Spatial autoregressive modelling of epidemiological data: geometric mean model proposal

Abstract

We propose the geometric mean spatial conditional model for fitting spatial public health data, assuming that the disease incidence in one region depends on that of neighbouring regions, and incorporating an autoregressive spatial term based on their geometric mean. We explore alternative spatial weights matrices, including those based on contiguity, distance, covariate differences and individuals' mobility. A simulation study assesses the model's performance with mobility-based spatial correlation. We illustrate our proposals by analysing the COVID-19 spread in Flanders, Belgium, and comparing the proposed model with other commonly used spatial models. Our approach demonstrates advantages in interpretability, computational efficiency, and fexibility over the commonly used and previously existing methods.