Estimating Hidden Epidemic: A Bayesian Spatiotemporal Compartmental Modeling Approach

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.