Bayesian computing with INLA: New features

Martins, Thiago G.; Simpson, Daniel; Lindgren, Finn; Rue, Håvard

doi:10.1016/j.csda.2013.04.014

Cited by 489 publications

(406 citation statements)

References 31 publications

(46 reference statements)

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“…In fact, while most of the commands are similar to those applied in standard R routines (e.g. lm or glm), a wealth of options can be specified within the R-INLA functions, that allow the user to select different model specifications; see Martins et al (2012) for new features.…”

Section: Discussionmentioning

confidence: 99%

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Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

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417

426

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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.

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Section: Discussionmentioning

confidence: 99%

“…More details on this methods can be found in Rue et al (2009);Martins et al (2012); Blangiardo and Cameletti (2013).…”

Section: Integrated Nested Laplace Approximation (Inla)mentioning

confidence: 99%

Spatial and spatio-temporal models with R-INLA

Blangiardo

Cameletti

Baio

et al. 2013

Spatial and Spatio-temporal Epidemiology

Self Cite

417

426

View full text Add to dashboard Cite

show abstract

“…p(θ i | y) and p(ψ k | y). Moreover, as described in Martins et al (2013), INLA can provide approximations to the posterior marginals of linear combinations of the latent field defined as ν = Bθ, where matrix B contains the weights defining the linear combination.…”

Section: Bayesian Inference With Inla and Spdementioning

confidence: 99%

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Blangiardo

Finazzi

Cameletti

2016

Spatial and Spatio-temporal Epidemiology

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Exposure to high levels of air pollutant concentration is known to be associated with respiratory problems which can translate into higher morbidity and mortality rates. The link between air pollution and population health has mainly been assessed considering air quality and hospitalization or mortality data. However this approach limits the analysis to individuals characterized by severe conditions. In this paper we evaluate the link between air pollution and respiratory diseases using general practice drug prescriptions for chronic respiratory diseases, which allow to draw conclusions based on the general population.We propose a two-stage statistical approach: in the first stage we specify a space-time model to estimate the monthly NO 2 concentration integrating several data sources characterized by different spatio-temporal resolution; in the second stage we link the concentration to the β 2 -agonists prescribed monthly by general practices in England and we model the prescription rates through a small area approach.

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“…The inclusion of random spatial and temporal structures in the model allowed us to account for unknown or unobserved confounding factors that influence the dengue transmission patterns, by introducing an extra source of variability into the model in a hierarchical framework. Model parameters were estimated within a Bayesian framework using Integrated Nested Laplace Approximation (INLA, www.r-inla.org) (Martins et al, 2013;Rue et al, 2009). INLA is a promising alternative to Markov…”

Section: Model Formulationmentioning

confidence: 99%

Quantifying the added value of climate information in a spatio-temporal dengue model

Lowe

Cazelles

Rácz

et al. 2015

Stoch Environ Res Risk Assess

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Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from one month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA).This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8% of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79% of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

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Bayesian computing with INLA: New features

Cited by 489 publications

References 31 publications

Spatial and spatio-temporal models with R-INLA

Spatial and spatio-temporal models with R-INLA

Two-stage Bayesian model to evaluate the effect of air pollution on chronic respiratory diseases using drug prescriptions

Quantifying the added value of climate information in a spatio-temporal dengue model

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