop < time2:
exposure += self.concentration_model.normed_integrated_concentration(start, stop)
return exposure
def exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""The number of virions per meter^3 between any two times."""
return (self._normed_exposure_between_bounds(time1, time2) *
self.concentration_model.infected.emission_rate_when_present())
def _normed_exposure(self) -> _VectorisedFloat:
"""
@ -1122,37 +1117,15 @@ class ExposureModel:
return normed_exposure * self.repeats
def exposure(self) -> _VectorisedFloat:
def deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
"""
The number of virus per meter^3.
"""
emission_rate_per_aerosol = self.concentration_model.infected.emission_rate_per_aerosol_when_present()
aerosols = self.concentration_model.infected.aerosols()
diameter = self.concentration_model.infected.particle.diameter
if not np.isscalar(diameter) and diameter is not None:
# we compute first the mean of all diameter-dependent quantities
# to perform properly the Monte-Carlo integration over
# particle diameters (doing things in another order would
# lead to wrong results).
exposure_integrated = np.array(self._normed_exposure()*aerosols).mean()
else:
# in the case of a single diameter or no diameter defined,
# one should not take any mean at this stage.
exposure_integrated = self._normed_exposure()*aerosols
# then we multiply by the diameter-independent quantity
# emission_rate_per_aerosol
return exposure_integrated * emission_rate_per_aerosol
def _deposited_exposure(self) -> _VectorisedFloat:
"""
The number of virus per m^3 deposited on the respiratory tract.
The number of virus per m^3 deposited on the respiratory tract
between any two times.
"""
emission_rate_per_aerosol = self.concentration_model.infected.emission_rate_per_aerosol_when_present()
aerosols = self.concentration_model.infected.aerosols()
fdep = self.fraction_deposited()
f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
diameter = self.concentration_model.infected.particle.diameter
@ -1161,28 +1134,33 @@ class ExposureModel:
# to perform properly the Monte-Carlo integration over
# particle diameters (doing things in another order would
# lead to wrong results).
dep_exposure_integrated = np.array(self._normed_exposure() *
dep_exposure_integrated = np.array(self._normed_exposure_between_bounds(time1, time2) *
aerosols *
fdep).mean()
else:
# in the case of a single diameter or no diameter defined,
# one should not take any mean at this stage.
dep_exposure_integrated = self._normed_exposure()*aerosols*fdep
dep_exposure_integrated = self._normed_exposure_between_bounds(time1, time2)*aerosols*fdep
# then we multiply by the diameter-independent quantity
# emission_rate_per_aerosol
return dep_exposure_integrated * emission_rate_per_aerosol
return (dep_exposure_integrated * emission_rate_per_aerosol *
self.exposed.activity.inhalation_rate *
(1 - self.exposed.mask.inhale_efficiency()) * f_inf)
def _deposited_exposure(self) -> _VectorisedFloat:
"""
The number of virus per m^3 deposited on the respiratory tract.
"""
deposited_exposure = 0.0
for start, stop in self.exposed.presence.boundaries():
deposited_exposure += self.deposited_exposure_between_bounds(start, stop)
return deposited_exposure
def infection_probability(self) -> _VectorisedFloat:
deposited_exposure = self._deposited_exposure()
f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
inf_aero = (
self.exposed.activity.inhalation_rate *
(1 - self.exposed.mask.inhale_efficiency()) *
deposited_exposure * f_inf
)
inf_aero = self._deposited_exposure()
# oneoverln2 multiplied by ID_50 corresponds to ID_63.
infectious_dose = oneoverln2 * self.concentration_model.virus.infectious_dose