data = root/"data"
figs = root/"figs"
total_time = 90 * days
lockdown_period = 7
gamma = 0.2
window = 10
CI = 0.95
state_cases = pd.read_csv(data/"Bihar_cases_data_Oct03.csv", parse_dates=["date_reported", "date_status_change"], dayfirst=True)
state_cases["geo_reported"] = state_cases.geo_reported.str.strip()
state_cases = state_cases[state_cases.date_reported <= "2020-09-30"]
state_ts = state_cases["date_reported"].value_counts().sort_index()
district_ts = state_cases.groupby(["geo_reported", "date_reported"])["date_reported"].count().sort_index()
districts, pops, migrations = etl.district_migration_matrix(data/"Migration Matrix - District.csv")
districts = sorted([etl.replacements.get(dn, dn) for dn in districts])
R_mandatory = dict()
for district in districts:#district_ts.index.get_level_values(0).unique():
try:
(_, Rt, *_) = analytical_MPVS(district_ts.loc[district], CI = CI, smoothing = notched_smoothing(window = 10), totals = False)
Rm = np.mean(Rt)
except ValueError as v:
Rm = 1.5
R_mandatory[district] = Rm
R_voluntary = {district: 1.2*R for (district, R) in R_mandatory.items()}
si, sf = 0, 10
import etl
simplefilter("ignore")
(data, figs) = setup()
gamma = 0.2
smoothing = 10
CI = 0.95
state_cases = pd.read_csv(data / "Bihar_cases_data_Oct03.csv",
parse_dates=["date_reported"],
dayfirst=True)
state_ts = state_cases["date_reported"].value_counts().sort_index()
district_names, population_counts, _ = etl.district_migration_matrix(
data / "Migration Matrix - District.csv")
populations = dict(zip(district_names, population_counts))
# first, look at state level predictions
(dates, Rt_pred, Rt_CI_upper, Rt_CI_lower, T_pred, T_CI_upper, T_CI_lower,
total_cases, new_cases_ts, anomalies, anomaly_dates) = analytical_MPVS(
state_ts,
CI=CI,
smoothing=notched_smoothing(window=smoothing),
totals=False)
plt.Rt(dates, Rt_pred[1:], Rt_CI_upper[1:], Rt_CI_lower[1:], CI, ymin=0, ymax=4)\
.title("\nBihar: Reproductive Number Estimate")\
.annotate(f"data from {str(dates[0]).split()[0]} to {str(dates[-1]).split()[0]}")\
.xlabel("date")\
.ylabel("$R_t$", rotation=0, labelpad=20)\