Bayesian Disease Mapping (3rd Ed.) Hierarchical Modeling in Spatial Epidemiology, Third Edition Chapman & Hall/CRC Interdisciplinary Statistics Series
Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications.
In addition to the new material, the book also covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data.
The target audience for this text is public health specialists, epidemiologists, and biostatisticians who need to work with geo-referenced health data.
Introduction. Bayesian Inference and Modeling. Computational Issues. Residuals and Goodness-of-Fit. Disease Map Reconstruction and Relative Risk Estimation. Disease Cluster Detection. Regression and Ecological Analysis. Putative Hazard Modeling. Multiple Scale Analysis. Multivariate Disease Analysis. Spatial Survival and Longitudinal Analyses. Spatiotemporal Disease Mapping. Disease Map Surveillance. Infectious Disease Modeling. Computational Software Issues. Basic R and Win/OpenBUGS. Selected WinBUGS Code. R Code for Thematic Mapping. Appendices.
Andrew B. Lawson is a professor of biostatistics and eminent scholar in the Division of Biostatistics and Epidemiology in the College of Medicine at the Medical University of South Carolina. He is an ASA fellow and an advisor in disease mapping and risk assessment for the World Health Organization. Dr. Lawson has published over 100 journal papers and eight books and is the founding editor of Spatial and Spatio-temporal Epidemiology. He received a PhD in spatial statistics from the University of St. Andrews. His research interests include the analysis of clustered disease maps, spatial and spatio-temporal disease surveillance, nutritional measurement error, and Bayesian latent variable and SEM modeling.
Date de parution : 03-2021
15.6x23.4 cm
Date de parution : 05-2018
15.6x23.4 cm
Thèmes de Bayesian Disease Mapping :
Mots-clés :
MCMC Algorithm; MCMC Sample; bayesian hierarchical mapping; Gaussian Prior Distribution; spatial biostatistics; Prior Distribution; spatial epidemiology; Temporal Random Effect; putative hazard modeling; Posterior Distribution; disease map surveillance; Car Prior Distribution; Bayesian disease mapping; Convolution Model; regression and ecological analysis; Case Event Data; Bayesian tools and methods; Georgia County Level; geographical analysis of disease; Uncorrelated Random Effect; INLA package in R; Data Set; Bayesian hierarchical regression modeling; Autologistic Model; WinBUGS code; Converged Sampler; conditional autoregressive models; Georgia Oral Cancer; georeferenced health data; Bayesian Residual; surveillance; Car Model; spatio-temporal; Spatial Random Effects; infectious diseases; UH; survival analysis; DIC Measure; INLA; Reversible Jump MCMC; Stan; MCMC Method; Disease Mapping; Heterogeneous Poisson Process; Hyperprior Distributions
