Missing 1 hospitalized

[jlubken]

Corey review, confirm, and suggest a solution.

There is an initial hospitalized patient because fitting was done for the date_first_hospitalized or date_first_hospitalized was estimated from doubling_time.

This likely causes an underestimate in growth rate for small marked share, because a single hospitalized patient represents a larger change from zero than in larger market shares.

This shows up later in test_model_cumulative_census:

    # TODO: is 1.0 for ceil function?
    diff = admits.hospitalized[1:-1] - (
        0.05 * 0.05 * (raw_df.infected[1:-1] + raw_df.recovered[1:-1]) - 1.0
    )

Missing a patient during in build_dispositions_df:

def build_dispositions_df(
    raw_df: pd.DataFrame,
    rates: Dict[str, float],
    market_share: float,
    current_date: datetime,
) -> pd.DataFrame:
    """Build dispositions dataframe of patients adjusted by rate and market_share."""
    patients = raw_df.infected + raw_df.recovered
    day = raw_df.day
    return pd.DataFrame({
        "day": day,
        "date": day.astype('timedelta64[D]') + np.datetime64(current_date),
        **{
            key: patients * rate * market_share
            for key, rate in rates.items()
        }
    })

[jlubken]

@PhilMiller and @cjbayesian Feel free to edit my notes for clarity and accuracy.