Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach

• URL: http://arxiv.org/abs/2104.09327v1
• Date: Wed, 14 Apr 2021 11:58:54 GMT
• Title: Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach
• Authors: Alexandra Hope Lee, Panagiotis Lymperopoulos, Joshua T. Cohen, John B. Wong, and Michael C. Hughes
• Abstract summary: We consider the problem of forecasting the daily number of hospitalized COVID-19 patients at a single hospital site. We develop several candidate hierarchical Bayesian models which directly capture the count nature of data. We demonstrate our approach on public datasets for 8 hospitals in Massachusetts, U.S.A. and 10 hospitals in the United Kingdom.
• Score: 59.318136981032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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