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.
Funding Statement
This work was supported by JSPS KAKENHI (#22K01421, #21K01421, #21H00699, #20H00080, #24K00244) and Japan-Korea Basic Scientific Cooperation Program between JSPS and NRF (#120218806).
The research of Taeryon Choi was supported under the framework of international cooperation program managed by the National Research Foundation of Korea (#2021K2A9A2A08000094, #FY2021).
Acknowledgments
The authors would like to thank the anonymous referees, an Associate Editor, and the Editor for their constructive comments that improved the quality of this paper.
Citation
Genya Kobayashi. Shonosuke Sugasawa. Yuki Kawakubo. Dongu Han. Taeryon Choi. "Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses." Ann. Appl. Stat. 18 (4) 3383 - 3404, December 2024. https://doi.org/10.1214/24-AOAS1941
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