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.
翻译:我们考虑了在单一医院站点每天预测住院COVID-19病人人数的问题,以便帮助行政人员进行后勤和规划。我们开发了几个候选的巴伊西亚等级模型,通过普瓦森普遍的可能性、通过自动递减和高萨进程潜在过程的模型时间序列依赖性、通过自动递减和高萨进程的潜在过程来直接记录数据的计算性质,并在各个相关站点共享统计力量。我们展示了我们在马萨诸塞州8家医院、美国医院和联合王国10家医院的公共数据集方面的做法。进一步的未来评估将我们的方法与目前3家相关医院的利益攸关方使用的基线相比较,通过调整州一级的预测来预测两周前的需求。