def run_models(self, refs, which):
growths = self.get_growths(refs, which)
R0s = []
ms = []
ms_bad = []
ms_good = []
for st_, (g, _) in growths.iterrows():
params = covid19.params(region=st_)
R0 = min(formulas.R0_from_K("SEAIR", params, K=np.log(g)), 2.5)
R0s.append(R0)
m = models.SEAIR(disease=covid19, region=st_, R0=R0)
m.set_data(info.get_seair_curves_for_region(st_))
base = m.copy()
m.run(120)
ms.append(m.clinical.overflow_model())
m = base.copy(R0=2.74)
m.run(120)
ms_bad.append(m.clinical.overflow_model())
m = base.copy(R0=1.0)
m.run(120)
ms_good.append(m.clinical.overflow_model())
return map(tuple, [ms_good, ms, ms_bad])
def _test_params(self):
p = covid19.params()
# Aliases
assert p.Qs == p.prob_symptoms
assert p.Qsv == p.prob_severe
assert p.Qcr == p.prob_critical
# Transforms
assert p.gamma == 1 / p.infectious_period
assert p.sigma == 1 / p.incubation_period
import numpy as np
import pandas as pd
import mundi
from pydemic import fitting as fit, models
from pydemic.diseases import covid19
from pydemic_ui import st
region = mundi.region(st.text_input("Region", value="BR"))
params = covid19.params(region=region)
cases = covid19.epidemic_curve(region)
def fit_infectious(df, gamma=params.gamma, fmt="{}-smooth", **kwargs):
return pd.DataFrame(
{
fmt.format(key): _infectious_curve(col, gamma, **kwargs)
for key, col in df.items()
},
index=df.index,
)
def _infectious_curve(col, gamma, **kwargs):
cases = fit.infectious_curve(col, gamma, **kwargs)
cases *= col[-1] / cases.sum()
return cases
def seir_curves(cases, params, **kwargs):
for param in ["gamma", "sigma", "prob_symptoms", "rho", "R0"]:
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()