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

arXiv: https://arxiv.org/abs/2104.09327 · HTML: https://mlsanity.com/paper/2104.09327/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [Variational Gaussian Process State-Space Models](https://mlsanity.com/paper/1406.4905/) (2014)

## Built on by (most cited first, 1 of 1)

- [Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories](https://mlsanity.com/paper/2105.00773/) (2021)

## Abstract (arXiv)

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.