  We consider the problem of forecasting the daily number of hospitalized
COVID-19 patients at a single hospital site, in order to help administrators
with logistics and planning. We develop several candidate hierarchical Bayesian
models which directly capture the count nature of data via a generalized
Poisson likelihood, model time-series dependencies via autoregressive and
Gaussian process latent processes, and share statistical strength across
related sites. We demonstrate our approach on public datasets for 8 hospitals
in Massachusetts, U.S.A. and 10 hospitals in the United Kingdom. Further
prospective evaluation compares our approach favorably to baselines currently
used by stakeholders at 3 related hospitals to forecast 2-week-ahead demand by
rescaling state-level forecasts.
