  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.
