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adapted dynamic total probability rule

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Luis Aleixo 2023-05-26 16:34:20 +02:00
parent ec54c40785
commit e8cf3d160a
1 changed files with 14 additions and 34 deletions
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48
caimira/models.py
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@ -1704,16 +1704,15 @@ class ExposureModel:
else:
return 0
def dynamic_total_probability_rule(self):
def dynamic_total_probability_rule(self) -> _VectorisedFloat:
if (self.geographical_data.geographic_population != 0 and self.geographical_data.geographic_cases != 0):
total_probability_rule = []
state_change_times = self.population_state_change_times()
for interval in zip(state_change_times[:-1], state_change_times[1:]):
total_probability_rule_list = []
population_change_times = self.population_state_change_times()
for start, stop in zip(population_change_times[:-1], population_change_times[1:]):
sum_probability = 0.0
exposed_present = self.exposed.people_present(interval[1])
infected_present = self.concentration_model.infected.people_present(interval[1])
exposed_present = self.exposed.people_present(stop)
infected_present = self.concentration_model.infected.people_present(stop)
# Create an equivalent exposure model but changing the number of infected cases.
total_people = exposed_present + infected_present
@ -1723,41 +1722,22 @@ class ExposureModel:
# Therefore we decided a hard limit of 10 infected people.
for num_infected in range(1, max_num_infected + 1):
exposure_model = nested_replace(
self, {
'concentration_model.infected': InfectedPopulation(
number=num_infected,
presence=SpecificInterval(present_times = (interval, )),
mask=self.concentration_model.infected.mask,
activity=self.concentration_model.infected.activity,
host_immunity=self.concentration_model.infected.host_immunity,
virus=self.concentration_model.infected.virus,
expiration=self.concentration_model.infected.expiration,
),
'exposed': Population(
number=exposed_present,
presence=SpecificInterval(present_times=(interval,)),
mask=self.exposed.mask,
activity=self.exposed.activity,
host_immunity=self.exposed.host_immunity,
),
self, {'concentration_model.infected.number':
IntPiecewiseConstant((start, stop), (num_infected,)),
}
)
prob_ind = exposure_model.infection_probability().mean() / 100
n = total_people - num_infected
# By means of the total probability rule
prob_at_least_one_infected = 1 - (1 - prob_ind)**n
sum_probability += (prob_at_least_one_infected *
self.geographical_data.probability_meet_infected_person(
self.concentration_model.infected.virus,
num_infected, total_people))
total_probability_rule.append(sum_probability)
if (isinstance(self.exposed.number, IntPiecewiseConstant) or
isinstance(self.concentration_model.infected.number, IntPiecewiseConstant)):
return (1 - np.prod([(1 - prob) for prob in total_probability_rule])) * 100
self.geographical_data.probability_meet_infected_person(self.concentration_model.infected.virus, num_infected, total_people))
total_probability_rule_list.append(sum_probability)
return (1 - np.prod([(1 - prob) for prob in total_probability_rule_list], axis = 0)) * 100
else:
return 0
def expected_new_cases(self) -> _VectorisedFloat:
# Create an equivalent exposure model without short-range interactions, if any.