Decoupling jet exposure and the part subtracted from the background concentration, in the short-range model, in order to treat correctly the diameter integrals. This correct the probability of infection.
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Nicolas Mounet
parent
b11dd90e92
commit
9a4790d407
1 changed files with 49 additions and 21 deletions
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@ -1192,29 +1192,46 @@ class ShortRangeModel:
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elif time1 <= start and stop < time2:
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return start, stop
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def normed_exposure_between_bounds(self, concentration_model: ConcentrationModel,
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time1: float, time2: float):
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def _normed_jet_exposure_between_bounds(self,
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concentration_model: ConcentrationModel,
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time1: float, time2: float):
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"""
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Get the integrated short-range concentration of viruses in the air between the times start and stop,
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normalized by the virus viral load.
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Get the part of the integrated short-range concentration of
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viruses in the air, between the times start and stop, coming
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from the jet concentration, normalized by the viral load, and
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without dilution.
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"""
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start, stop = self.extract_between_bounds(time1, time2)
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jet_origin = self.expiration.jet_origin_concentration()
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dilution = self.dilution_factor()
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return jet_origin * (stop - start)
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total_normed_concentration_diluted = (
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concentration_model.integrated_concentration(start, stop
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)/dilution/
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concentration_model.virus.viral_load_in_sputum
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def _normed_interpolated_longrange_exposure_between_bounds(
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self, concentration_model: ConcentrationModel,
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time1: float, time2: float):
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"""
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Get the part of the integrated short-range concentration due
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to the background concentration, normalized by the viral load
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and the breathing rate, and without dilution.
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One needs to interpolate the integrated long-range concentration
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for the particle diameters defined here.
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TODO: make sure any potential extrapolation has a
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negligible effect.
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"""
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start, stop = self.extract_between_bounds(time1, time2)
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if stop<=start:
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return 0.
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normed_int_concentration = (
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concentration_model.integrated_concentration(start, stop)
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/concentration_model.virus.viral_load_in_sputum
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/concentration_model.infected.activity.exhalation_rate
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)
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total_normed_concentration_interpolated = np.interp(
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normed_int_concentration_interpolated = np.interp(
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self.expiration.particle.diameter,
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concentration_model.infected.particle.diameter,
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total_normed_concentration_diluted
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normed_int_concentration
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)
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return (jet_origin/dilution * (stop - start)
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) - total_normed_concentration_interpolated
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return normed_int_concentration_interpolated
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@dataclass(frozen=True)
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@ -1292,7 +1309,7 @@ class ExposureModel:
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# we compute first the mean of all diameter-dependent quantities
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# to perform properly the Monte-Carlo integration over
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# particle diameters (doing things in another order would
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# lead to wrong results).
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# lead to wrong results for the probability of infection).
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dep_exposure_integrated = np.array(self._long_range_normed_exposure_between_bounds(time1, time2) *
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aerosols *
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fdep).mean()
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@ -1323,24 +1340,35 @@ class ExposureModel:
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deposited_exposure = 0.
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for interaction in self.short_range:
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start, stop = interaction.extract_between_bounds(time1, time2)
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short_range_exposure = interaction.normed_exposure_between_bounds(
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short_range_jet_exposure = interaction._normed_jet_exposure_between_bounds(
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self.concentration_model, start, stop)
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short_range_lr_exposure = interaction._normed_interpolated_longrange_exposure_between_bounds(
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self.concentration_model, start, stop)
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dilution = interaction.dilution_factor()
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fdep = interaction.expiration.particle.fraction_deposited(evaporation_factor=1.0)
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diameter = interaction.expiration.particle.diameter
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# Aerosols not considered given the formula for the initial concentration at mouth/nose.
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# Aerosols not considered given the formula for the initial
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# concentration at mouth/nose.
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if diameter is not None and not np.isscalar(diameter):
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# we compute first the mean of all diameter-dependent quantities
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# to perform properly the Monte-Carlo integration over
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# particle diameters (doing things in another order would
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# lead to wrong results).
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deposited_exposure += np.array(short_range_exposure *
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fdep).mean()
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# lead to wrong results for the probability of infection).
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deposited_exposure += (np.array(short_range_jet_exposure
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* fdep).mean()/dilution
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- np.array(short_range_lr_exposure * fdep).mean()
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* self.concentration_model.infected.activity.exhalation_rate
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/dilution)
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else:
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# in the case of a single diameter or no diameter defined,
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# one should not take any mean at this stage.
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deposited_exposure += short_range_exposure*fdep
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deposited_exposure += (short_range_jet_exposure
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* fdep/dilution
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- short_range_lr_exposure * fdep
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* self.concentration_model.infected.activity.exhalation_rate
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/dilution)
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# multiply by the (diameter-independent) inhalation rate
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deposited_exposure *= interaction.activity.inhalation_rate
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