Analysis of non-separable spatiotemporal data with application to enterovirus study in Taiwan

We propose an estimation procedure to incorporate non-separable spatiotemporal correlation into a generalized linear mixed model. The motivation of this paper is from a study of enterovirus infection with spatial-temporal correlation. The proposed method underlying a working estimating equation comes from a generalization of weighted least squares approaches. With an iterative two-stage estimation procedure, we may address the non-identifiability problem caused by latent random effects. Under certain regularity conditions, we show that the proposed estimate has consistency and asymptotic normality for spatiotemporal data. We also conduct a model-based simulation and apply the method to the enterovirus data in Taiwan.