Bayesian spatial and spatio-temporal modeling of Dengue transmission in Bandung: a comparative study of INLA and MCMC approaches

This study models the spatial and spatio-temporal relative risk of Dengue transmission in Bandung using Bayesian inference. Applying the Besag-York-Mollié (BYM) framework, we evaluated separable and inseparable space-time models. Results show that Integrated Nested Laplace Approximation (INLA) produces estimates comparable to Markov chain Monte Carlo (MCMC) but is significantly faster. Evaluated via Deviance Information Criterion (DIC), the INLA-estimated inseparable spatio-temporal model performed best (DIC: 1795.190), effectively identifying high-risk areas like Arcamanik, Lengkong, and southeastern Bandung. This highlights INLA’s computational efficiency and the necessity of space-time interactions in robust disease mapping.