Forecasting COVID-19 counts at a single hospital: a hierarchical Bayesian approach
AH Lee, P Lymperopoulos, JT Cohen, JB Wong… - arXiv preprint arXiv …, 2021 - arxiv.org
AH Lee, P Lymperopoulos, JT Cohen, JB Wong, MC Hughes
arXiv preprint arXiv:2104.09327, 2021•arxiv.orgWe 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 …
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 …
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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