Estimating Hidden Epidemic: A Bayesian Spatiotemporal Compartmental Modeling Approach

Che-Yi Liao (), Peiliang Bai (), Lance A. Waller () and Kamran Paynabar ()
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Che-Yi Liao: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Peiliang Bai: Microsoft, New York, New York 10036
Lance A. Waller: Department of Biostatistics & Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322
Kamran Paynabar: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332

INFORMS Joural on Data Science, 2025, vol. 4, issue 3, 230-247

Abstract: Efforts to mitigate public health crises have been complicated by unreported cases and the ever-changing trends of those monitored health events across geographic regions and socioeconomic cultures. To resolve both challenges, we propose a Bayesian spatiotemporal susceptible-exposed-infected-recovered-removed (BayST-SEIRD) framework that builds the hidden effects of neighboring communities, local features, and the reporting rates into its transmission mechanism. To alleviate the computational burdens embedded in a fully Bayesian algorithm, we propose an alternating approach that learns the compartmental structure and the spatial effects separately. With a simulation study, we show that this algorithm can accurately retrieve our designed system. Then, we apply BayST-SEIRD to model the coronavirus disease 2019 (COVID-19) dynamics in the metropolitan Atlanta area. We observe that most counties’ reporting rates were below 10% of the projected total infected population and that age and educational level are negatively correlated with the exposing rate, suggesting the needs for stronger incentives for COVID-19 testing and quarantine among the younger population. Importantly, BayST-SEIRD facilitates the reconstruction of actual case counts of the monitored subject among neighboring communities, which is critical to designing impactful public health policy interventions.

Keywords: healthcare analytics; public health surveillance; spatiotemporal modeling; compartmental modeling; hidden epidemics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:4:y:2025:i:3:p:230-247

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