Bayesian Joint Spatiotemporal Modelling of Primary and Recurrent Infections of HFMD at County Level in Jiangsu, China, 2009--2023

Over the past decade, multiple outbreaks of hand, foot, and mouth disease (HFMD) have occurred in East Asia, especially in China. It is crucial to understand the distribution pattern and risk factors of HFMD while also studying the corresponding characteristics of recurrent infections. This paper aims to jointly analyze the spatiotemporal distribution and influential factors of primary and recurrent HFMD in Jiangsu province, China, under the Bayesian framework. Using county-level monthly HFMD counts from 2009 to 2023, we proposed four spatiotemporal hierarchical models with latent effects shared in the reinfection sub-model to evaluate the influence of air pollution, meteorological factors, and demographic characteristics on HFMD on primary and recurrent HFMD infections. The integrated nested Laplace approximation (INLA) approach estimates model parameters and quantifies the spatial and temporal random effects. The optimal model with spatial, temporal, and spatiotemporal interaction effect indicates a significant positive influence of NO2, wind speed, relative humidity, and solar radiation, as well as a significant negative effect of PM2.5, O3, temperature above 27 {degrees}C, precipitation and COVID-19, on both infections. Scattered status and critical primary infection significantly positively affect both primary and recurrent incidence. Positive sharing coefficients reveal similar spatiotemporal patterns of primary and recurrent incidence. Non-linear analysis further demonstrates the influence of air pollution and meteorological factors. Our findings deepen the understanding of primary and recurrent HFMD infections and are expected to contribute to developing more effective disease control guidelines.