def apply(self, sim):
''' Introduce new infections with this strain '''
for ind in cvi.find_day(self.days, sim.t, interv=self, sim=sim): # Time to introduce strain
susceptible_inds = cvu.true(sim.people.susceptible)
rescale_factor = sim.rescale_vec[sim.t] if self.rescale else 1.0
n_imports = sc.randround(self.n_imports/rescale_factor) # Round stochastically to the nearest number of imports
importation_inds = np.random.choice(susceptible_inds, n_imports)
sim.people.infect(inds=importation_inds, layer='importation', strain=self.index)
return
def apply(self, sim):
''' Introduce new infections with this variant '''
for ind in cvi.find_day(self.days, sim.t, interv=self,
sim=sim): # Time to introduce variant
susceptible_inds = cvu.true(sim.people.susceptible)
rescale_factor = sim.rescale_vec[sim.t] if self.rescale else 1.0
scaled_imports = self.n_imports / rescale_factor
n_imports = sc.randround(
scaled_imports
) # Round stochastically to the nearest number of imports
if self.n_imports > 0 and n_imports == 0 and sim['verbose']:
msg = f'Warning: {self.n_imports:n} imported infections of {self.label} were specified on day {sim.t}, but given the rescale factor of {rescale_factor:n}, no agents were infected. Increase the number of imports or use more agents.'
print(msg)
importation_inds = np.random.choice(
susceptible_inds, n_imports, replace=False
) # Can't use cvu.choice() since sampling from indices
sim.people.infect(inds=importation_inds,
layer='importation',
variant=self.index)
sim.results['n_imports'][sim.t] += n_imports
return
def apply(self, sim):
''' Perform vaccination '''
t = sim.t
if t < self.start_day:
return
elif self.end_day is not None and t > self.end_day:
return
# Check that there are still vaccines
rel_t = t - self.start_day
if rel_t < len(self.daily_vaccines):
n_vaccines = sc.randround(self.daily_vaccines[rel_t] /
sim.rescale_vec[t])
else:
return
vacc_probs = np.ones(
sim.n
) # Begin by assigning equal vaccine weight (converted to a probability) to everyone
if self.subtarget is not None:
subtarget_inds, subtarget_vals = get_subtargets(
self.subtarget, sim)
vacc_probs[
subtarget_inds] = subtarget_vals # People being explicitly subtargeted
# First dose
# Don't give dose to people who have had at least one dose
vacc_inds = cvu.true(self.vaccinations > 0)
vacc_probs[vacc_inds] = 0.0
# Now choose who gets vaccinated and vaccinate them
n_vaccines = min(n_vaccines, (vacc_probs != 0).sum())
all_vacc_inds = cvu.choose_w(probs=vacc_probs,
n=n_vaccines,
unique=True) # Choose who actually tests
sim.results['new_doses'][t] += len(all_vacc_inds)
self.vaccinations[all_vacc_inds] = 1
self.delay_days[all_vacc_inds] = self.delay
self.first_dates[all_vacc_inds] = sim.t
# Calculate the effect per person
vacc_eff = self.cumulative[0] # Pull out corresponding effect sizes
rel_sus_eff = 0.5 + 0.5 * self.rel_sus
rel_symp_eff = 0.5 + 0.5 * self.rel_symp
# Apply the vaccine to people
#sim.people.rel_sus[all_vacc_inds] *= rel_sus_eff
#sim.people.symp_prob[all_vacc_inds] *= rel_symp_eff
sim.people.rel_sus[
all_vacc_inds] = self.orig_rel_sus[all_vacc_inds] * rel_sus_eff
sim.people.symp_prob[
all_vacc_inds] = self.orig_symp_prob[all_vacc_inds] * rel_symp_eff
all_vacc_inds2 = cvu.true(
(self.vaccinations == 1) * (self.delay_days > 0) *
(self.first_dates > 0) *
(self.first_dates + self.delay_days == sim.t))
rel_sus_eff2 = self.rel_sus
rel_symp_eff2 = self.rel_symp
#sim.people.rel_sus[all_vacc_inds2] *= rel_sus_eff2
#sim.people.symp_prob[all_vacc_inds2] *= rel_symp_eff2
sim.people.rel_sus[
all_vacc_inds2] = self.orig_rel_sus[all_vacc_inds2] * rel_sus_eff2
sim.people.symp_prob[all_vacc_inds2] = self.orig_symp_prob[
all_vacc_inds2] * rel_symp_eff2
self.vaccinations[all_vacc_inds2] = 2
sim.results['new_doses'][t] += len(all_vacc_inds2)
def apply(self, sim):
''' Perform vaccination '''
t = sim.t
if t < self.start_day:
return
elif self.end_day is not None and t > self.end_day:
return
# Check that there are still vaccines
rel_t = t - self.start_day
if rel_t < len(self.daily_vaccines):
n_vaccines = sc.randround(self.daily_vaccines[rel_t]/sim.rescale_vec[t]) # Correct for scaling that may be applied by rounding to the nearest number of tests
if not (n_vaccines and np.isfinite(n_vaccines)): # If there are no doses today, abort early
return
else:
if sim.rescale_vec[t] != sim['pop_scale']:
raise RuntimeError('bad rescale time')
else:
return
vacc_probs = np.ones(sim.n) # Begin by assigning equal vaccine weight (converted to a probability) to everyone
# Remove minors
ppl = sim.people
inds0 = cv.true(ppl.age <= 18)
vacc_probs[inds0] *= 0
# add age priority
inds1 = cv.true(ppl.age >= self.age_priority)
vacc_probs[inds1] *= 10
# Handle scheduling
# first dose:
vacc_probs[self.vaccinations == 0] *= self.dose_priority[0]
# time between first and second dose:
no_dose = [sim.t < (d[0] + self.delay) if len(d) > 0 else False for d in self.vaccination_dates]
vacc_probs[no_dose] *= 0
# time available for second dose:
second_dose = [sim.t >= (d[0] + self.delay) if len(d) > 0 else False for d in self.vaccination_dates]
vacc_probs[second_dose] *= self.dose_priority[1]
# Don't give dose 2 people who have had more than 1
vacc_inds = cvu.true(self.vaccinations > 1)
vacc_probs[vacc_inds] = 0.0
# Now choose who gets vaccinated and vaccinate them
n_vaccines = min(n_vaccines, (vacc_probs!=0).sum()) # Don't try to vaccinate more people than have nonzero vaccination probability
all_vacc_inds = cvu.choose_w(probs=vacc_probs, n=n_vaccines, unique=True) # Choose who actually tests
sim.results['new_doses'][t] += len(all_vacc_inds)
# Did the vaccine take?
vacc_take_inds = all_vacc_inds[np.logical_or(cvu.n_binomial(self.take_prob, len(all_vacc_inds)) & (self.vaccinations[all_vacc_inds] == 0),
self.vaccine_take[all_vacc_inds] & (self.vaccinations[all_vacc_inds] == 1))]
self.vaccine_take[vacc_take_inds] = True
# Calculate the effect per person
vacc_doses = self.vaccinations[vacc_take_inds] # Calculate current doses
eff_doses = np.minimum(vacc_doses, len(self.cumulative) - 1) # Convert to a valid index
vacc_eff = self.cumulative[eff_doses] # Pull out corresponding effect sizes
rel_trans_eff = (1.0 - vacc_eff) + vacc_eff * self.rel_trans
rel_symp_eff = (1.0 - vacc_eff) + vacc_eff * self.rel_symp
# Apply the vaccine to people
sim.people.rel_trans[vacc_take_inds] = self.orig_rel_trans[vacc_take_inds]*rel_trans_eff
sim.people.symp_prob[vacc_take_inds] = self.orig_symp_prob[vacc_take_inds]*rel_symp_eff
self.vaccinations[all_vacc_inds] += 1
for v_ind in all_vacc_inds:
self.vaccination_dates[v_ind].append(sim.t)
return
legend_args={'loc': 'upper left'},
axis_args={'hspace': 0.4},
interval=50,
n_cols=1)
pl.axhline(1, linestyle='--', c=[0.8, 0.4, 0.4], alpha=1, lw=4)
pl.axvline(sim.day('2021-05-01'), linestyle='--')
pl.text(sim.day('2021-05-01') + 2, 8, 'ii.')
pl.axvline(sim.day('2021-04-06'), linestyle='--')
pl.text(sim.day('2021-04-06') + 2, 8, 'i.')
pl.title('Scenario 6: Effective reproduction number')
#%%
total = []
for i in range(18):
result = []
result.append(sc.randround(sims[i].results['cum_infections'][-1]))
result.append(sc.randround(sims[i].results['cum_symptomatic'][-1]))
result.append(sc.randround(sims[i].results['cum_deaths'][-1]))
total.append(result)
total
#%%
exposed = []
symptomatic = []
deaths = []
for i in range(18):
exposed.append(sims[i].get_analyzer().hists[0][1].tolist())
symptomatic.append(sims[i].get_analyzer().hists[0][2].tolist())
deaths.append(sims[i].get_analyzer().hists[0][3].tolist())