def decorated(*args,
title=NOT_GIVEN,
header=NOT_GIVEN,
subheader=NOT_GIVEN,
**kwargs):
st = kwargs.get("where", st_mod) if where is None else where
if header is not NOT_GIVEN and not isinstance(header, bool):
st.header(str(header))
elif subheader is not NOT_GIVEN and not isinstance(subheader, bool):
st.subheader(str(subheader))
if title is not NOT_GIVEN or default:
if subheader or level == "subheader":
st.subheader(str(title))
else:
st.header(str(title))
return fn(*args, **kwargs)
def main(embed=False, disease=None):
"""
Run application.
"""
if not embed:
st.css(keep_menu=True)
st.sidebar.logo()
title = st.empty().title
title(_("Covid risk factors"))
else:
title = lambda *args: None
opts = sidebar(title,
where=st if embed else st.sidebar,
embed=embed,
disease=disease)
show_opts = extract_keys(SHOW_OPTS, opts)
clinical_opts = extract_keys(CLINICAL_OPTS, opts)
run_opts = extract_keys(RUN_OPTS, opts)
# Start model with known R0
region = opts["region"]
plot_opts = show_opts["plot_opts"]
st.header(_("Cases and deaths"))
region.ui.epidemic_summary()
region.ui.cases_and_deaths(title=None,
download=f"cases-{region.id}.csv",
**plot_opts)
model = start_model(**opts)
group = start_group(model, **run_opts)
show_outputs(model,
group,
clinical_opts=clinical_opts,
**opts,
**show_opts)
def sidebar(where=st.sidebar) -> dict:
"""
Run sidebar that asks for user input.
"""
st = where
st.header(_("Brazilian Epidemiological indicators"))
st.subheader(_("Sections"))
opts = {k: v.title for k, v in SECTIONS.items()}
opts["download"] = _("Download data")
msg = _("Select a section")
section = st.radio(msg, list(opts), format_func=opts.get)
st.subheader(_("Filter results"))
regions = select_regions(where=st)
st.subheader(_("Options"))
opts = {
"static_table": st.checkbox(_("Static tables")),
"cmap": st.selectbox(_("Colormap"), COLOR_MAPS, index=COLOR_MAPS.index("BuPu")),
}
return {"regions": regions, "section": section, **opts}
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 sidebar(title, where=st.sidebar, embed=False, disease="covid-19"):
"""
Collect inputs in the sidebar or other location.
"""
st = where
region = st.region_input("BR", text=True)
title(_("Covid risk factors ({name})").format(name=_(region.name)))
# Scenarios
model = start_model(region, disease)
R0 = model.R0
st.header(_("Forecast scenarios"))
subs = {"R0": fmt(R0), "place": _(region.name)}
st.markdown(
_("""The computed value of R0 for {place} is **{R0}**. Let us
consider 3 different scenarios: the first progress with this value of R0, the
second increases social isolation to obtain a lower R0 and the third loosen
social isolation and correspond to a higher R0.""").format(**subs))
st.subheader("Scenario 1: more isolation")
msg = _("What is the new R0?")
R0_tight = st.slider(msg, 0.1, R0, R0 * 0.66)
st.subheader("Scenario 2: less isolation")
R0_loose = st.slider(msg, R0, max(2 * R0, 3.0), 1.33 * R0)
R0_list = (R0, R0_tight, R0_loose)
# Parameters
st.header(_("Parameters"))
st.subheader(_("Healthcare system"))
if np.isnan(region.icu_capacity):
population = region.population
msg = _("Total ICU beds")
icu = int(population / 10_000)
icu = st.number_input(msg, min_value=0, value=icu)
msg = _("Total hospital beds")
hospital = int(population / 1_000)
hospital = st.number_input(msg, min_value=0, value=hospital)
else:
icu = region.icu_capacity
hospital = region.hospital_capacity
msg = _("Fraction of ICU beds that is occupied?")
rate = 1 - st.slider(msg, 0, 100, value=75) / 100
# Options
def ask(title, **kwargs):
if embed:
return True
return st.checkbox(title, **kwargs)
st.header(_("Options"))
st.subheader(_("Plotting options"))
options = {
"logy": not ask(_("Linear scale")),
"grid": not ask(_("Hide grid"))
}
st.subheader(_("Advanced information"))
options = {
"plot_opts": options,
"show_weekday_rate": ask(_("Notification per weekday")),
}
return {
"region": region,
"icu_capacity": icu,
"hospital_capacity": hospital,
"icu_surge_capacity": rate * icu,
"hospital_surge_capacity": rate * hospital,
"R0_list": R0_list,
**options,
}
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()
values.
""")
# Show cases
cases = info.get_cases_for_region(region)
data = info.get_seair_curves_for_region(region, notification_rate=notification)
n_cases = len(cases)
ui.cases_and_deaths_plot(cases, logy=logy, grid=grid)
# Run model
cm = run_model(region, duration, R0, notification)
data_size = cm.iter - duration
# Show results
st.header("Simulation results")
st.subheader("Cases and deaths")
plot_cases_and_projection(cm, cases)
#
# Cards
#
st.subheader(_("New deaths"))
progression_cards(cm["deaths"], color="st-red")
st.subheader(_("New cases"))
opts = [_("Estimated"), _("Reported")]
is_reported = st.radio(_("Reporting"), [0, 1], format_func=opts.__getitem__)
mul = notification if is_reported else 1.0
progression_cards(cm["cases"] * mul, color="st-gray-900")
from pydemic.utils import trim_zeros
import pandas as pd
from fbprophet import Prophet
from matplotlib import pyplot as plt
from mundi import region
from pydemic.models import SEAIR
from pydemic.region import RegionT
from pydemic_ui import st
plt.gcf()
for r in [
"US", "DE", "IT", "CN", "ES", "BR", "BR-SP", "BR-MG", "BR-PA", "BR-MS"
]:
br: RegionT = region(r)
st.header(br.name)
cases = br.pydemic.epidemic_curve()
new = cases.diff().iloc[15:]
new.plot(grid=True, logy=True)
st.pyplot()
col: pd.Series = new["cases"]
# col = trim_zeros(col.where(col < 10, 0))
Y = col.reset_index().rename(columns={"date": "ds", "cases": "y"})
Y["cap"] = cap = 100 * Y["y"].max()
m = Prophet(
changepoint_range=0.9,
weekly_seasonality=True,