dT_CI_u = dT + np.sqrt(var_up_school**2 + var_up_nonschool**2)
return (dT[1:], dT_CI_l[1:], dT_CI_u[1:])
rt1_10x = run_simulation(*models(0, 1.10*school_Rt))
rt1_25x = run_simulation(*models(0, 1.25*school_Rt))
rt1_50x = run_simulation(*models(0, 1.50*school_Rt))
(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(age_ts.sum(level = 1), CI = CI, smoothing = smoothing, totals = False)
MAK = SIR(name = "MAK", population = 1.339e6, dT0 = T_pred[-1], Rt0 = Rt_pred[-1], upper_CI = T_CI_upper[-1], lower_CI = T_CI_lower[-1], mobility = 0, random_seed = 0, I0 = age_ts.sum(level = 1).sum()).run(30)
plt.daily_cases(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI,
prediction_ts = [
(MAK.dT[:-1], MAK.lower_CI[1:], MAK.upper_CI[1:], plt.PRED_PURPLE, "current social distancing"),
(*rt1_10x, "orange", "10% increase in school-age $R_t$"),
(*rt1_25x, "mediumseagreen", "25% increase in school-age $R_t$"),
(*rt1_50x, "hotpink", "50% increase in school-age $R_t$"),
])\
.xlabel("\ndate")\
.ylabel("cases\n")
# .title("\nMakassar Daily Cases - school reopening scenarios")\
# .annotate("\nBayesian training process on empirical data, with anomalies identified")
(_, r) = plt.xlim()
plt.xlim(left = pd.Timestamp("Sep 1, 2020"), right = r)
plt.ylim(bottom = 10, top = 1000)
plt.vlines(dates[-1], ymin = 1, ymax = 1000, color = "black", linestyles = "solid")
plt.semilogy()
plt.show()
.xlabel("\ndate")\
.ylabel("$R_t$\n", rotation=0, labelpad=30)\
.annotate(f"\n{window}-day smoothing window, gamma-prior Bayesian estimation method")\
.show()
logger.info("running case-forward prediction")
prediction_period = 14*days
I0 = (~cases.confirmed.isna()).sum() - (~cases.recovered.isna()).sum() - (~cases.died.isna()).sum()
IDN = SIR(name = "IDN", population = 8_819_500, dT0 = T_pred[-1], Rt0 = Rt_pred[-1], upper_CI = T_CI_upper[-1], lower_CI = T_CI_lower[-1], mobility = 0, random_seed = 0, I0 = I0)\
.run(prediction_period)
plt.daily_cases(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI,
prediction_ts = [
(IDN.dT[:-1], IDN.lower_CI[1:], IDN.upper_CI[1:], plt.PRED_PURPLE, "predicted cases")
])\
.title("\nSouth Sulawesi: Daily Cases")\
.xlabel("\ndate")\
.ylabel("cases\n")\
.annotate("\nBayesian training process on empirical data, with anomalies identified")\
.show()
# makassar estimates
mak_cases = cases[cases.regency == "Makassar"]
mak_new_cases = mak_cases.confirmed.value_counts().sort_index()
logger.info("running city-level Rt estimate")
(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(mak_new_cases, CI = CI, smoothing = smoothing, totals = False)
plt.Rt(dates, Rt_pred, Rt_CI_upper, Rt_CI_lower, CI)\
.title("\nMakassar: Reproductive Number Estimate")\
.xlabel("\ndate")\
]
model = lambda: Model.single_unit(name=state,
RR0=Rt_pred[-1],
population=pop,
infectious_period=infectious_period,
I0=T_pred[-1],
lower_CI=T_CI_lower[-1],
upper_CI=T_CI_upper[-1],
random_seed=33)
forward_pred_period = 9
t_pred = [
dates[-1] + pd.Timedelta(days=i)
for i in range(forward_pred_period + 1)
]
current = model().run(forward_pred_period)
target = simulate_PID_controller(model(), 0, forward_pred_period)
plt.Rt(dates, Rt_pred, Rt_CI_upper, Rt_CI_lower, CI, ymin = 0, ymax = 5, yaxis_colors = False)\
.adjust(left = 0.10, right = 0.95, bottom = 0.15, top = 0.95)\
.xlabel("date")\
.ylabel("$R_t$")\
.show()
plt.daily_cases(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI,
prediction_ts = [
(current[0].delta_T[1:], current[0].lower_CI[1:], current[0].upper_CI[1:], "orange", r"projection with current $R_t$"),
(target[0].delta_T[1:], target[0].lower_CI[1:], target[0].upper_CI[1:], "green", r"projection with $R_t \rightarrow 0.9$")
])\
.adjust(left = 0.10, right = 0.95, bottom = 0.15, top = 0.95)\
.xlabel("date")\
.ylabel("cases")\
.show()
np.random.seed(33)
Bihar = SIR("Bihar",
99_000_000,
dT0=T_pred[-1],
Rt0=Rt_pred[-1],
lower_CI=T_CI_lower[-1],
upper_CI=T_CI_upper[-1],
mobility=0)
Bihar.run(14)
t_pred = [dates[-1] + pd.Timedelta(days=i) for i in range(len(Bihar.dT))]
plt.daily_cases(dates, T_pred[1:], T_CI_upper[1:], T_CI_lower[1:], new_cases_ts[1:], anomaly_dates, anomalies, CI,
prediction_ts = [
(Bihar.dT, Bihar.lower_CI, Bihar.upper_CI, None, "predicted cases")
])\
.title("\nBihar: New Daily Cases")\
.annotate(f"data from {str(dates[0]).split()[0]} to {str(dates[-1]).split()[0]}; predictions until {str(t_pred[-1]).split()[0]}")\
.xlabel("date").ylabel("cases")\
.show()
# now, do district-level estimation
smoothing = 10
state_cases["geo_reported"] = state_cases.geo_reported.str.strip()
district_time_series = state_cases.groupby(
["geo_reported", "date_reported"])["date_reported"].count().sort_index()
migration = np.zeros((len(district_names), len(district_names)))
estimates = []
max_len = 1 + max(map(len, district_names))
with tqdm(etl.normalize(district_names)) as districts:
for district in districts:
districts.set_description(f"{district :<{max_len}}")
.xlabel("\ndate")\
.ylabel("$R_t$\n", rotation=0, labelpad=30)\
.annotate(f"\n{window}-day smoothing window, gamma-prior Bayesian estimation method")\
.show()
logger.info("running case-forward prediction")
prediction_period = 14*days
IDN = SIR(name = "IDN", population = 267.7e6, dT0 = T_pred[-1], Rt0 = RR_pred[-1], upper_CI = T_CI_upper[-1], lower_CI = T_CI_lower[-1], mobility = 0, random_seed = 0)\
.run(prediction_period)
plt.daily_cases(dates, T_pred[1:], T_CI_upper[1:], T_CI_lower[1:], new_cases_ts[1:], anomaly_dates, anomalies, CI,
prediction_ts = [
(IDN.dT[:-1], IDN.lower_CI[1:], IDN.upper_CI[1:], None, "predicted cases")
])\
.title("\nDKI Jakarta: Daily Cases")\
.xlabel("\ndate")\
.ylabel("cases\n")\
.annotate("\nBayesian training process on empirical data, with anomalies identified")\
.show()
logger.info("district-level projections")
district_cases = dkij.groupby(["district", "date_positiveresult"])["id"].count().sort_index()
districts = dkij.district.unique()
migration = np.zeros((len(districts), len(districts)))
estimates = []
max_len = 1 + max(map(len, districts))
with tqdm(districts) as progress:
for district in districts:
progress.set_description(f"{district :<{max_len}}")
(dates, RR_pred, RR_CI_upper, RR_CI_lower, *_) = analytical_MPVS(district_cases.loc[district], CI = CI, smoothing = smoothing, totals=False)
plt.Rt(dates, Rt_pred, RR_CI_lower, RR_CI_upper, CI)\
.ylabel("$R_t$")\
.xlabel("date")\
.title(f"\n{state}: Reproductive Number Estimate")\
.annotate(f"public data from {str(dates[0]).split()[0]} to {str(dates[-1]).split()[0]}")\
.show()
I0 = (ts.loc[state].Hospitalized - ts.loc[state].Recovered - ts.loc[state].Deceased).sum()
state_model = SIR(name = state, population = pop, dT0 = T_pred[-1], Rt0 = Rt_pred[-1], mobility = 0, I0 = I0, upper_CI = T_CI_upper[-1], lower_CI = T_CI_lower[-1], random_seed = 0).run(10)
empirical = state_ts_full[(state_ts_full.index >= "Oct 14, 2020") & (state_ts_full.index < "Oct 25, 2020")]
plt.daily_cases(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI,
prediction_ts=[
(state_model.dT, state_model.lower_CI, state_model.upper_CI, plt.PRED_PURPLE, "predicted cases"),
] + [(empirical, empirical, empirical, "black", "empirical post-prediction cases")] if state != "Maharashtra" else [])\
.ylabel("cases")\
.xlabel("date")\
.title(f"\n{state}: Daily Cases")\
.annotate("\nBayesian training process on empirical data, with anomalies identified")
_, r = plt.xlim()
plt.xlim(left = pd.Timestamp("August 01, 2020"), right = r)
plt.show()
gdf = gpd.read_file(data/f"{code}.json")
district_names = sorted(gdf.district)
district_time_series = get_time_series(df[df.detected_state == state], "detected_district").Hospitalized
migration = np.zeros((len(district_names), len(district_names)))
estimates = []
max_len = 1 + max(map(len, district_names))
with tqdm(district_time_series.index.get_level_values(0).unique()) as districts:
for district in districts: