Multiple endemic disease risk modeling using a Bayesian spatiotemporal shared components model

Abstract

Traditionally, endemic diseases such as dengue, diarrhea, and tuberculosis are modeled separately, which leads to a limited understanding of current disease dynamics and an inaccurate evaluation of the parameters of the different models. In this study, we propose a joint spatiotemporal model to predict the risks of multiple endemic diseases and identify hotspots. The model includes spatial shared component random effects and a covariate for healthy behavior. The model was applied to the joint modeling of dengue, diarrhea, and tuberculosis in thirty districts in Bandung, Indonesia over a five-year period. Our findings show that the joint model was effective in understanding the characteristics of the diseases. One potential advantage of using shared component models is that they can identify diseases with spatial or temporal distribution patterns and consider shared risk factors that may be spatially correlated, such as climate. It is recommended to conduct exploratory analyses to determine the correlation between the risks of the diseases being studied and the reference disease before using this type of model.