def estimate_r0(cases_df, place, sample_size, min_days, w_date):
used_brazil = False
incidence = (cases_df[place].query("totalCases > @MIN_CASES_TH").pipe(
prepare_for_r0_estimation)[:w_date])
if len(incidence) < MIN_DAYS_r0_ESTIMATE:
used_brazil = True
incidence = (make_brazil_cases(cases_df).pipe(
prepare_for_r0_estimation)[:w_date])
Rt = ReproductionNumber(incidence=incidence,
prior_shape=5.12,
prior_scale=0.64,
si_pars={
'mean': 4.89,
'sd': 1.48
},
window_width=MIN_DAYS_r0_ESTIMATE - 2)
Rt.compute_posterior_parameters()
return Rt.sample_from_posterior(sample_size=sample_size)
def run_Rt_estimation(incidence, prior_shape, prior_scale, mean_si, sd_si,
t_max, window_width):
si_pars = {'mean': mean_si, 'sd': sd_si}
Rt = ReproductionNumber(incidence=incidence,
si_pars=si_pars,
prior_shape=prior_shape,
prior_scale=prior_scale,
window_width=window_width)
Rt.compute_posterior_parameters()
Rt_posterior_sample = Rt.sample_from_posterior(sample_size=N)
Rt.compute_posterior_summaries(posterior_sample=Rt_posterior_sample,
t_max=t_max)
results = Rt
return results