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dynamic exposed for incidence rate

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This commit is contained in:
Luis Aleixo 2023-05-15 15:53:50 +02:00
parent f14406de74
commit 77c0c5f3a6
1 changed files with 65 additions and 5 deletions
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70
caimira/models.py
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@ -1494,9 +1494,17 @@ class ExposureModel:
c_model.ventilation.air_exchange(c_model.room, time)) for time in c_model.state_change_times()))):
raise ValueError("If the diameter is an array, none of the ventilation parameters "
"or virus decay constant can be arrays at the same time.")
if not isinstance(self.exposed.number, int):
raise NotImplementedError("Cannot use dynamic occupancy for"
" the exposed population")
@method_cache
def population_state_change_times(self) -> typing.List[float]:
"""
All time dependent population entities on this model must provide information
about the times at which their state changes.
"""
state_change_times = set(self.concentration_model.infected.presence_interval().transition_times())
state_change_times.update(self.exposed.presence_interval().transition_times())
return sorted(state_change_times)
def long_range_fraction_deposited(self) -> _VectorisedFloat:
"""
@ -1681,6 +1689,59 @@ class ExposureModel:
else:
return 0
def dynamic_total_probability_rule(self):
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:]):
exposed_present = self.exposed.people_present(interval[1])
infected_present = self.concentration_model.infected.people_present(interval[1])
# Create an equivalent exposure model but changing the number of infected cases.
total_people = exposed_present + infected_present
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 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,
),
}
)
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
total_probability_rule.append(prob_at_least_one_infected *
self.geographical_data.probability_meet_infected_person(
self.concentration_model.infected.virus,
num_infected, total_people))
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
else:
return 0
def expected_new_cases(self) -> _VectorisedFloat:
# Create an equivalent exposure model without short-range interactions, if any.
if (len(self.short_range) == 0):
@ -1689,8 +1750,7 @@ class ExposureModel:
else:
prob = self.infection_probability()
exposed_occupants = self.exposed.number
return prob * exposed_occupants / 100
return prob * self.exposed.number / 100
def reproduction_number(self) -> _VectorisedFloat:
"""