def run(self):
super().run()
models = ["crude", "delay", "overflow"]
kind = st.sidebar.selectbox(_("Clinical model"), models)
m = SEAIR(region="BR", disease=covid19)
cm = m.clinical(kind)
cm.ui.summary_table(subheader=_("Parameters table"))
def main(embed=False):
if not embed:
st.css(keep_menu=True)
st.sidebar.logo()
models = ["crude", "delay", "overflow"]
kind = st.sidebar.selectbox(_("Clinical model"), models)
m = SEAIR(region="BR", disease=covid19)
cm = m.clinical(kind)
cm.ui.summary_table(subheader=_("Parameters table"))
def handle_explore_option(self, option):
if option == "model":
model = SEAIR(region=self.region, disease="covid-19")
model.run(180)
object = model.clinical.overflow_model()
elif option == "region":
object = self.region
else:
import pydemic_ui.components as object
return object
def start_model(region: RegionT, disease="covid-19"):
"""
Start model with cases data for the given region.
"""
cases = region.pydemic.epidemic_curve(disease=disease,
real=True,
keep_observed=True)
m = SEAIR(region=region, disease=disease)
m.set_cases(cases, adjust_R0=True, save_observed=True)
m.info.save_event("simulation_start")
return m
def model(*, daily_cases, runner, period, disease, **kwargs):
"""
Return model from parameters
"""
m = SEAIR(disease=disease, **kwargs)
recovered = 0.0
exposed = daily_cases * m.incubation_period
infectious = daily_cases * m.infectious_period * m.Qs
asymptomatic = daily_cases * m.infectious_period * (1 - m.Qs)
susceptible = m.population - exposed - asymptomatic - infectious - recovered
m.set_ic(state=(susceptible, exposed, asymptomatic, infectious, recovered))
m = runner(m, period)
return m
def main(embed=False, disease=None):
"""
Main interface of the API explorer.
"""
if not embed:
st.css(keep_menu=True)
st.sidebar.logo()
where = st if embed else st.sidebar
st.title(_("Pydemic-UI API explorer"))
where.header(_("Options"))
region = where.text_input(_("Mundi region code"), value="BR")
try:
region = mundi.region(region)
except LookupError:
where.error("Invalid mundi region code.")
return
opts = {
"model": _("A Pydemic Model"),
"region": _("A Mundi Region"),
"components": _("Input components"),
}
msg = _("What do you want to explore?")
opt = where.radio(msg, list(opts), format_func=opts.get)
if opt == "model":
model = SEAIR(region=region, disease="covid-19")
model.run(180)
obj = model.clinical.overflow_model()
elif opt == "region":
obj = region
else:
import pydemic_ui.components as obj
explore_object_attribute(opt, obj, where=where)
def seir(run=120, region="BR", disease=covid19, **kwargs):
"""
A simple SEIR example.
"""
m = SEAIR(region=region, disease=disease, **kwargs)
m.set_ic(cases=1e-6 * m.population)
m.run(run)
return m.clinical.overflow_model()
def get_model(region):
region = mundi.region(region)
data = region.pydemic.epidemic_curve()
empirical_CFR = (data["deaths"] / data["cases"]).mean()
notification_rate = min(0.5, covid19.CFR(region=region) / empirical_CFR)
m = SEAIR(region=region, R0=R0[region.id])
real_data = data.copy()
real_data["cases"] /= notification_rate
m.set_cases(real_data, save_observed=True)
m.run(60)
return m.clinical.overflow_model()
return predictor
def fit(self, data):
"""
Fit model to epidemic curve.
"""
self.set_cases(data, adjust_R0=True)
Model.predictor = predictor
Model.fit = fit
region = mundi.region("IT")
model = SEAIR(region=region).clinical.overflow_model()
params = model.disease_params
predictor = model.predictor()
curves: pd.DataFrame = region.pydemic.epidemic_curve()
curves = curves.reset_index().drop_duplicates("date",
keep="first").set_index("date")
tf = curves.index[-1]
notification_ratio = params.CFR / (curves.loc[tf, "deaths"] /
curves.loc[tf, "cases"])
curves["cases"] /= notification_ratio | dbg
start, end = curves.index[[0, -1]]
full_index = pd.to_datetime(np.arange((end - start).days),
unit="D",
def test_seair_initialization(self):
# Probability of developing symptoms
assert SEAIR(disease="disease").prob_symptoms == 1.0
assert SEAIR(disease=covid19).prob_symptoms == covid19.prob_symptoms()
assert SEAIR(prob_symptoms=0.5).prob_symptoms == 0.5
assert SEAIR(Qs=0.5).prob_symptoms == 0.5