def plot_mobility(series, label, stringency = None, until = None, annotation = "Google Mobility Data; baseline mobility measured from Jan 3 - Feb 6"):
plt.plot(series.date, smoothed(series.retail_and_recreation_percent_change_from_baseline), label = "Retail/Recreation")
plt.plot(series.date, smoothed(series.grocery_and_pharmacy_percent_change_from_baseline), label = "Grocery/Pharmacy")
plt.plot(series.date, smoothed(series.parks_percent_change_from_baseline), label = "Parks")
plt.plot(series.date, smoothed(series.transit_stations_percent_change_from_baseline), label = "Transit Stations")
plt.plot(series.date, smoothed(series.workplaces_percent_change_from_baseline), label = "Workplaces")
plt.plot(series.date, smoothed(series.residential_percent_change_from_baseline), label = "Residential")
if until:
right = pd.Timestamp(until)
elif stringency is not None:
right = stringency.Date.max()
else:
right = series.date.iloc[-1]
lax = plt.gca()
if stringency is not None:
plt.sca(lax.twinx())
stringency_IN = stringency.query("CountryName == 'India'")
stringency_US = stringency.query("(CountryName == 'United States') & (RegionName.isnull())", engine = "python")
plt.plot(stringency_IN.Date, stringency_IN.StringencyIndex, 'k--', alpha = 0.6, label = "IN Measure Stringency")
plt.plot(stringency_US.Date, stringency_US.StringencyIndex, 'k.' , alpha = 0.6, label = "US Measure Stringency")
plt.PlotDevice().ylabel("lockdown stringency index", rotation = -90, labelpad = 50)
plt.legend()
plt.sca(lax)
plt.legend(loc = "lower right")
plt.fill_betweenx((-100, 60), pd.to_datetime("March 24, 2020"), pd.to_datetime("June 1, 2020"), color = "black", alpha = 0.05, zorder = -1)
plt.text(s = "national lockdown", x = pd.to_datetime("April 27, 2020"), y = -90, fontdict = plt.theme.note, ha = "center", va = "top")
plt.PlotDevice()\
.xlabel("\ndate")\
.ylabel("% change in mobility\n")
# .title(f"\n{label}: Mobility & Lockdown Trends")\
# .annotate(annotation)\
plt.ylim(-100, 60)
plt.xlim(left = series.date.iloc[0], right = right)
def demand_curves(ranking_provider,
vax_policy,
phis=[25, 50, 100, 200],
phi_benchmark=25,
N_state=N_TN):
wtp_rankings = {phi: ranking_provider(phi, vax_policy) for phi in phis}
figure = plt.figure()
lines = []
# benchmark
benchmark = wtp_rankings[phi_benchmark]
x_pop = list(
chain(*zip(benchmark.loc[0]["num_vax"].shift(1).fillna(0),
benchmark.loc[0]["num_vax"])))
y_wtp = list(
chain(*zip(benchmark.loc[0]["wtp_pc_usd"], benchmark.loc[0]
["wtp_pc_usd"])))
lines.append(
plt.plot(x_pop, y_wtp, figure=figure, color="black", linewidth=2)[0])
lines.append(plt.plot(0, 0, color="white")[0])
# plot dynamic curve
for (phi, all_wtp) in wtp_rankings.items():
daily_doses = phi * percent * annually * N_state
distributed_doses = 0
x_pop = []
y_wtp = []
t_vax = []
ranking = 0
for t in range(simulation_range):
wtp = all_wtp.loc[t].reset_index()
ranking = wtp[(wtp.index >= ranking)
& (wtp.num_vax > distributed_doses)].index.min()
if np.isnan(ranking):
break
x_pop += [distributed_doses, distributed_doses + daily_doses]
t_vax += [t, t + 1]
y_wtp += [wtp.iloc[ranking].wtp_pc_usd] * 2
distributed_doses += daily_doses
lines.append(
plt.plot(x_pop,
y_wtp,
label=f"dynamic, {vax_policy}, $\phi = ${phi}%",
figure=figure)[0])
plt.legend(lines, [f"static, t = 0, $\phi = ${phi_benchmark}%", ""] +
[f"dynamic, {vax_policy}, $\phi = ${phi}%" for phi in phis],
title="allocation",
title_fontsize="24",
fontsize="20")
plt.xticks(fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("WTP (USD)\n").xlabel("\nnumber vaccinated")
plt.ylim(0, 350)
plt.xlim(left=0, right=N_TN)
plt.show()
def outcomes_per_policy(percentiles,
metric_label,
fmt,
phis=[25, 50, 100, 200],
reference=(25, "no_vax"),
reference_color=no_vax_color,
vax_policies=["contact", "random", "mortality"],
policy_colors=[
contactrate_vax_color, random_vax_color,
mortality_vax_color
],
policy_labels=[
"contact rate priority", "random assignment",
"mortality priority"
],
spacing=0.2):
fig = plt.figure()
md, lo, hi = percentiles[reference]
*_, bars = plt.errorbar(x=[0],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt=fmt,
color=reference_color,
label="no vaccination",
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.hlines(md,
xmin=-1,
xmax=5,
linestyles="dotted",
colors=reference_color)
for (i, phi) in enumerate(phis, start=1):
for (j, (vax_policy, color, label)) in enumerate(
zip(vax_policies, policy_colors, policy_labels)):
md, lo, hi = death_percentiles[phi, vax_policy]
*_, bars = plt.errorbar(x=[i + spacing * (j - 1)],
y=[md],
yerr=[[md - lo], [hi - md]],
figure=fig,
fmt=fmt,
color=color,
label=label if i == 0 else None,
ms=12,
elinewidth=5)
[_.set_alpha(0.5) for _ in bars]
plt.legend(ncol=4,
fontsize="20",
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.xticks(range(len(phis) + 1),
[f"$\phi = {phi}$%" for phi in ([0] + phis)],
fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel(f"{metric_label}\n")
plt.gca().grid(False, axis="x")
ymin, ymax = plt.ylim()
plt.vlines(x=[0.5 + _ for _ in range(len(phis))],
ymin=ymin,
ymax=ymax,
color="gray",
alpha=0.5,
linewidths=2)
plt.ylim(ymin, ymax)
plt.xlim(-0.5, len(phis) + 1.5)
linewidth=2)
plt.xticks(fontsize="20", rotation=0)
plt.yticks(fontsize="20")
plt.legend([scatter_TN, plot_TN, scatter_TT, plot_TT], [
"Tamil Nadu (raw)", "Tamil Nadu (smoothed)", "India (raw)",
"India (smoothed)"
],
fontsize="20",
ncol=4,
framealpha=1,
handlelength=0.75,
loc="lower center",
bbox_to_anchor=(0.5, 1))
plt.gca().xaxis.set_major_formatter(plt.bY_FMT)
plt.gca().xaxis.set_minor_formatter(plt.bY_FMT)
plt.xlim(left=pd.Timestamp("March 1, 2020"),
right=pd.Timestamp("April 15, 2021"))
plt.ylim(bottom=0)
plt.PlotDevice().ylabel("per-capita infection rate\n").xlabel("\ndate")
plt.show()
# 1B: per capita vaccination rates
vax = load_vax_data()\
.reindex(pd.date_range(start = pd.Timestamp("Jan 1, 2021"), end = simulation_start, freq = "D"), fill_value = 0)\
[pd.Timestamp("Jan 1, 2021"):simulation_start]\
.drop(labels = [pd.Timestamp("2021-03-15")]) # handle NAN
plt.plot(vax.index,
vax["Tamil Nadu"] / N_TN,
color=TN_color,
label="Tamil Nadu",
linewidth=2)
figure=figure)[0]
]
plt.legend(
lines,
# ["static, t = 0, $\phi = $50%", ""] + [f"dynamic, {vax_policy}, $\phi = ${phi}%" for phi in phis],
["static, t = 0, $phi = $50%", ""] +
[f"dynamic, {vax_policy}, $phi = ${phi}%" for phi in phis],
title="allocation",
title_fontsize="24",
fontsize="20")
plt.xticks(fontsize="20")
plt.yticks(fontsize="20")
plt.PlotDevice().ylabel("TEV (USD)\n").xlabel(
"\npercentage of country vaccinated")
plt.ylim(0, 1600)
plt.xlim(left=0, right=100)
plt.show()
# 3C: YLL per million choropleth
if "3C" in figs_to_run or run_all:
india = gpd.read_file(data/"india.geojson")\
.drop(columns = ["id", "dt_code", "st_code", "year"])\
.rename(columns = {"st_nm": "state"})\
.set_index(["state", "district"])\
.rename(index = lambda s: s.replace(" and ", " And "))\
.assign(
dissolve_state = lambda _:_.index.get_level_values(0),
dissolve_district = lambda _:np.where(
_.index.isin(coalesce_states, level = 0),
_.index.get_level_values(0),
_.index.get_level_values(1)))\
data = Path("./data")
download_data(data, 'timeseries.json', "https://api.covid19india.org/v3/")
# data prep
with (data/'timeseries.json').open("rb") as fp:
df = flat_table.normalize(pd.read_json(fp)).fillna(0)
df.columns = df.columns.str.split('.', expand = True)
dates = np.squeeze(df["index"][None].values)
df = df.drop(columns = "index").set_index(dates).stack([1, 2]).drop("UN", axis = 1)
series = mobility[mobility.sub_region_1.isna()]
plt.plot(series.date, smoothed(series.retail_and_recreation_percent_change_from_baseline), label = "Retail/Recreation")
plt.fill_betweenx((-100, 60), pd.to_datetime("March 24, 2020"), pd.to_datetime("June 1, 2020"), color = "black", alpha = 0.05, zorder = -1)
plt.text(s = "national lockdown", x = pd.to_datetime("April 27, 2020"), y = -20, fontdict = plt.note_font, ha = "center", va = "top")
plt.ylim(-100, 10)
plt.xlim(series.date.min(), series.date.max())
plt.legend(loc = 'upper right')
lax = plt.gca()
plt.sca(lax.twinx())
plt.plot(df["TT"][:, "delta", "confirmed"].index, smoothed(df["TT"][:, "delta", "confirmed"].values), label = "Daily Cases", color = plt.PRED_PURPLE)
plt.legend(loc = 'lower right')
plt.PlotDevice().ylabel("new cases", rotation = -90, labelpad = 50)
plt.sca(lax)
plt.PlotDevice().title("\nIndia Mobility and Case Count Trends")\
.annotate("Google Mobility Data + Covid19India.org")\
.xlabel("\ndate")\
.ylabel("% change in mobility\n")
plt.show()
plt.plot(series.date, smoothed(series.retail_and_recreation_percent_change_from_baseline), label = "Retail/Recreation")
plt.fill_betweenx((-100, 60), pd.to_datetime("March 24, 2020"), pd.to_datetime("June 1, 2020"), color = "black", alpha = 0.05, zorder = -1)
scatter = plt.scatter(infections.index[:-7],
infections.values[:-7],
color="#CC4C75",
marker="s",
s=5,
alpha=0.5)
lineplot, = plt.plot(infections.index[:-7],
smoothed(infections.values[:-7]),
color="#CC4C75",
linewidth=2)
plt.PlotDevice()\
.l_title("daily confirmed cases in India")\
.r_title("source:\nCOVID19India")\
.axis_labels(x = "date", y = "cases", enforce_spacing = True)\
.adjust(left = 0.10, bottom = 0.15, right = 0.98, top = 0.90)
plt.xlim(infections.index[0] - one_day, infections.index[-1] + one_day)
plt.legend([scatter, lineplot],
["reported infection counts", "7-day moving average"],
handlelength=0.5,
framealpha=0,
prop={'size': 16})
plt.show()
# fig 2
mob2020 = pd.read_csv("data/2020_IN_Region_Mobility_Report.csv",
parse_dates=["date"])
mob2021 = pd.read_csv("data/2021_IN_Region_Mobility_Report.csv",
parse_dates=["date"])
mob = pd.concat([mob2020, mob2021]).set_index("date")
linear_projection(dates, Rt_pred, 7)))
estimates = pd.DataFrame(estimates)
estimates.columns = ["regency", "Rt", "Rt_CI_lower", "Rt_CI_upper", "Rt_proj"]
estimates.set_index("regency", inplace=True)
estimates.to_csv("data/SULSEL_Rt_projections.csv")
print(estimates)
gdf = gpd.read_file("data/gadm36_IDN_shp/gadm36_IDN_2.shp")\
.query("NAME_1 == 'Sulawesi Selatan'")\
.merge(estimates, left_on = "NAME_2", right_on = "regency")
choro = plt.choropleth(gdf, mappable=plt.get_cmap(0.4, 1.4, "viridis"))
for ax in choro.figure.axes[:-1]:
plt.sca(ax)
plt.xlim(left=119, right=122)
plt.ylim(bottom=-7.56, top=-1.86)
plt.show()
logger.info("adaptive control")
(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(new_cases, CI = CI, smoothing = smoothing, totals = False)
Rt = pd.DataFrame(data={"Rt": Rt_pred[1:]}, index=dates)
Rt_current = Rt_pred[-1]
Rt_m = np.mean(Rt[(Rt.index >= "April 21, 2020")
& (Rt.index <= "May 22, 2020")])[0]
Rt_v = np.mean(Rt[(Rt.index <= "April 14, 2020")])[0]
Rt_m_scaled = Rt_current + 0.75 * (Rt_m - Rt_current)
Rt_v_scaled = Rt_current + 0.75 * (Rt_v - Rt_current)
sulsel = pd.read_csv("data/3 OCT 2020 Data collection template update South Sulawesi_CASE.csv", usecols = schema.keys())\
.rename(columns = schema)\
.dropna(how = 'all')
parse_datetimes(sulsel.loc[:, "confirmed"])
sulsel = sulsel.confirmed.value_counts().sort_index()
plt.plot(dkij.index, dkij.values, color="royalblue", label="private")
plt.plot(dkij_public.diff(), color="firebrick", label="public")
plt.legend()
plt.PlotDevice()\
.title("\nJakarta: public vs private case counts")\
.xlabel("date")\
.ylabel("cases")
plt.xlim(right=dkij.index.max())
plt.ylim(top=800)
plt.show()
plt.plot(sulsel, color="royalblue", label="private", linewidth=3)
plt.plot(sulsel_public.diff(), color="firebrick", label="public")
plt.legend()
plt.PlotDevice()\
.title("\nSouth Sulawesi: public vs private case counts")\
.xlabel("date")\
.ylabel("cases")
plt.xlim(right=sulsel.index.max())
plt.show()
# correlation: dkij
dkij_diff = dkij_public.diff().dropna()
(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()
prob_deathx = dDx / Sx
prob_death = dD / S
sns.set_palette(age_group_colors)
dt = [simulation_start + pd.Timedelta(n, "days") for n in range(max_t - 1)]
PrD = pd.DataFrame(prob_deathx).T\
.rename(columns = dict(enumerate(IN_age_structure.keys())))\
.assign(t = dt)\
.set_index("t")
PrD.plot()
plt.legend(title="Age category",
title_fontsize=18,
fontsize=16,
framealpha=1,
handlelength=1)
plt.xlim(right=pd.Timestamp("Jan 01, 2022"))
plt.PlotDevice()\
.xlabel("\nDate")\
.ylabel("Probability\n")
plt.subplots_adjust(left=0.12, bottom=0.12, right=0.94, top=0.96)
plt.gca().xaxis.set_minor_locator(mpl.ticker.NullLocator())
plt.gca().xaxis.set_minor_formatter(mpl.ticker.NullFormatter())
plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
plt.xticks(fontsize="16")
plt.yticks(fontsize="16")
plt.gca().xaxis.grid(True, which="major")
plt.semilogy()
plt.ylim(bottom=1e-7)
plt.show()
