Bayesian Hierarchical Lognormal Modeling of Dengue Incidence with Area-Specific Temporal Effects

Abstract

This study presents the development and validation of a Bayesian hierarchical model to estimate the incidence rate of dengue fever (DF) in West Java, Indonesia. Bayesian hierarchical models offer powerful tools for handling uncertainty and regional heterogeneity, yet their implementation remains challenging—especially in complex datasets with multilevel structures. The proposed model incorporates both random intercepts (for regencies/cities) and random slopes (for year), with various prior distribution scenarios tested to ensure robustness. Among the tested predictors, population density was found to significantly influence DF incidence. Model performance evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) yielded values of 31.26 and 48.77, respectively, indicating good predictive accuracy. This research highlights the effectiveness of hierarchical Bayesian modeling for epidemiological analysis and contributes to more targeted public health strategies in dengue-endemic regions