Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

The book "Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" is a comprehensive resource for applying Bayesian methodology in small area health data studies. It covers topics such as disease mapping, Bayesian hierarchical modeling, spatio-temporal modeling, and special topics like multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBayes, and includes INLA for comparative purposes. It is designed for researchers and students from biostatistics, epidemiology, public health, and environmental science and will likely become a key reference for these fields.

\n Format: Hardback
\n Length: 284 pages
\n Publication date: 28 April 2021
\n Publisher: Taylor & Francis Ltd
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The impact of location on health outcomes has garnered increasing attention in recent years, leading to a growing interest in disease mapping. This field focuses on understanding the complex interplay between context and individual predisposition in studies of disease. The Bayesian paradigm plays a significant role in unraveling these complexities, offering a powerful framework for analyzing small area health data.

"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" is a comprehensive resource for individuals seeking to apply Bayesian methodology in small area health data studies. This book offers a comprehensive review of R graphics relevant to spatial health data, providing an overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data. It also delves into Bayesian Computation and goodness-of-fit, reviewing basic Bayesian disease mapping models. Special topics covered include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling.

The book fills a void in the literature and available software by 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. It emphasizes the use of Markov Chain Monte Carlo (MCMC) via Nimble, BRugs, and CARBayes, while also including INLA for comparative purposes. Additionally, a wide range of packages useful in the analysis of geo-referenced spatial data are employed, and code is provided to facilitate model fitting.

With its comprehensive coverage and practical approach, "Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" is poised to become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental sciences. Its emphasis on MCMC and the inclusion of relevant software make it an invaluable resource for those seeking to advance their understanding of spatial and spatio-temporal health data analysis.

\n Weight: 590g\n
Dimension: 159 x 241 x 25 (mm)\n
ISBN-13: 9780367490126\n \n