label=[
"random assignment", "contact rate prioritized",
"mortality prioritized"
][i])
(ticks, _) = plt.xticks()
tags = [_.split("_")[1:-2] for _ in metric_percentiles.keys()]
plt.xticks(ticks,
labels=[""] + [
v.replace("ve", "$v_e = $") + "\n" +
p.replace("annualgoal", "$\phi = $") for (v, p) in tags
],
rotation=0)
plt.legend(ncol=3,
title_fontsize=18,
fontsize=16,
framealpha=1,
handlelength=1,
bbox_to_anchor=(1.0090, 1),
loc="lower right")
plt.PlotDevice().ylabel("deaths\n")
plt.xticks(fontsize="16")
plt.yticks(fontsize="16")
plt.grid(False, which="both", axis="x")
ylims = plt.ylim() #(800, 1150)
plt.vlines([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5], ymin=ylims[0], ymax=ylims[1])
plt.xlim(left=-0.5, right=5.5)
plt.ylim(*ylims)
plt.show()
# # plot YLLs
evaluated_YLL_percentiles = {
(model_idx, selected_model) = min(
((i, each)
for (i, each) in enumerate(models) if each.params["tested"] > 0),
key=lambda _: _[1].aic)
print(" i aic r2 beta")
for (i, model) in enumerate(models):
print("*" if i == model_idx else " ", i + 1, model.aic.round(2),
model.rsquared.round(2), model.params["tested"].round(2))
scale_factor = selected_model.params["tested"]
plt.plot(0.2093 * df[state][:, "delta", "tested"],
label="national test-scaled")
plt.plot(scale_factor * df[state][:, "delta", "tested"],
label="state test-scaled")
plt.plot(df[state][:, "delta", "confirmed"], label="confirmed")
plt.legend()
plt.PlotDevice().title(f"\n{state} / case scaling comparison").xlabel(
"\ndate").ylabel("cases\n")
plt.show()
# I vs D estimators
gamma = 0.2
window = 7 * days
CI = 0.95
smooth = notched_smoothing(window)
(dates_I, Rt_I, Rtu_I, Rtl_I, *_) = analytical_MPVS(df[state][:, "delta",
"confirmed"],
CI=CI,
smoothing=smooth,
totals=False)
n = len(dates)
plt.plot(dates,
np.array([_.mean().astype(int) for _ in model.dT][:n]),
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)
random_seed=0)
total_t = 0
schedule = [(1.01, 75), (1.4, 75), (0.9, 75)]
R0_timeseries = []
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")\
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)
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()
all_dT = sum(
np.pad([np.mean(_) for _ in model.dT[1:]], (0, max_t - len(model.Rt)))
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()