Spatial and Syndromic Surveillance for Public Health

Thông tin sách: Spatial and Syndromic Surveillance for Public Health (Hardcover, 288 trang) – Wiley, 2005. Ngôn ngữ: Tiếng Anh.

Following the events of 9/11 and in the current world climate, there is increasing concern of the impact of potential bioterrorism attacks. Spatial surveillance systems are used to detect changes in public health data, and alert us to possible outbreaks of disease, either from natural resources or from bioterrorism attacks. Statistical methods play a key role in spatial surveillance, as they are used to identify changes in data, and build models of that data in order to make predictions about future activity.

This book is the first to provide an overview of all the current key methods in spatial surveillance, and present them in an accessible form, suitable for the public health professional. It features an abundance of examples using real data, highlighting the practical application of the methodology. It is edited and authored by leading researchers and practitioners in spatial surveillance methods.

• Provides an overview of the current key methods in spatial surveillance of public health data.
• Includes coverage of both single and multiple disease surveillance.
• Covers all of the key topics, including syndromic surveillance, spatial cluster detection, and Bayesian data mining.

From the Back Cover 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.

About the Author

Andrew Lawson, Department of Epidemiology and Biostatistics, University of South Carolina, USA
Andrew has published many papers in leading journals, and a number of books on spatial statistics, including four for Wiley.

Ken Kleinman, Department of Ambulatory Care and Prevention, Harvard Medical School, Boston, USA
Ken is an epidemiologist who specializes in disease surveillance, and has recently worked on projects modeling the spread of anthrax following a potential terrorist attack.