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Summary. We consider joint spatial modelling of areal multivariate categorical data assuming a multiway contingency table for the variables, modelled by using a log‐linear model, and connected across units by using spatial random effects. With no distinction regarding whether variables are response or explanatory, we do not limit inference to conditional probabilities, as in customary spatial logistic...
Summary. Spatiotemporal disease mapping models have been used extensively to describe the pattern of surveillance data. They are usually formulated in a hierarchical Bayesian framework and posterior marginals are not available in closed form. Hence, the standard method for parameter estimation is Markov chain Monte Carlo algorithms. A new method for approximate Bayesian inference in latent Gaussian...
Summary. Clinical data on the location of residence at the time of diagnosis of new lupus cases in Toronto, Canada, for the 40 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Inference is complicated by numerous irregular changes in the census regions on which population is reported. A model is introduced consisting of a continuous random spatial surface and fixed...
Conditional auto‐regressive models are commonly used to capture spatial cor relation in areal unit data, as part of a hierarchical Bayesian model. The spatial correlation structure that is induced by these models is determined by geographical adjacency, but this is too simplistic for some real data sets, which can visually exhibit subregions of strong correlation as well as locations at which the...
The integrated nested Laplace approximation (INLA) is a convenient way to obtain approximations to the posterior marginals for parameters in Bayesian hierarchical models when the latent effects can be expressed as a Gaussian Markov random field. In addition, its implementation in the R‐INLA package for the R statistical software provides an easy way to fit models using the INLA in practice in a fraction...
As malaria incidence decreases and more countries move towards elimination, maps of malaria risk in low‐prevalence areas are increasingly needed. For low‐burden areas, disaggregation regression models have been developed to estimate risk at high spatial resolution from routine surveillance reports aggregated by administrative unit polygons. However, in areas with both routine surveillance data and...
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