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.
@article{arxiv.2104.09327,
title = {Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach},
author = {Alexandra Hope Lee and Panagiotis Lymperopoulos and Joshua T. Cohen and John B. Wong and Michael C. Hughes},
journal= {arXiv preprint arXiv:2104.09327},
year = {2021}
}
Comments
In ICLR 2021 Workshop on Machine Learning for Preventing and Combating Pandemics