label="mean simulated daily cases",
color="rebeccapurple")
plt.fill_between(dates, [_.min().astype(int) for _ in model.dT][:n],
[_.max().astype(int) for _ in model.dT][:n],
label="simulation range",
alpha=0.3,
color="rebeccapurple")
plt.vlines(pd.Timestamp(date),
1,
1e6,
linestyles="dashed",
label="date of seroprevalence study")
plt.legend(handlelength=1, framealpha=1)
plt.semilogy()
plt.xlim(pd.Timestamp("April 1, 2020"), dates[-1])
plt.ylim(1, 1e6)
plt.PlotDevice().xlabel("\ndate").ylabel("new daily cases\n").annotate(
"Daily Cases: Scaled Data & Simulation - Tamil Nadu, no vaccination")
plt.show()
# calculate hazards
dT = np.array([_.mean().astype(int) for _ in model.dT])
S = np.array([_.mean().astype(int) for _ in model.S])
dTx = (dT * sero_breakdown[..., None]).astype(int)
Sx = (S * COVID_age_ratios[..., None]).astype(int)
lambda_x = dTx / Sx
Pr_covid_t = np.zeros(lambda_x.shape)
Pr_covid_pre_t = np.zeros(lambda_x.shape)
Pr_covid_t[:, 0] = lambda_x[:, 0]
Pr_covid_pre_t[:, 0] = lambda_x[:, 0]
for t in range(1, len(lambda_x[0, :])):
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)
for (R0, t) in schedule:
R0_timeseries += [R0] * t
sir_model.Rt0 = R0
sir_model.run(t)
total_t += t
plt.plot(sir_model.dT)
plt.show()
plt.plot(R0_timeseries, "-", color="black", label="$R_0$")
plt.plot(sir_model.Rt, "-", color="dodgerblue", label="$R_t$")
plt.legend(framealpha=1, handlelength=1, loc="best")
plt.PlotDevice().xlabel("time").ylabel("reproductive rate").adjust(left=0.10,
bottom=0.15,
right=0.99,
top=0.99)
plt.ylim(0.5, 1.5)
plt.show()
# 1: parametric scheme:
dates, Rt, Rt_lb, Rt_ub, *_, anomalies, anomaly_dates = analytical_MPVS(
pd.DataFrame(sir_model.dT),
smoothing=convolution("uniform", 2),
CI=0.99,
totals=False)
pd = plt.Rt(dates, Rt, Rt_ub, Rt_lb, ymin = 0.5, ymax = 2.5, CI = 0.99, yaxis_colors = False, format_dates = False, critical_threshold = False)\
.xlabel("time")\
.ylabel("reproductive rate")\
.adjust(left = 0.11, bottom = 0.15, right = 0.98, top = 0.98)
plt.plot(sir_model.Rt, "-", color="white", linewidth=3, zorder=10)
sim_rt, = plt.plot(sir_model.Rt,
"-",
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="upper left")
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.note_font,
ha="center",
va="top")
plt.PlotDevice()\
.title(f"\n{label}: Mobility & Lockdown Trends")\
.annotate(annotation)\
.xlabel("\ndate")\
.ylabel("% change in mobility\n")
plt.ylim(-100, 60)
plt.xlim(left=series.date.iloc[0], right=right)
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")
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()
dkij_pub_clipped = dkij_diff[dkij_diff.index <= dkij.index.max()].iloc[1:]
for model in models.values())
prob_death = dDx / Sx
sns.set_palette(sns.color_palette("hls", 7))
dt = [simulation_start + pd.Timedelta(n, "days") for n in range(max_t - 1)]
PrD = pd.DataFrame(prob_death).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("Feb 01, 2021"))
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()