title: Modelling the presence of disease under spatial misalignment using Bayesian latent Gaussian models.
abstract: Modelling patterns of the spatial incidence of diseases using local environmental factors has been a growing problem in the last few years. Geostatistical models have become popular lately because they allow estimating and predicting the underlying disease risk and relating it with possible risk factors. Our approach to these models is based on the fact that the presence/absence of a disease can be expressed with a hierarchical Bayesian spatial model that incorporates the information provided by the geographical and environmental characteristics of the region of interest. Nevertheless, our main interest here is to tackle the misalignment problem arising when information about possible covariates are partially (or totally) different than those of the observed locations and those in which we want to predict. As a result, we present two different models depending on the fact that there is uncertainty on the covariates or not. In both cases, Bayesian inference on the parameters and prediction of presence/absence in new locations are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation. In particular, the spatial effect is implemented with the stochastic partial differential equation approach. The methodology is evaluated on the presence of the Fasciola hepatica in Galicia, a North-West region of Spain.
authors.affiliation: Operational Research Centre, Miguel Hernández de Elche University, Elche. conesa@uv.es.
authors.forename: Xavier
authors.initials: X
authors.lastname: Barber
authors.forename: David
authors.initials: D
authors.lastname: Conesa
authors.forename: Silvia
authors.initials: S
authors.lastname: Lladosa
authors.forename: Antonio
authors.initials: A
authors.lastname: López-Quílez
mesh_terms: D001499:Bayes Theorem; D004813:Epidemiology; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D062206:Spatial Analysis; D013269:Stochastic Processes