def main():
"""Main."""
a = parse_args()
p = Parameters(
current_hospitalized=a.current_hospitalized,
doubling_time=a.doubling_time,
known_infected=a.known_infected,
market_share=a.market_share,
relative_contact_rate=a.relative_contact_rate,
susceptible=a.susceptible,
n_days=a.n_days,
hospitalized=RateLos(a.hospitalized_rate, a.hospitalized_los),
icu=RateLos(a.icu_rate, a.icu_los),
ventilated=RateLos(a.ventilated_rate, a.ventilated_los),
)
raw_df = DataFrame({
"Susceptible": p.susceptible_v,
"Infected": p.infected_v,
"Recovered": p.recovered_v,
})
admits_df = build_admissions_df(p)
census_df = build_census_df(admits_df, p)
prefix = a.prefix
for df, name in (
(raw_df, "raw"),
(admits_df, "admits"),
(census_df, "census"),
):
df.to_csv(prefix + name + ".csv")
notes = "The total size of the susceptible population will be the entire catchment area for Penn Medicine entities (HUP, PAH, PMC, CCH)"
show_more_info_about_this_tool(
st=st,
parameters=p,
inputs=DEFAULTS,
notes=notes
)
# PRESENTATION
# Two more combination variable initialization / input element creation
as_date = st.checkbox(label="Present result as dates instead of days", value=False)
display_n_days_slider(st, p, DEFAULTS)
# begin format data
admissions_df = build_admissions_df(p=p) # p.n_days, *p.dispositions)
census_df = build_census_df(admissions_df, parameters=p)
# end format data
st.subheader("New Admissions")
st.markdown("Projected number of **daily** COVID-19 admissions at Penn hospitals")
st.altair_chart(
new_admissions_chart(alt, admissions_df, parameters=p, as_date=as_date), use_container_width=True
)
if st.checkbox("Show Projected Admissions in tabular form"):
draw_projected_admissions_table(st, admissions_df, as_date=as_date)
st.subheader("Admitted Patients (Census)")
st.markdown(
"Projected **census** of COVID-19 patients, accounting for arrivals and discharges at Penn hospitals"
)
st.altair_chart(
admitted_patients_chart(alt=alt, census=census_df, parameters=p, as_date=as_date), use_container_width=True