Spatial and spatio-temporal models with R-INLA

Marta Blangiardo; Michela Cameletti; Gianluca Baio; Håvard Rue

doi:10.1016/j.sste.2012.12.001

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 journal › Review article › peer-review

267 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 language	English (US)
Pages (from-to)	33-49
Number of pages	17
Journal	Spatial and Spatio-temporal Epidemiology
Volume	4
Issue number	1
DOIs	https://doi.org/10.1016/j.sste.2012.12.001
State	Published - Mar 2013
Externally published	Yes

Bibliographical note

Funding Information:
The authors wish to thank Dr. Finn Lindgren for his help with the development of the R code implemented for the examples in Section 4 , and Dr. Léa Fortunato for her comments on Section 3.2–3.3 . Dr. Marta Blangiardo received partial support from the NERC-MRC Grant NE/I00789X/1 ; Dr. Gianluca Baio received partial support from the UK Department of Health’s NIHR Biomedical Research Centres funding scheme ; Dr. Michela Cameletti received partial support from the FYRE 2011 (Fostering Young REsearchers) project founded by Fondazione Cariplo and Universitá degli Studi di Bergamo .

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.sste.2012.12.001

Cite this

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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.",

keywords = "Area-level data, Bayesian approach, Integrated Nested Laplace Approximation, Point-level data, Stochastic Partial Differential Equation approach",

author = "Marta Blangiardo and Michela Cameletti and Gianluca Baio and H{\aa}vard Rue",

note = "Funding Information: The authors wish to thank Dr. Finn Lindgren for his help with the development of the R code implemented for the examples in Section 4 , and Dr. L{\'e}a Fortunato for her comments on Section 3.2–3.3 . Dr. Marta Blangiardo received partial support from the NERC-MRC Grant NE/I00789X/1 ; Dr. Gianluca Baio received partial support from the UK Department of Health{\textquoteright}s NIHR Biomedical Research Centres funding scheme ; Dr. Michela Cameletti received partial support from the FYRE 2011 (Fostering Young REsearchers) project founded by Fondazione Cariplo and Universit{\'a} degli Studi di Bergamo . ",

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doi = "10.1016/j.sste.2012.12.001",

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Spatial and spatio-temporal models with R-INLA

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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