Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses
Genya Kobayashi, Shonosuke Sugasawa, Yuki Kawakubo, Dongu Han, Taeryon Choi
The Annals of Applied Statistics · 2024 · Volume 18 · Issue 4
Abstract
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.
Keywords
clustering, count data, dynamic factor model, Finite mixture model, Markov chain Monte Carlo, Pólya-Gamma augmentation, state space model
Enable JavaScript for citation tools and additional paper interactions.