This paper proposes a novel methodology called the mixture of Bayesian predictive syntheses (MBPS) for multiple time series count data for the challenging task of predicting the numbers of COVID-19 inpatients and isolated cases in Japan and Korea at the subnational-level. MBPS combines a set of predictive models and partitions the multiple time series into clusters based on their contribution to predicting the outcome. In this way, MBPS leverages the shared information within each cluster and is suitable for predicting COVID-19 inpatients since the data exhibit similar dynamics over multiple areas. Also, MBPS avoids using a multivariate count model, which is generally cumbersome to develop and implement. Our Japanese and Korean data analyses demonstrate that the proposed MBPS methodology has improved predictive accuracy and uncertainty quantification.

翻译：本文针对日本和韩国次国家级COVID-19住院患者及隔离病例数量的预测难题，提出了一种适用于多时间序列计数数据的新型方法——贝叶斯预测合成混合模型（MBPS）。MBPS通过整合一组预测模型，并根据各时间序列对预测结果的贡献度将其划分为不同聚类。该方法能够有效利用每个聚类内的共享信息，特别适用于COVID-19住院患者预测，因为多地区数据往往呈现相似的动态变化规律。同时，MBPS避免了使用通常难以开发和实施的多变量计数模型。我们在日本和韩国的数据分析表明，所提出的MBPS方法在预测精度和不确定性量化方面均有显著提升。