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 run_model(region, duration, R0, notification_rate):
m = models.SEAIR(region=region, disease=covid19, R0=R0)
data = info.get_seair_curves_for_region(
region, notification_rate=notification_rate)
m.set_data(data)
m.initial_cases = info.get_cases_for_region(region)["cases"].iloc[0]
m.run(duration)
return m.clinical.overflow_model()
def process_region(region, targets, duration):
data = info.get_seair_curves_for_region(region, use_deaths=True)
m = models.SEAIR(region=region, disease=covid19)
m.set_data(data)
m.initial_cases = info.get_cases_for_region(region)["cases"].iloc[0]
out = {}
for level in targets:
new = m.copy(name=_("Isolation {}").format(pc(level / 100)))
new.R0 *= 1 - level / 100
new.run(duration)
out[level] = new.clinical.overflow_model()
return MappingProxyType(out)
def __run_simulations(self, region, duration) -> Tuple[Model, ModelGroup]:
targets = self.user_inputs["targets"]
disease = get_disease("covid-19")
base = models.SEAIR(region=region, disease=disease, name=region.id)
base.set_cases(self.__get_cases_information(region, disease), save_observed=True)
column_names = []
R0s = []
for target in targets:
column_names.append(_("Isolation {}").format(pc(target / 100)))
R0s.append(base.R0 * (1 - target / 100))
info_group = base.split(name=column_names, R0=R0s)
info_group.run(duration)
return base, info_group.clinical.overflow_model()
def simulations(
region, targets: Sequence[int], duration, disease
) -> Tuple[Model, ModelGroup]:
disease = get_disease(disease)
base = models.SEAIR(region=region, disease=disease, name=region.id)
base.set_cases(cases(region, disease), save_observed=True)
names = []
R0s = []
for target in targets:
names.append(_("Isolation {}").format(pc(target / 100)))
R0s.append(base.R0 * (1 - target / 100))
group = base.split(name=names, R0=R0s)
group.run(duration)
return base, group.clinical.overflow_model()
def __process_region(self, region):
targets = self.user_inputs["targets"]
data = info.get_seair_curves_for_region(region, use_deaths=True)
model = models.SEAIR(region=region, disease=covid19)
model.set_data(data)
model.initial_cases = info.get_cases_for_region(region)["cases"].iloc[0]
out = {}
for level in targets:
new_model = model.copy(name=_("Isolation {}").format(pc(level / 100)))
new_model.R0 *= 1 - level / 100
new_model.run(DURATION)
out[level] = new_model.clinical.overflow_model()
return MappingProxyType(out)
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"]:
kwargs.setdefault(param, getattr(params, param))
return fit.seair_curves(cases, **kwargs)
notification = st.slider("Sub-notificação", 0.01, 0.30, value=0.13)
R0 = st.slider("R0", 0.1, 3.0, value=2.0)
m = models.SEAIR(region=region, disease=covid19, R0=R0)
data = seir_curves(cases["cases"] / notification,
params,
population=region.population)
m.set_data(data)
m.run(60)
cm = m.clinical.overflow_model()
ax = cases.plot()
ax = (cases["cases"] / notification).plot(ax=ax)
cm.plot(["cases:dates", "deaths:dates"], ax=ax, logy=True)
st.pyplot()
df = pd.concat([cases.diff(), fit_infectious(cases)], axis=1)
df = np.maximum(df, 1).dropna()