Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach
This work addresses a practical need for hospital administrators to improve logistics and resource allocation during the COVID-19 pandemic, though it is incremental as it builds on existing Bayesian and time-series methods.
The authors tackled the problem of forecasting daily COVID-19 hospitalizations at individual hospitals to aid administrative planning, developing hierarchical Bayesian models that outperformed baseline methods by sharing statistical strength across sites and using count-based likelihoods.
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.