Disease mapping involves the analysis of geo-referenced disease incidence data and has many applications, for example within resource allocation, cluster alarm analysis, and ecological studies. There is a real need amongst public health workers for simpler and more efficient tools for the analysis of geo-referenced disease incidence data. Bayesian and multilevel methods provide the required efficiency, and with the emergence of software packages - such as WinBUGS and MLwiN - are now easy to implement in practice.* Provides an introduction to Bayesian and multilevel modelling in disease mapping.* Adopts a practical approach, with many detailed worked examples.* Includes introductory material on WinBUGS and MLwiN.* Discusses three applications in detail - relative risk estimation, focused clustering, and ecological analysis.* Suitable for public health workers and epidemiologists with a sound statistical knowledge.* Supported by a Website featuring data sets and WinBUGS and MLwiN programs.
Disease Mapping with WinBUGS and MLwiN provides a practical introduction to the use of software for disease mapping for researchers, practitioners and graduate students from statistics, public health and epidemiology who analyse disease incidence data.