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added probability of infection based on dynamic occupants

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This commit is contained in:
Luis Aleixo 2023-05-26 16:34:05 +02:00
parent 2a3c598488
commit ec54c40785
1 changed files with 25 additions and 10 deletions
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35
caimira/models.py
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@ -1637,29 +1637,44 @@ class ExposureModel:
deposited_exposure += self.long_range_deposited_exposure_between_bounds(time1, time2)
return deposited_exposure
def deposited_exposure(self) -> _VectorisedFloat:
"""
The number of virus per m^3 deposited on the respiratory tract.
"""
deposited_exposure: _VectorisedFloat = 0.0
for start, stop in self.exposed.presence_interval().boundaries():
deposited_exposure += self.deposited_exposure_between_bounds(start, stop)
deposited_exposure += (self.deposited_exposure_between_bounds(start, stop))
return deposited_exposure * self.repeats
def deposited_exposure_list(self):
"""
The number of virus per m^3 deposited on the respiratory tract.
"""
population_change_times = self.population_state_change_times()
@method_cache
def infection_probability(self) -> _VectorisedFloat:
deposited_exposure = []
for start, stop in zip(population_change_times[:-1], population_change_times[1:]):
deposited_exposure.append(self.deposited_exposure_between_bounds(start, stop))
return deposited_exposure * self.repeats
def infection_probability_list(self):
# Viral dose (vD)
vD = self.deposited_exposure()
vD_list = self.deposited_exposure_list()
# oneoverln2 multiplied by ID_50 corresponds to ID_63.
infectious_dose = oneoverln2 * self.concentration_model.virus.infectious_dose
# Probability of infection.
return (1 - np.exp(-((vD * (1 - self.exposed.host_immunity))/(infectious_dose *
self.concentration_model.virus.transmissibility_factor)))) * 100
# Probability of infection.
return [(1 - np.exp(-((vD * (1 - self.exposed.host_immunity))/(infectious_dose *
self.concentration_model.virus.transmissibility_factor)))) for vD in vD_list]
@method_cache
def infection_probability(self) -> _VectorisedFloat:
return (1 - np.prod([1 - prob for prob in self.infection_probability_list()], axis = 0)) * 100
def total_probability_rule(self) -> _VectorisedFloat:
if (isinstance(self.concentration_model.infected.number, IntPiecewiseConstant) or
isinstance(self.exposed.number, IntPiecewiseConstant)):