Bayesian Hierarchical Models for Spatial Connectivity
A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping
Modelling infectious disease data across a geographical region is an important consideration when modelling spatial connectivity.However, conventional approaches require the spatial structure to be fully defined prior to model fitting.By applying penalised smoothing splines to coordinates, we create 2-dimensional smooth surfaces describing the spatial structure of the data whilst making minimal assumptions about the structure.These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects.Through simulation studies we show that the splines can be applied to any continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data.Using bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease.
