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Since the outbreak of COVID-19, governments and academia have made tremendousefforts to predict the course of the pandemic and implement prevention measuresby monitoring various indicators. These indicators are obtained daily orweekly, and typically constitute time series count data over multiplesub-regions of a country, where groups of sub-regions often exhibit similardynamics. This paper proposes a novel methodology called the mixture ofBayesian predictive syntheses (MBPS) to improve the predictive performance forsuch data by combining a set of predictive models and clustering the timeseries based on the contribution of those predictive models. MBPS takes theform of a finite mixture of dynamic factor models and is particularly usefulwhen the multiple time series are discrete, as MBPS does not requiremultivariate count models, which are generally cumbersome to develop andimplement. The clustering through MBPS provides valuable insights into the dataand improves predictive performance by leveraging the share information withineach cluster. Grouping multiple series into clusters also reduces the number ofparameters needed compared to employing a multivariate model. To validate itsefficacy, we apply the model to analyse the data on the numbers of COVID-19inpatients and isolated individuals in Japan and Korea. The results demonstratethat our proposed approach outperforms the agent methods in predictiveaccuracy. Moreover, the insights gained from our model provide valuableinformation for interpreting and diagnosing the analysis, contributing to theformulation of preventive measures for infectious diseases in the future.

Clustering and Predicting Multiple Time Series Count Data via Mixture of  Bayesian Predictive Syntheses: Analysis of COVID-19 Hospitalisation in Japan  and Korea