Environmetrics · 2010 · 207 citations · 26 references
Public HealthBiostatisticsVeterinary EpidemiologyBayesian BiostatisticsEpidemiologySpatial StatisticsBayesian Disease MappingBayesian StatisticsDisease MappingInferential StatisticsMarkov Chain Monte CarloSpace-time EpidemiologyBayesian Disease ModelingBayesian Latent Gaussian ModelsDeviance Information CriterionLatent Gaussian ModelsModel ChoiceSpace-time Disease MappingSpatial-temporal Disease ModelingSpatiotemporal Disease Mapping
Spatio‑temporal disease mapping models are popular tools for describing disease count patterns, typically formulated in hierarchical Bayesian latent Gaussian frameworks, but traditionally rely on computationally expensive MCMC methods that can introduce large Monte Carlo error; INLA has recently been proposed as an alternative. This paper aims to describe how various parametric and nonparametric spatio‑temporal models and their extensions can be fitted to space–time count data using INLA. The authors emphasize selecting appropriate linear constraints for identifiability and apply these models to Salmonellosis cattle counts from the Swiss Federal Veterinary Office (1991–2008). INLA provides accurate parameter estimates quickly and supplies deviance information criterion for Bayesian model choice. © 2010 John Wiley & Sons, Ltd.
Abstract Spatio‐temporal disease mapping models are a popular tool to describe the pattern of disease counts. They are usually formulated in a hierarchical Bayesian framework with latent Gaussian model. So far, computationally expensive Markov chain Monte Carlo algorithms have been used for parameter estimation which might induce a large Monte Carlo error. An alternative method using integrated nested Laplace approximations (INLA) has recently been proposed. A major advantage of INLA is that it returns accurate parameter estimates in short computational time. Additionally, the deviance information criterion is provided for Bayesian model choice. This paper describes how several parametric and nonparametric models and extensions thereof can be fitted to space–time count data using INLA. Particular emphasis is given to the appropriate choice of linear constraints to ensure identifiability of the parameter estimates. The models are applied to counts of Salmonellosis in cattle reported to the Swiss Federal Veterinary Office 1991–2008. Copyright © 2010 John Wiley & Sons, Ltd.
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