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updated based on test excel results

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This commit is contained in:
Luis Aleixo 2022-10-07 09:03:24 +02:00
parent f7b63decc3
commit 52615b1cfb
2 changed files with 24 additions and 17 deletions
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9
caimira/models.py
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@ -934,7 +934,7 @@ class Cases:
"""Probability that a randomly selected individual in a focal population is infected."""
return self.geographic_cases*virus.infectiousness_days*self.ascertainment_bias/self.geographic_population
def probability_meet_infected_person(self, virus: Virus, event_population: int, n_infected: int) -> _VectorisedFloat:
def probability_meet_infected_person(self, virus: Virus, n_infected: int, event_population: int) -> _VectorisedFloat:
"""
Probability to meet n_infected persons in an event.
From https://doi.org/10.1038/s41562-020-01000-9.
@ -1483,11 +1483,12 @@ class ExposureModel:
self, {'concentration_model.infected.number': num_infected}
)
prob_ind = exposure_model.infection_probability().mean() / 100
exposed_ind = self.exposed.number
print(prob_ind)
n = total_people - num_infected
# By means of the total probability rule
prob_at_least_one_infected = 1 - (1 - prob_ind)**(exposed_ind-1)
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, exposed_ind, num_infected))
self.geographical_data.probability_meet_infected_person(self.concentration_model.infected.virus, num_infected, total_people))
return sum_probability * 100
else:
return 0

32
caimira/tests/models/test_exposure_model.py
View file

@ -235,7 +235,7 @@ def test_infectious_dose_vectorisation(sr_model):
@pytest.mark.parametrize(
"pop, cases, infectiousness_days, AB, prob_random_individual", [
[100_000, 68, 7, 5, 0.02345],
[100_000, 68, 7, 5, 0.023800],
[200_000, 121, np.array([7, 14]), 5, np.array([0.021175, 0.042350])],
[np.array([100_000, 200_000]), 68, 14, 10, np.array([0.0952, 0.0476])],
[150_000, np.array([68, 121]), 14, 2, np.array([0.012693, 0.022587])],
@ -259,31 +259,37 @@ def test_probability_random_individual(pop, cases, infectiousness_days, AB, prob
@pytest.mark.parametrize(
"pop, cases, AB, exposed, infected, prob_meet_infected_person", [
[100_000, 68, 5, 10, 1, 0.306889],
[200_000, 121, 5, 10, 1, 0.286890],
[np.array([100_000, 200_000]), 68, 10, 15, 2, np.array([0.259207, 0.126199])],
[150_000, np.array([68, 121]), 2, np.array([10, 15]), np.array([1, 2]), np.array([0.113147, 0.039803])],
[100000, 68, 5, 10, 1, 0.321509274],
[100000, 121, 5, 20, 1, 0.302950694],
[100000, np.array([68, 121]), 5, np.array([10, 20]), 1, np.array([0.321509274, 0.302950694])],
]
)
def test_prob_meet_infected_person(pop, cases, AB, exposed, infected, prob_meet_infected_person):
cases = models.Cases(geographic_population=pop, geographic_cases=cases,
ascertainment_bias=AB)
virus = models.Virus.types['SARS_CoV_2']
np.testing.assert_allclose(cases.probability_meet_infected_person(virus, exposed, infected),
np.testing.assert_allclose(cases.probability_meet_infected_person(virus, infected, exposed+infected),
prob_meet_infected_person, rtol=0.05)
@pytest.mark.parametrize(
"population, cm, pop, cases, AB, probabilistic_exposure_probability",[
[populations[1], known_concentrations(lambda t: 36.),
100000, 68, 5, 38.594805],
[populations[0], known_concentrations(lambda t: 36.),
100000, 121, 2, 29.138216],
"exposed_population, cm, pop, cases, AB, probabilistic_exposure_probability",[
[10, known_concentrations(lambda t: 36.),
100000, 68, 5, 41.51920685],
[20, known_concentrations(lambda t: 72.),
100000, 68, 5, 64.09068488],
[30, known_concentrations(lambda t: 1.2),
100000, 68, 5, 55.93154502],
])
def test_probabilistic_exposure_probability(population, cm,
def test_probabilistic_exposure_probability(exposed_population, cm,
pop, AB, cases, probabilistic_exposure_probability):
population = models.Population(
exposed_population, models.PeriodicInterval(120, 60), models.Mask.types['Type I'],
models.Activity.types['Standing'], host_immunity=0.,)
model = ExposureModel(cm, (), population, models.Cases(geographic_population=pop,
geographic_cases=cases, ascertainment_bias=AB))
geographic_cases=cases, ascertainment_bias=AB),)
np.testing.assert_allclose(
model.total_probability_rule(), probabilistic_exposure_probability, rtol=0.05
)