Spatial and spatio-temporal models with R-INLA

Marta Blangiardo*, Michela Cameletti, Gianluca Baio, Håvard Rue

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

165 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)33-49
Number of pages17
JournalSpatial and Spatio-temporal Epidemiology
Volume4
Issue number1
DOIs
StatePublished - Mar 2013

Keywords

  • Area-level data
  • Bayesian approach
  • Integrated Nested Laplace Approximation
  • Point-level data
  • Stochastic Partial Differential Equation approach

ASJC Scopus subject areas

  • Epidemiology
  • Geography, Planning and Development
  • Infectious Diseases
  • Health, Toxicology and Mutagenesis

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