def get_immunity_mult(region_model):
"""Returns the immunity multiplier, a measure of the immunity in a region.
The greater the immunity multiplier, the greater the effect of immunity.
The more populous a region, the greater the effect of immunity (since outbreaks
are usually localized)
Later on, use this multiplier by multiplying the transmission rate by:
effective_r = R_t * (1-perc_population_infected_thus_far)**immunity_mult
"""
population = region_model.region_params['population']
if region_model.country_str == 'US':
if region_model.subregion_str:
immunity_mult = IMMUNITY_MULTIPLIER_US_SUBREGION
else:
immunity_mult = IMMUNITY_MULTIPLIER
elif region_model.subregion_str:
immunity_mult = IMMUNITY_MULTIPLIER
elif population < 20000000:
immunity_mult = inv_sigmoid(10000000, 0.0000003,
IMMUNITY_MULTIPLIER - 1, 1)(population)
else:
immunity_mult = inv_sigmoid(20000000, 0.00000003,
IMMUNITY_MULTIPLIER - 1.25,
1.25)(population)
if region_model.country_str not in EARLY_IMPACTED_COUNTRIES:
# these countries may not have comprehensive early testing, hence we
# correct for that by increasing the immunity mult
immunity_mult *= 1.1
return immunity_mult
def get_immunity_mult(self):
"""Returns the immunity multiplier, a measure of the immunity in a region.
The greater the immunity multiplier, the greater the effect of immunity.
The more populous a region, the greater the effect of immunity (since outbreaks
are usually localized)
Later on, use this multiplier by multiplying the transmission rate by:
effective_r = R_t * (1-perc_population_infected_thus_far)**immunity_mult
"""
assert 0 <= IMMUNITY_MULTIPLIER <= 2, IMMUNITY_MULTIPLIER
assert 0 <= IMMUNITY_MULTIPLIER_US_SUBREGION <= 2, IMMUNITY_MULTIPLIER_US_SUBREGION
population = self.region_params['population']
if self.country_str == 'US':
if self.subregion_str:
immunity_mult = IMMUNITY_MULTIPLIER_US_SUBREGION
else:
immunity_mult = IMMUNITY_MULTIPLIER
elif self.subregion_str:
immunity_mult = IMMUNITY_MULTIPLIER
elif population < 20000000:
immunity_mult = utils.inv_sigmoid(10000000, 0.0000003,
IMMUNITY_MULTIPLIER - 1,
1)(population)
else:
immunity_mult = utils.inv_sigmoid(20000000, 0.00000003,
IMMUNITY_MULTIPLIER - 1.25,
1.25)(population)
if self.country_str not in EARLY_IMPACTED_COUNTRIES + [
'Brazil', 'Mexico'
]:
# These countries may not have comprehensive early testing, so the true prevalence
# may be higher, hence we correct for that by increasing the immunity mult
# We also do not include Brazil/Mexico due to the already-high immunity_mult
immunity_mult *= 1.1
return immunity_mult
def get_transition_sigmoid(inflection_day, rate_of_inflection, init_r_0, lockdown_r_0):
"""Returns a sigmoid function based on the specified parameters.
A sigmoid helps smooth the transition between init_r_0 and lockdown_r_0,
with the midpoint being inflection_day.
rate_of_inflection is typically a value between 0-1, with 1 being a very steep
transition. We typically use 0.2-0.5 in our projections.
"""
assert 0 < rate_of_inflection <= 1, rate_of_inflection
assert 0 < init_r_0 <= 10, init_r_0
assert 0 <= lockdown_r_0 <= 10, lockdown_r_0
shift = inflection_day
a = rate_of_inflection
b = init_r_0 - lockdown_r_0
c = lockdown_r_0
return utils.inv_sigmoid(shift, a, b, c)
def get_transition_sigmoid(inflection_idx, inflection_rate, low_value, high_value,
check_values=True):
"""Returns a sigmoid function based on the specified parameters.
A sigmoid helps smooth the transition between low_value and high_value,
with the midpoint being inflection_idx.
inflection_rate is typically a value between 0-1, with 1 being a very steep
transition. We typically use 0.2-0.5 in our projections.
"""
if check_values:
assert 0 < inflection_rate <= 1, inflection_rate
assert 0 < low_value <= 10, low_value
assert 0 <= high_value <= 10, high_value
shift = inflection_idx
a = inflection_rate
b = low_value - high_value
c = high_value
return utils.inv_sigmoid(shift, a, b, c)