def examplev1():
# length of our base campaign
duration = 30
# estimate per-day probability needed for a coverage of 30%
prob = cv.historical_vaccinate_prob.estimate_prob(duration=duration,
coverage=0.30)
print('using per-day probability of ', prob)
pfizer = cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob)
sim = cv.Sim(interventions=pfizer, use_waning=True)
sim.run()
to_plot = cv.get_default_plots(kind='sim')
to_plot['Total counts'] += ['cum_vaccinated']
to_plot['Daily counts'] += ['new_doses']
sim.plot(to_plot=to_plot)
def examplev3():
pars = {'use_waning': True}
variants = [cv.variant('b117', days=30, n_imports=10)]
sim = cv.Sim(pars=pars, variants=variants)
# length of our base campaign
duration = 30
# estimate per-day probability needed for a coverage of 30%
prob = cv.historical_vaccinate_prob.estimate_prob(duration=duration,
coverage=0.30)
print('using per-day probability of ', prob)
scenarios = {
'scen1': {
'name': 'both doses',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob)
]
}
},
'scen3': {
'name': 'first dose only',
'pars': {
'interventions': [
cv.historical_vaccinate_prob(vaccine='pfizer',
days=np.arange(-duration, 0),
prob=prob,
compliance=[1.0, 0.0])
]
}
},
}
scens = cv.Scenarios(sim=sim, scenarios=scenarios)
scens.run()
to_plot = cv.get_default_plots(kind='scenarios')
to_plot.pop(2)
to_plot.update({'Cumulative doses': ['cum_vaccinated', 'cum_doses']})
scens.plot(to_plot=to_plot)
pars=pars,
interventions=vx,
analyzers=placebo_arm(day=start_trial,
trial_size=trial_size // 2))
sims.append(sim)
# Run
msim = cv.MultiSim(sims)
msim.run(keep_people=True)
results = sc.objdict()
print('Vaccine efficiency for symptomatic covid:')
for sim in msim.sims:
vaccine = sim.label
vacc_inds = cv.true(sim.people.vaccinated
) # Find trial arm indices, those who were vaccinated
placebo_inds = sim['analyzers'][0].placebo_inds
assert (len(set(vacc_inds).intersection(set(placebo_inds))) == 0
) # Check that there is no overlap
# Calculate vaccine efficiency
VE = 1 - (np.isfinite(sim.people.date_symptomatic[vacc_inds]).sum() /
np.isfinite(sim.people.date_symptomatic[placebo_inds]).sum())
results[vaccine] = VE
print(f' {vaccine:8s}: {VE*100:0.2f}%')
# Plot
to_plot = cv.get_default_plots('default', 'scen')
to_plot['Vaccinations'] = ['cum_vaccinated']
msim.plot(to_plot=to_plot)
print('Done')
return {'vals': [1.0 for ind in inds], 'inds': inds}
pfizer = cv.vaccinate(vaccine='pfizer',
days=[start_trial],
prob=0.0,
subtarget=subtarget)
sim = cv.Sim(use_waning=True,
pars=pars,
interventions=pfizer,
analyzers=placebo_arm(day=start_trial,
trial_size=trial_size // 2))
sim.run()
# Find trial arm indices, those who were vaccinated
vacc_inds = cv.true(sim.people.vaccinated)
placebo_inds = sim['analyzers'][0].placebo_inds
# Check that there is no overlap
assert (len(set(vacc_inds).intersection(set(placebo_inds))) == 0)
# Calculate vaccine efficiency
VE = 1 - (np.isfinite(sim.people.date_symptomatic[vacc_inds]).sum() /
np.isfinite(sim.people.date_symptomatic[placebo_inds]).sum())
print('Vaccine efficiency for symptomatic covid:', VE)
# Plot
to_plot = cv.get_default_plots('default', 'sim')
to_plot['Health outcomes'] += ['cum_vaccinated']
sim.plot(to_plot=to_plot)
print('Done')