Added documentation on the adapted methods
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Luis Aleixo
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8566657f81
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1 changed files with 58 additions and 21 deletions
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@ -1001,7 +1001,7 @@ class ConcentrationModel:
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def _normed_concentration(self, time: float) -> _VectorisedFloat:
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"""
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Virus exposure concentration, as a function of time, and
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Virus long range exposure concentration, as a function of time, and
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normalized by the emission rate.
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The formulas used here assume that all parameters (ventilation,
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emission rate) are constant between two state changes - only
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@ -1027,18 +1027,18 @@ class ConcentrationModel:
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def concentration(self, time: float) -> _VectorisedFloat:
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"""
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Virus exposure concentration, as a function of time.
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Virus long range exposure concentration, as a function of time.
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Note that time is not vectorised. You can only pass a single float
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to this method.
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"""
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return (self._normed_concentration(time) *
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self.infected.emission_rate_when_present())
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self.infected.emission_rate_when_present())
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@method_cache
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def normed_integrated_concentration(self, start: float, stop: float) -> _VectorisedFloat:
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"""
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Get the integrated concentration of viruses in the air between the times start and stop,
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Get the integrated long range concentration of viruses in the air between the times start and stop,
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normalized by the emission rate.
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"""
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if stop <= self._first_presence_time():
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@ -1078,32 +1078,53 @@ class ShortRangeModel:
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#: Short range interactions
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presence: typing.List[SpecificInterval]
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#: Expiration type
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#: Expiration types
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expirations: typing.List[Expiration]
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#: The dilution factors for each of the expiratory activity in the short range interactions
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#: The dilution factors for each of the expiratory activity
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dilutions: typing.List[_VectorisedFloat]
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def _normed_concentration(self, concentration_model: ConcentrationModel, time: float) -> _VectorisedFloat:
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# normalized only by the viral load
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def _normed_concentration(self, concentration_model: ConcentrationModel, time: float) -> _VectorisedFloat:
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"""
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Virus short range exposure concentration, as a function of time.
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If the given time falls within a short range interval it returns the
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short range concentration normalized by the virus viral load. Otherwise
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it returns 0.
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"""
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for index, period in enumerate(self.presence):
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start, finish = tuple(period.boundaries())
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# Verifies if the given time falls within a short range interaction
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if start < time <= finish:
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dilution = self.dilutions[index]
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jet_origin_concentration = concentration_model.infected.expiration.jet_origin_concentration()
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# Long range concentration normalized by the virus viral load
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long_range_normed_concentration = concentration_model.concentration(time) / concentration_model.virus.viral_load_in_sputum
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background_concentration = concentration_model.concentration(time) / concentration_model.virus.viral_load_in_sputum
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long_range_normed_concentration=np.interp(self.expirations[index].particle.diameter, concentration_model.infected.particle.diameter, background_concentration)
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# The long range concentration values are then approximated using interpolation:
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# The set of points where we want the interpolated values are the short range particle diameters (given the current expiration);
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# The set of points with a known value are the long range particle diameters (given the initial expiration);
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# The set of known values are the long range concentration values normalized by the viral load.
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long_range_normed_concentration=np.interp(self.expirations[index].particle.diameter, concentration_model.infected.particle.diameter, long_range_normed_concentration)
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# Short range concentration formula. The long range concentration is added in the concentration method (ExposureModel).
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return ((1/dilution)*(jet_origin_concentration - long_range_normed_concentration))
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return 0.
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def short_range_concentration(self, concentration_model: ConcentrationModel, time: float):
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"""
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Virus short range exposure concentration, as a function of time.
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"""
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return (self._normed_concentration(concentration_model, time) *
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concentration_model.virus.viral_load_in_sputum)
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def normed_exposure_between_bounds(self, concentration_model, time1, time2):
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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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"""
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# TODO Implement the integration without using scipy.
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return scipy.integrate.quad_vec(lambda t: self._normed_concentration(concentration_model, t), time1, time2)[0]
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@ -1119,7 +1140,7 @@ class ExposureModel:
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#: The virus concentration model which this exposure model should consider.
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concentration_model: ConcentrationModel
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#: The short range model which this simulation model should consider.
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#: The short range model which this exposure model should consider.
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short_range: ShortRangeModel
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#: The population of non-infected people to be used in the model.
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@ -1156,11 +1177,26 @@ class ExposureModel:
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exposure += self.concentration_model.normed_integrated_concentration(start, stop)
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return exposure
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def concentration(self, time: float) -> _VectorisedFloat:
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def concentration(self, time: float) -> _VectorisedFloat:
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"""
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Virus exposure concentration, as a function of time.
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It considers the long range concentration with the
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contribution of the short range concentration.
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"""
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return (self.concentration_model.concentration(time) +
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self.short_range.short_range_concentration(self.concentration_model, time))
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def deposited_exposure_between_bounds(self, time1: float, time2: float) -> _VectorisedFloat:
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"""
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The number of virus per m^3 deposited on the respiratory tract
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between any two times.
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Considers a contribution between the short range and long range exposures:
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It calculates the deposited exposure given a short range interaction (if any).
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Then, the deposited exposure given the long range interactions is added to the
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initial deposited exposure.
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"""
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deposited_exposure = 0.
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for index, period in enumerate(self.short_range.presence):
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start, stop = tuple(period.boundaries())
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@ -1180,48 +1216,50 @@ class ExposureModel:
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fdep = self.short_range.expirations[index].particle.fraction_deposited(evaporation_factor=1.0) #ASK for evaporation factor
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diameter = self.short_range.expirations[index].particle.diameter
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# Aerosols not considered given the formula for the initial concentration at mouth/nose.
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if not np.isscalar(diameter) and diameter is not None:
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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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#aerosols *
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fdep).mean()
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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#*aerosols
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deposited_exposure += short_range_exposure*fdep
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# then we multiply by the diameter-independent quantity virus viral load
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deposited_exposure *= self.concentration_model.virus.viral_load_in_sputum
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# Background concentration
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# Long range concentration
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emission_rate_per_aerosol = self.concentration_model.infected.emission_rate_per_aerosol_when_present()
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f_inf = self.concentration_model.infected.fraction_of_infectious_virus()
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aerosols = self.concentration_model.infected.aerosols()
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fdep = self.long_range_fraction_deposited()
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diameter = self.concentration_model.infected.particle.diameter
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if not np.isscalar(diameter) and diameter is not None:
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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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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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aerosols *
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fdep).mean()
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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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dep_exposure_integrated = self._long_range_normed_exposure_between_bounds(time1, time2)*aerosols*fdep
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# then we multiply by the diameter-independent quantity emission_rate_per_aerosol,
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# and parameters of the vD equation (i.e. f_inf, BR_k and n_in).
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# and parameters of the vD equation (i.e. BR_k and n_in).
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deposited_exposure += (dep_exposure_integrated * emission_rate_per_aerosol *
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self.exposed.activity.inhalation_rate *
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(1 - self.exposed.mask.inhale_efficiency()))
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# In the end we multiply the final results by the fraction of infectious virus of the vD equation.
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return f_inf * deposited_exposure
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def deposited_exposure(self) -> _VectorisedFloat:
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@ -1266,5 +1304,4 @@ class ExposureModel:
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self, {'concentration_model.infected.number': 1}
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)
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return single_exposure_model.expected_new_cases()
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return single_exposure_model.expected_new_cases()
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