Using R for Bayesian Spatial and Spatio Temporal Health Modeling

<p>Progressively more and more attention has been paid to how location affects health outcomes. The area of <i>disease mapping</i> focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. <b><i>Using R for Bayesian Spatial and Spatio-Temporal Health Modeling</i></b> provides a major resource for those interested in applying Bayesian methodology in small area health data studies.</p><p><b>Features:</b></p><ul><li>Review of R graphics relevant to spatial health data</li></ul><ul><li>Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data</li></ul><ul><li>Bayesian Computation and goodness-of-fit</li></ul><ul><li>Review of basic Bayesian disease mapping models</li></ul><ul><li>Spatio-temporal modeling with MCMC and INLA</li></ul><ul><li>Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling</li></ul><ul><li>Software for fitting models based on BRugs, Nimble, CARBayes and INLA</li></ul><ul><li>Provides code relevant to fitting all examples throughout the book at a supplementary <a>website</a></li></ul><p>The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.</p>