Journal
SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY
Volume 7, Issue -, Pages 39-55Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.sste.2013.07.003
Keywords
Integrated Nested Laplace Approximation; Stochastic Partial Differential Equation approach; Bayesian approach; Area-level data; Point-level data
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Funding
- NERC-MRC Grant [NE/I00789X/1]
- UK Department of Health's NIHR Biomedical Research Centres
- FYRE (Fostering Young REsearchers) project by Fondazione Cariplo
- Universita degli Studi di Bergamo
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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. (C) 2012 Elsevier Ltd. All rights reserved.
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