|Author||Andrew B. Lawson and Ken Kleinman|
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.