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144 | class ClusteredInfectionSeed(InfectionSeed):
""" """
def __init__(
self,
world: "World",
infection_selector: InfectionSelector,
daily_cases_per_capita_per_age_per_region: pd.DataFrame,
seed_past_infections: bool = True,
seed_strength=1.0,
account_secondary_infections=False,
):
super().__init__(
world=world,
infection_selector=infection_selector,
daily_cases_per_capita_per_age_per_region=daily_cases_per_capita_per_age_per_region,
seed_past_infections=seed_past_infections,
seed_strength=seed_strength,
account_secondary_infections=account_secondary_infections,
)
def get_total_people_to_infect(self, people, cases_per_capita_per_age):
"""
Args:
people:
cases_per_capita_per_age:
"""
people_by_age = defaultdict(int)
for person in people:
people_by_age[person.age] += 1
total = sum(
[
people_by_age[age] * cases_per_capita_per_age.loc[age]
for age in people_by_age
]
)
ret = int(total)
ret += int(random() < (total - ret))
return ret
def get_household_score(self, household, age_distribution):
"""
Args:
household:
age_distribution:
"""
if len(household.residents) == 0:
return 0
ret = 0
for resident in household.residents:
ret += age_distribution.loc[resident.age]
return ret / np.sqrt(len(household.residents))
def infect_super_area(
self, super_area, cases_per_capita_per_age, time, record=None
):
"""
Args:
super_area:
cases_per_capita_per_age:
time:
record: (Default value = None)
"""
infection_id = self.infection_selector.infection_class.infection_id()
people = super_area.people
total_to_infect = self.get_total_people_to_infect(
people=people, cases_per_capita_per_age=cases_per_capita_per_age
)
# Early exit if nothing to infect
if total_to_infect <= 0:
return
# Early exit if no households or people
if len(super_area.households) == 0 or len(people) == 0:
return
# Handle division by zero
cases_sum = cases_per_capita_per_age.sum()
if cases_sum == 0:
return
age_distribution = cases_per_capita_per_age / cases_sum
households = np.array(super_area.households)
scores = [self.get_household_score(h, age_distribution) for h in households]
# Early exit if all scores are zero
if sum(scores) == 0:
return
cum_scores = np.cumsum(scores)
seeded_households = set()
attempts = 0
max_attempts = len(households) * 10 # Prevent infinite loops
while total_to_infect > 0 and attempts < max_attempts:
attempts += 1
num = random() * cum_scores[-1]
idx = np.searchsorted(cum_scores, num)
household = households[idx]
if household.id in seeded_households:
continue
# Mark household as attempted regardless of outcome
seeded_households.add(household.id)
# Try to infect household members
infected_in_household = False
for person in household.residents:
if person.immunity.get_susceptibility(infection_id) > 0:
self.infect_person(person=person, time=time, record=record)
if time < 0:
self.bring_infection_up_to_date(
person=person, time_from_infection=-time, record=record
)
total_to_infect -= 1
infected_in_household = True
if total_to_infect <= 0:
return
# If we've tried all households and can't infect anyone, exit
if len(seeded_households) >= len(households):
return
|