6533b7d4fe1ef96bd1261e39
RESEARCH PRODUCT
An autoregressive approach to spatio-temporal disease mapping
Paloma Botella-rocamoraAntonio López-quílezMiguel A. Martinez-beneitosubject
Statistics and ProbabilityEpidemiologyComputer sciencecomputer.software_genreBayesian statisticsspatial statisticsBayes' theoremsymbols.namesakeMarkov random fieldsEconometricsDiseaseSpatial analysisParametric statisticsDemographyKronecker productCovariance matrixBayes TheoremField (geography)Bayesian statisticsEpidemiologic StudiesAutoregressive modelSpainsymbolsRegression AnalysisData miningcomputerdescription
Disease mapping has been a very active research field during recent years. Nevertheless, time trends in risks have been ignored in most of these studies, yet they can provide information with a very high epidemiological value. Lately, several spatio-temporal models have been proposed, either based on a parametric description of time trends, on independent risk estimates for every period, or on the definition of the joint covariance matrix for all the periods as a Kronecker product of matrices. The following paper offers an autoregressive approach to spatio-temporal disease mapping by fusing ideas from autoregressive time series in order to link information in time and by spatial modelling to link information in space. Our proposal can be easily implemented in Bayesian simulation software packages, for example WinBUGS. As a result, risk estimates are obtained for every region related to those in their neighbours and to those in the same region in adjacent periods. Copyright (C) 2007 John Wiley & Sons, Ltd.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-11-06 |