np.random.seed(33)
delhi = Model([
ModelUnit("Delhi",
18_000_000,
I0=T_pred[-1],
RR0=RR_pred[-1],
mobility=0)
])
delhi.run(14, np.zeros((1, 1)))
t_pred = [
dates[-1] + pd.Timedelta(days=i) for i in range(len(delhi[0].delta_T))
]
plot_RR_est(dates, RR_pred, RR_CI_upper, RR_CI_lower, CI)
PlotDevice().title("Delhi: Reproductive Number Estimate").xlabel(
"Date").ylabel("Rt", rotation=0, labelpad=20)
plt.show()
delhi[0].lower_CI[0] = T_CI_lower[-1]
delhi[0].upper_CI[0] = T_CI_upper[-1]
print(delhi[0].delta_T)
print(delhi[0].lower_CI)
print(delhi[0].upper_CI)
plot_T_anomalies(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts,
anomaly_dates, anomalies, CI)
plt.scatter(t_pred,
delhi[0].delta_T,
color="tomato",
s=4,
"dates": dates,
"RR_pred": RR_pred,
"RR_CI_upper": RR_CI_upper,
"RR_CI_lower": RR_CI_lower,
"T_pred": T_pred,
"T_CI_upper": T_CI_upper,
"T_CI_lower": T_CI_lower,
"total_cases": total_cases[2:],
"new_cases_ts": new_cases_ts,
})
print(" + Rt today:", RR_pred[-1])
plot_RR_est(dates, RR_pred, RR_CI_lower, RR_CI_upper, CI)\
.ylabel("Estimated $R_t$")\
.xlabel("Date")\
.title(state)\
.size(11, 8)\
.save(figs/f"Rt_est_{state}.png", dpi=600, bbox_inches="tight")\
.show()
plot_T_anomalies(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI)\
.ylabel("Predicted/Observed Cases")\
.xlabel("Date")\
.title(state)\
.size(11, 8)\
.save(figs/f"T_est_{state}.png", dpi=600, bbox_inches="tight")\
.show()
# estimates["anomaly"] = estimates["dates"].isin(set(anomaly_dates))
state_cases = pd.read_csv(data/"Bihar_cases_data_Jul23.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,
RR_pred, RR_CI_upper, RR_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 = convolution(window = smoothing))
plot_RR_est(dates, RR_pred, RR_CI_upper, RR_CI_lower, CI, ymin=0, ymax=4)\
.title("Bihar: Reproductive Number Estimate Comparisons")\
.xlabel("Date")\
.ylabel("Rt", rotation=0, labelpad=20)
plt.ylim(0, 4)
# public data
paths = {
"v3": [data_path(_) for _ in (1, 2)],
"v4": [data_path(_) for _ in range(3, 13)]
}
for target in paths['v3'] + paths['v4']:
download_data(data, target)
dfn = load_all_data(
v3_paths = [data/filepath for filepath in paths['v3']],
v4_paths = [data/filepath for filepath in paths['v4']]
alpha = 3.8
beta = 2.25
vs = 0.999
true_Rt = pd.read_table("./true_Rt.txt", dtype="float", squeeze=True)
obs_cases = pd.read_table("./obs_cases.txt", dtype="float", squeeze=True)
(dates, RR_pred, RR_CI_upper, RR_CI_lower, T_pred, T_CI_upper, T_CI_lower,
total_cases, new_cases_ts, anomalies, anomaly_dates) = analytical_MPVS(
obs_cases,
CI=CI,
alpha=alpha,
beta=beta,
variance_shift=vs,
smoothing=lambda ts: box_filter(ts, smoothing, smoothing // 2))
print("Rt today:", RR_pred[-1])
print("a, b, vs, MSE:", alpha, beta, vs,
((true_Rt.loc[len(true_Rt) - len(RR_pred):] - RR_pred)**2).sum())
plot_RR_est(dates, RR_pred, RR_CI_lower, RR_CI_upper, CI)\
.ylabel("Estimated $R_t$")\
.title("Synthetic Data Estimation")\
.size(11, 8)
plt.plot(true_Rt.index, true_Rt.values, 'k--', label="True $R_t$")
plt.xlim(0, 150)
plt.ylim(0, 2.5)
plt.legend()
# plt.savefig("synthetic_Rt_est.png", dpi=600, bbox_inches="tight")
plt.show()
def state_checks():
root = cwd()
data = root/"data"
# model details
gamma = 0.2
prevalence = 1
dfn = get_dataframe()
states = ["Maharashtra", "Andhra Pradesh", "Tamil Nadu", "Madhya Pradesh", "Punjab", "Gujarat", "Kerala", "Bihar"]
# first, check reimplementation against known figures
CI = 0.99
for state in states:
ts = get_time_series(dfn[
(dfn.date_announced <= "2020-05-08") &
(dfn.detected_state == state)
])
(
dates,
RR_pred, RR_CI_upper, RR_CI_lower,
T_pred, T_CI_upper, T_CI_lower,
total_cases, new_cases_ts,
anomalies, anomaly_date
) = analytical_MPVS(ts.Hospitalized, CI = CI)
plot_RR_est(dates, RR_pred, RR_CI_upper, RR_CI_lower, CI)
plt.title(f"{state}: Estimated $R_t$", loc="left", fontsize=20)
plt.figure()
plot_T_anomalies(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI)
plt.title(f"{state}: Predicted Cases", loc="left", fontsize=20)
plt.show()
# generate smoothing checks for national time series
ts = get_time_series(dfn)
CI = 0.95
# 1: box filter
RRfig, RRax = plt.subplots(3, 1, sharex=True)
T_fig, T_ax = plt.subplots(3, 1, sharex=True)
for (i, n) in enumerate((5, 10, 15)):
(
dates,
RR_pred, RR_CI_upper, RR_CI_lower,
T_pred, T_CI_upper, T_CI_lower,
total_cases, new_cases_ts,
anomalies, anomaly_dates
) = analytical_MPVS(ts.Hospitalized, CI = CI, smoothing = lambda ts: box_filter(ts, n, None))
plt.sca(RRax[i])
plot_RR_est(dates, RR_pred, RR_CI_upper, RR_CI_lower, CI)
plt.title(f"smoothing window: {n}, no local smoothing", loc = "left", fontsize = 16)
plt.sca(T_ax[i])
plot_T_anomalies(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI)
plt.title(f"smoothing window: {n}, no local smoothing", loc = "left", fontsize = 16)
plt.figure(RRfig.number)
RRfig.tight_layout()
plt.suptitle("Estimated Rt (box filter convolution)", fontsize = 20)
plt.figure(T_fig.number)
T_fig.tight_layout()
plt.suptitle("Estimated Daily Cases (box filter convolution)", fontsize = 20)
plt.show()
# 2: box filter, local smooothing
RRfig, RRax = plt.subplots(3, 1, sharex=True)
T_fig, T_ax = plt.subplots(3, 1, sharex=True)
for (i, n) in enumerate((5, 10, 15)):
s = n//2 + 1
(
dates,
RR_pred, RR_CI_upper, RR_CI_lower,
T_pred, T_CI_upper, T_CI_lower,
total_cases, new_cases_ts,
anomalies, anomaly_dates
) = analytical_MPVS(ts.Hospitalized, CI = CI, smoothing = lambda ts: box_filter(ts, n, s))
plt.sca(RRax[i])
plot_RR_est(dates, RR_pred, RR_CI_upper, RR_CI_lower, CI)
plt.title(f"smoothing window: {n}, local smoothing: last {s} points", loc = "left", fontsize = 16)
plt.sca(T_ax[i])
plot_T_anomalies(dates, T_pred, T_CI_upper, T_CI_lower, new_cases_ts, anomaly_dates, anomalies, CI)
plt.title(f"smoothing window: {n}, local smoothing: last {s} points", loc = "left", fontsize = 16)
plt.figure(RRfig.number)
RRfig.tight_layout()
plt.suptitle("Estimated Rt (box filter convolution, local smoothing)", fontsize = 20)
plt.subplots_adjust(right = 0.9)
plt.figure(T_fig.number)
T_fig.tight_layout()
plt.suptitle("Estimated Daily Cases (box filter convolution, local smoothing)", fontsize = 20)
plt.subplots_adjust(right = 0.9)
plt.show()