Generalized Linear Mixed Models With Spatial Random Effects for Spatio-Temporal Data: An Application to Dengue Fever Mapping

The Generalized Linear Mixed Models (GLMMs) with spatial random effects for spatio-temporal data are proposed. A hierarchical Bayesian method is used for parameter estimation. The random effects are assumed to be normally distributed and the spatial random effects are assumed to be proper Conditional Autoregressive (CAR) models. The proposed models are applied to Dengue fever data in Northern Thailand, including climatic covariates, rainfall and temperature. The Dengue fever maps are constructed from the posterior mean of the mortality rates.