Non-Gaussian spatial modelling of Chagas disease in Argentina

We model the spatial distribution of prevalence of the most important Chagas disease vectors to enable predictive mapping of univariate and multivariate prevalence of the species vectors and the disease risk. We analyse both the binary variable of presence-absence of Chagas disease and the species richness in Argentina, in combination with meteorological and topographical covariates associated to the grid. We use several statistical techniques to produce distribution maps of presence-absence, and species richness including a hierarchical Bayesian framework within the context of multivariate geostatistical modelling. Our results show that, as expected, the inclusion of covariates improves the quality of the fitted models, and that there is spatial interaction between neighboring cells/pixels.