title: Spatial and spatio-temporal models with R-INLA.
abstract: During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constraint. Recently the use of Gaussian random fields has become increasingly popular in epidemiology as very often epidemiological data are characterised by a spatial and/or temporal structure which needs to be taken into account in the inferential process. The Integrated Nested Laplace Approximation (INLA) approach has been developed as a computationally efficient alternative to MCMC and the availability of an R package (R-INLA) allows researchers to easily apply this method. In this paper we review the INLA approach and present some applications on spatial and spatio-temporal data.
authors.affiliation: MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College, London, UK. m.blangiardo@imperial.ac.uk
authors.forename: Marta
authors.initials: M
authors.lastname: Blangiardo
authors.forename: Michela
authors.initials: M
authors.lastname: Cameletti
authors.forename: Gianluca
authors.initials: G
authors.lastname: Baio
authors.forename: Håvard
authors.initials: H
authors.lastname: Rue
mesh_terms: D006801:Humans; D015233:Models, Statistical; D012984:Software; D062211:Spatio-Temporal Analysis