Missing 1 hospitalized

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()
}
})
```