class AgeAdjustedModel:
def __init__(self):
self.r0 = 2.65
self.total_days = 0
self.population = POP_DENVER
self.beta = None
self.susceptible = ProbState(period=0,
count=self.population - 1,
name='susceptible')
self.incubating = ProbState(period=3, name='incubating')
self.infectious = ProbState(period=3.8, count=1, name='infectious')
self.isolated_holding = ProbState(period=90, name='isolated_holding')
self.incubating.add_exit_state(self.infectious, 1)
self.incubating.normalize_states_over_period()
self.infectious.add_exit_state(self.isolated_holding, 1)
self.infectious.normalize_states_over_period()
self.subgroups = dict()
for key, value in AGE_DISTRIBUTION.items():
self.subgroups[key] = AgeGroup(SubgroupRates(ICD[key], value),
name=key)
self.sum_isolated = None
self.sum_noncrit = None
self.sum_icu = None
self.sum_icu_vent = None
self.sum_recovered = None
self.sum_deceased = None
def gather_sums(self):
# print(f"base:{len(self.susceptible.domain)}")
self.sum_isolated = [0] * len(self.susceptible.domain)
self.sum_noncrit = [0] * len(self.susceptible.domain)
self.sum_icu = [0] * len(self.susceptible.domain)
self.sum_icu_vent = [0] * len(self.susceptible.domain)
self.sum_recovered = [0] * len(self.susceptible.domain)
self.sum_deceased = [0] * len(self.susceptible.domain)
# print(f"final isolated 0-9:{len(self.subgroups['0-9'].isolated.domain)} {self.subgroups['0-9'].isolated.pending}, {self.subgroups['0-9'].isolated.count}")
for key, value in self.subgroups.items():
# print(f"adding isolated {key}: {self.sum_isolated} {value.isolated.domain}")
self.sum_isolated = np.add(self.sum_isolated,
value.isolated.domain)
self.sum_noncrit = np.add(self.sum_noncrit, value.h_noncrit.domain)
self.sum_icu = np.add(self.sum_icu, value.h_icu.domain)
self.sum_icu_vent = np.add(self.sum_icu_vent,
value.h_icu_vent.domain)
self.sum_recovered = np.add(self.sum_recovered,
value.recovered.domain)
self.sum_deceased = np.add(self.sum_deceased,
value.deceased.domain)
def reset(self):
self.total_days = 0
self.susceptible.reset()
self.susceptible.count = self.population - 1
self.incubating.reset()
self.infectious.reset()
self.infectious.count = 1
self.subgroups = dict()
for key, value in AGE_DISTRIBUTION.items():
self.subgroups[key] = AgeGroup(SubgroupRates(ICD[key], value),
name=key)
def set_r0(self, value):
self.r0 = value
def set_population(self, value):
self.population = value
def run_period(self, days):
# Integrate the SIR equations over the time grid, t.
for _ in range(0, days):
self.step_day()
def run_r0_set(self, date_offsets, r0_values):
self.reset()
day_counter = 0
for itr in range(0, len(date_offsets)):
self.set_r0(r0_values[itr])
self.beta = calc_beta(self.r0, self.dayspergen)
while day_counter < date_offsets[itr]:
self.step_day()
day_counter += 1
def step_day(self):
new_infections = self.beta * self.susceptible.count * self.infectious.count / self.population
# print(f"Day {self.total_days}, {self.beta} * {self.susceptible.count} * {self.infectious.count} / {self.population} = {new_infections}")
self.susceptible.store_pending(-new_infections)
self.incubating.store_pending(new_infections)
self.incubating.pass_downstream()
self.infectious.pass_downstream()
diagnosed = self.isolated_holding.pending
self.isolated_holding.pending = 0
for key, agegroup in self.subgroups.items():
subpop = diagnosed * agegroup.stats.pop_dist
agegroup.apply_infections(subpop)
agegroup.calculate_redistributions()
self.susceptible.apply_pending()
self.incubating.apply_pending()
self.infectious.apply_pending()
for key, agegroup in self.subgroups.items():
agegroup.apply_pending()
self.total_days += 1
class HospitalFullModel:
def __init__(self):
self.r0 = 2.65
self.total_days = 0
self.population = POP_DENVERMETRO - 1
self.beta = None
self.susceptible = ProbState(period=0, count=self.population)
self.incubating = ProbState(period=3)
self.infectious = ProbState(period=3.8, count=1)
self.isolated = ProbState(period=14)
self.unhospitalized = ProbState(period=14)
self.h_noncritical = ProbState(period=8)
self.h_critical = ProbState(period=6)
self.h_icu = ProbState(period=10)
self.recovered = ProbState(period=10000)
self.dead = ProbState(period=10000)
self.incubating.add_exit_state(self.infectious, 1)
self.incubating.normalize_states_over_period()
self.isolated.add_exit_state(self.recovered, 1)
self.isolated.normalize_states_over_period()
self.unhospitalized.add_exit_state(self.recovered, .25)
self.unhospitalized.add_exit_state(self.dead, .75)
self.unhospitalized.normalize_states_over_period()
self.infectious.add_exit_state(self.isolated, .85)
self.infectious.add_exit_state(self.h_noncritical, .11)
self.infectious.add_exit_state(self.h_critical, .4)
self.infectious.normalize_states_over_period()
self.h_noncritical.add_exit_state(self.recovered, 1)
self.h_noncritical.normalize_states_over_period()
self.h_critical.add_exit_state(self.recovered, 1)
self.h_critical.normalize_states_over_period()
self.h_icu.add_exit_state(self.recovered, .75)
self.h_icu.add_exit_state(self.dead, .25)
self.h_icu.normalize_states_over_period()
def reset(self):
self.total_days = 0
self.susceptible.reset()
self.susceptible.count = self.population - 1
self.incubating.reset()
self.infectious.reset()
self.infectious.count = 1
self.isolated.reset()
self.unhospitalized.reset()
self.h_noncritical.reset()
self.h_critical.reset()
self.h_icu.reset()
self.recovered.reset()
self.dead.reset()
def set_r0(self, value):
self.r0 = value
def set_population(self, value):
self.population = value
def run_period(self, days):
time_domain = np.linspace(0, days, days + 1)
# Initial conditions vector
init = (self.susceptible.count, self.incubating.count,
self.infectious.count, self.isolated.count,
self.h_noncritical.count, self.h_critical.count,
self.h_icu.count, self.unhospitalized.count,
self.recovered.count, self.dead.count)
# Integrate the SIR equations over the time grid, t.
results = odeint(deriv_seirh, init, time_domain, args=(self, ))
(d_susceptible, d_incubating, d_infectious, d_isolated, d_noncritical,
d_critical, d_icu, d_unhospitalized, d_recovered, d_dead) = results.T
self.total_days += days
self.susceptible.extend(d_susceptible)
self.incubating.extend(d_incubating)
self.infectious.extend(d_infectious)
self.isolated.extend(d_isolated)
self.h_noncritical.extend(d_noncritical)
self.h_critical.extend(d_critical)
self.h_icu.extend(d_icu)
self.unhospitalized.extend(d_unhospitalized)
self.recovered.extend(d_recovered)
self.dead.extend(d_dead)
def run_period2(self, days):
# Integrate the SIR equations over the time grid, t.
for _ in range(0, days):
self.step_day()
def run_r0_set(self, date_offsets, r0_values):
self.reset()
prev_date = 0
for itr in range(0, len(date_offsets)):
self.set_r0(r0_values[itr])
self.beta = calc_beta(self.r0, self.dayspergen)
self.recalculate()
span = date_offsets[itr] - prev_date + 1
self.run_period2(span)
prev_date = date_offsets[itr]
if len(self.unhospitalized.domain) != len(self.dead.domain):
raise ValueError(
f"oops, {len(self.unhospitalized.domain)} != {len(self.dead.domain)}"
)
def step_day(self):
new_infections = self.beta * self.susceptible.count * self.infectious.count / self.population
(new_symptomatic, ) = self.incubating.get_state_redist()
new_isolated, new_noncritical, new_critical = self.infectious.get_state_redist(
)
(recovered1, ) = self.isolated.get_state_redist()
(recovered2, ) = self.h_noncritical.get_state_redist()
(new_icu, ) = self.h_critical.get_state_redist()
recovered3, dead1 = self.h_icu.get_state_redist()
recovered4, dead2 = self.unhospitalized.get_state_redist()
d_susceptible = -new_infections
d_incubating = new_infections - new_symptomatic
d_infectious = new_symptomatic - (new_isolated + new_noncritical +
new_critical)
d_isolated = new_isolated - recovered1
d_noncritical = new_noncritical - recovered2
d_critical = new_critical - new_icu
d_icu = new_icu - (recovered3 + dead1)
d_unhospitalized = -(recovered4 + dead2)
d_recovered = recovered1 + recovered2 + recovered3 + recovered4
d_dead = dead1 + dead2
(d_noncritical, d_critical, d_icu,
d_unhospitalized) = adjust_for_overload(self.h_noncritical.count,
self.h_critical.count,
self.h_icu.count,
d_noncritical, d_critical,
d_icu, d_unhospitalized)
balances = (d_susceptible + d_incubating + d_infectious + d_isolated +
d_noncritical + d_critical + d_icu + d_unhospitalized +
d_recovered + d_dead)
if int(balances) != 0:
raise ValueError(f"balances {balances} != 0")
self.susceptible.adjust(d_susceptible)
self.incubating.adjust(d_incubating)
self.infectious.adjust(d_infectious)
self.isolated.adjust(d_isolated)
self.h_noncritical.adjust(d_noncritical)
self.h_critical.adjust(d_critical)
self.h_icu.adjust(d_icu)
self.unhospitalized.adjust(d_unhospitalized)
self.recovered.adjust(d_recovered)
self.dead.adjust(d_dead)
self.total_days += 1
class SEIRHModel:
def __init__(self):
self.r0 = 2.65
self.total_days = 0
self.population = POP_DENVER
self.beta = None
self.susceptible = ProbState(period=0, count=self.population)
self.incubating = ProbState(period=3)
self.infectious = ProbState(period=3.8, count=1)
self.isolated = ProbState(period=14)
self.h_noncritical = ProbState(period=8)
self.h_critical = ProbState(period=6)
self.h_icu = ProbState(period=10)
self.recovered = ProbState(period=10000)
self.dead = ProbState(period=10000)
self.incubating.add_exit_state(self.infectious, 1)
self.incubating.normalize_states_over_period()
self.isolated.add_exit_state(self.recovered, 1)
self.isolated.normalize_states_over_period()
self.infectious.add_exit_state(self.isolated, .85)
self.infectious.add_exit_state(self.h_noncritical, .11)
self.infectious.add_exit_state(self.h_critical, .4)
self.infectious.normalize_states_over_period()
self.h_noncritical.add_exit_state(self.recovered, 1)
self.h_noncritical.normalize_states_over_period()
self.h_critical.add_exit_state(self.recovered, 1)
self.h_critical.normalize_states_over_period()
self.h_icu.add_exit_state(self.recovered, .75)
self.h_icu.add_exit_state(self.dead, .25)
self.h_icu.normalize_states_over_period()
def reset(self):
self.total_days = 0
self.susceptible.reset()
self.susceptible.count = self.population - 1
self.incubating.reset()
self.infectious.reset()
self.infectious.count = 1
self.isolated.reset()
self.h_noncritical.reset()
self.h_critical.reset()
self.h_icu.reset()
self.recovered.reset()
self.dead.reset()
def set_r0(self, value):
self.r0 = value
def set_population(self, value):
self.population = value
def recalculate(self):
self.beta = calc_beta(self.r0, self.dayspergen)
def run_period(self, days):
time_domain = np.linspace(0, days, days + 1)
# Initial conditions vector
init = (self.susceptible.count, self.incubating.count,
self.infectious.count, self.isolated.count,
self.h_noncritical.count, self.h_critical.count,
self.h_icu.count, self.recovered.count, self.dead.count)
# Integrate the SIR equations over the time grid, t.
results = odeint(deriv_seirh, init, time_domain, args=(self, ))
(d_susceptible, d_incubating, d_infectious, d_isolated, d_noncritical,
d_critical, d_icu, d_recovered, d_dead) = results.T
self.total_days += days
self.susceptible.extend(d_susceptible)
self.incubating.extend(d_incubating)
self.infectious.extend(d_infectious)
self.isolated.extend(d_isolated)
self.h_noncritical.extend(d_noncritical)
self.h_critical.extend(d_critical)
self.h_icu.extend(d_icu)
self.recovered.extend(d_recovered)
self.dead.extend(d_dead)
def run_r0_set(self, date_offsets, r0_values):
self.reset()
prev_date = 0
for itr in range(0, len(date_offsets)):
self.set_r0(r0_values[itr])
self.beta = calc_beta(self.r0, self.dayspergen)
span = date_offsets[itr] - prev_date + 1
self.run_period(span)
prev_date = date_offsets[itr]