def progression_cards(deaths, color="st-blue"):
deaths = deaths.values[-60:]
st.cards(
{
_("7 days"): fmt(deaths[6]),
_("15 days"): fmt(deaths[14]),
_("30 days"): fmt(deaths[29]),
_("60 days"): fmt(deaths[59]),
},
color=color,
)
def show(self):
curves = self.get_epidemic_curves(self.user_inputs['states'].index).fillna(0)
# Acc cases
ax = curves.iloc[-30:, self.user_inputs['idx']::2].plot(legend=False, grid=True)
curves[self.user_inputs['loc'], self.user_inputs['which']].iloc[-30:].plot(ax=ax, legend=False, grid=True)
st.pyplot()
# Daily cases
# ax = curves.iloc[-30:, self.user_inputs['idx']::2]
# curves[self.user_inputs['loc'], self.user_inputs['which']] \
# .iloc[-30:] \
# .diff() \
# .plot(ax=ax, legend=False, grid=True)
# st.pyplot()
# # Growth factors
growths = self.get_growths(self.user_inputs['states'].index, self.user_inputs['which'])
ci = pd.DataFrame({"low": growths["value"] - growths["std"], "std": growths["std"]})
st.header("Growth factor +/- error")
ci.plot.bar(width=0.9, ylim=(0.8, 2), stacked=True, grid=True)
plt.plot(self.user_inputs['states'].index, [1] * len(self.user_inputs['states']), "k--")
st.pyplot()
# # Duplication times
st.header("Duplication time")
(np.log(2) / np.log(growths["value"])).plot.bar(grid=True, ylim=(0, 30))
st.pyplot()
# R0
st.header("R0")
params = covid19.params(region=self.user_inputs['loc'])
(
np.log(growths["value"])
.apply(lambda K: formulas.R0_from_K("SEAIR", params, K=K))
.plot.bar(width=0.9, grid=True, ylim=(0, 4))
)
st.pyplot()
ms_good, ms_keep, ms_bad = self.run_models(self.user_inputs['states'].index, self.user_inputs['which'])
# ICU overflow
for ms, msg in [
(ms_keep, "keep trends"),
(ms_bad, "no distancing"),
(ms_good, "more distancing"),
]:
st.header(f"Deaths and ICU overflow ({msg})")
deaths = pd.DataFrame({m.region.id: m["deaths:dates"] for m in ms})
deaths.plot(legend=False, color="0.5")
deaths.sum(1).plot(grid=True)
deaths[self.user_inputs['loc']].plot(legend=True, grid=True)
st.cards({"Total de mortes": fmt(deaths.iloc[-1].sum())})
st.pyplot()
data = {}
for m in ms:
overflow = m.results["dates.icu_overflow"]
if overflow:
data[m.region.id] = (overflow - pd.to_datetime(today())).days
if data:
data = pd.Series(data)
data.plot.bar(width=0.9, grid=True)
st.pyplot()
def show_outputs(base, group, region: RegionT, plot_opts, clinical_opts,
**kwargs):
"""
Show results from user input.
"""
cmodels = group.clinical.overflow_model(**clinical_opts)
cforecast = cmodels[0]
start = base.info["event.simulation_start"]
#
# Introduction
#
st.header(_("Introduction"))
st.markdown(report_intro(region))
st.cards(
{
_("Basic reproduction number"): fmt(base.R0),
_("Ascertainment rate"): pc(
base.info["observed.notification_rate"]),
},
color="st-gray-900",
)
#
# Forecast
#
st.header(_("Forecasts"))
st.markdown(forecast_intro(region))
# Infectious curve
group["infectious:dates"].plot(**plot_opts)
mark_x(start.date, "k--")
plt.legend()
plt.title(_("Active cases"))
plt.tight_layout()
st.pyplot()
st.markdown("#### " + _("Download data"))
opts = ["critical", "severe", "infectious", "cases", "deaths"]
default_columns = ["critical", "severe", "cases", "deaths"]
columns = st.multiselect(_("Select columns"),
opts,
default=default_columns)
rename = dict(zip(range(len(columns)), columns))
columns = [c + ":dates" for c in columns]
data = pd.concat([cm[columns].rename(rename, axis=1) for cm in cmodels],
axis=1,
keys=cmodels.names)
st.data_anchor(data.astype(int), f"data-{region.id}.csv")
#
# Reopening
#
st.header(_("When can we reopen?"))
st.markdown(reopening_intro(region))
st.subheader(_("Step 1: Controlling the curve"))
st.markdown(rt_intro(region))
st.subheader(_("Step 2: Testing"))
st.markdown(rt_intro(region))
if kwargs.get("show_weekday_rate"):
region.ui.weekday_rate()
st.subheader(_("Step 3: Hospital capacity"))
st.markdown(rt_intro(region))
# Hospitalization
cmodels["critical:dates"].plot(**plot_opts)
mark_x(start.date, "k--")
mark_y(cforecast.icu_surge_capacity, "k:")
plt.legend()
plt.title(_("Critical cases"))
plt.tight_layout()
st.pyplot()