Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of "Statistical Methods in Spatial Epidemiology" is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as "disease mapping," "ecological analysis," "disease clustering," "bio-terrorism," "space-time analysis," "surveillance" and "infectious disease modelling," Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques.

This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.

Growing public awareness of environmental hazards has increased the demand for investigations into the geographical distribution of disease. Data resulting from studies is not always straightforward to interpret and An Introductory Guide to Disease Mapping aims to explain the basic principles underlying the construction and analysis of disease maps.

• An introduction to new developments in disease mapping
• Comprehensive coverage of an active area of research and development
• Numerous case studies to highlight the application of the techniques discussed

In the current world climate of acute concern surrounding potential bioterrorism attacks, there is a call for increasingly sophisticated surveillance systems that will alert us to possible outbreaks of disease or contamination. "Spatial and Syndromic Surveillance for Public Health" is the first text to provide a survey of the state of the art in public health syndromic surveillance.

The early detection of adverse disease outcomes is now an important capability of online public health surveillance systems. This volume lends particular focus to spatial surveillance, where disease maps are examined in conjunction with other data streams. Diverse statistical and data mining research from the main contributors to this fast growing area of concern have been gathered together; with statistical material ranging from process control and conventional temporal surveillance to advanced generalised linear mixed modelling and Bayesian hierarchical models. Focuses on spatial surveillance Reviews non-spatial surveillance methods Deals with data mining and Bayesian methods; includes new developments in Bayesian syndromic modelling as well as advanced hidden Markov models Discusses clustering and space-time detection Evaluates both hierarchical modelling and testing in the area of cluster detection Reviews optimal and multivariate surveillance

"Spatial and Syndromic Surveillance for Public Health" is accessible to those in academia, public service and commerce alike. Epidemiologists, public health workers, statisticians, health planners or military personnel will all find the in-depth examination of these cutting edge techniques invaluable.

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 a such as WinBUGS and MLwiN a 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 a 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"A 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.