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Changed variable names

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This commit is contained in:
Luis Aleixo 2022-10-04 11:35:51 +02:00
parent c9134679fd
commit a61b88ce1b
1 changed files with 9 additions and 9 deletions
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18
caimira/models.py
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@ -916,7 +916,7 @@ class InfectedPopulation(_PopulationWithVirus):
class Cases:
"""
The geographical data to calculate the probability of having at least 1
new infection in a specific event.
new infection in a probabilistic exposure.
"""
#: Geographic location population
geographic_population: int = 0
@ -931,12 +931,12 @@ class Cases:
"""Probability that a randomly selected individual in a focal population is infected."""
return self.geographic_cases*self.ascertainment_bias/self.geographic_population
def probability_meet_infected_person(self, event, x) -> _VectorisedFloat:
def probability_meet_infected_person(self, event_population: int, n_infected: int) -> _VectorisedFloat:
"""
Probability to meet x infected persons in an event.
Probability to meet n_infected persons in an event.
From https://doi.org/10.1038/s41562-020-01000-9.
"""
return sct.binom.pmf(x, event, self.probability_random_individual())
return sct.binom.pmf(n_infected, event_population, self.probability_random_individual())
@dataclass(frozen=True)
@ -1469,19 +1469,19 @@ class ExposureModel:
def total_probability_rule(self) -> _VectorisedFloat:
if (self.geographical_data.geographic_population != 0 and self.geographical_data.geographic_cases != 0):
sum_probability = 0.0
# Create an equivalent exposure model but with i infected cases
# Create an equivalent exposure model but changing the number of infected cases.
total_people = self.concentration_model.infected.number + self.exposed.number
X = (total_people if total_people < 10 else 10)
max_num_infected = (total_people if total_people < 10 else 10)
# The influence of a higher number of simultainious infected people (> 4 - 5) yields an almost negligible contirbution to the total probability.
# To be on the safe side, a hard coded limit with a safety margin of 2x was set.
# Therefore we decided a hard limit of 10 infected people.
for x in range(1, X):
for num_infected in range(1, max_num_infected):
exposure_model = nested_replace(
self, {'concentration_model.infected.number': x}
self, {'concentration_model.infected.number': num_infected}
)
prob_exposed_occupant = exposure_model.infection_probability().mean() / 100
# By means of a Binomial Distribution
sum_probability += (prob_exposed_occupant)*self.geographical_data.probability_meet_infected_person(self.exposed.number, x)
sum_probability += (prob_exposed_occupant)*self.geographical_data.probability_meet_infected_person(self.exposed.number, num_infected)
return sum_probability * 100
else:
return 0