For Public Health
Andrew B. Lawson
and Ken Kleinman
Spatial and Syndromic Surveillance 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.
- 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.
- Introduction : spatial and syndromic surveillance for public health
- Overview of temporal surveillance
- Optimal surveillance
- Spatial and spatio-temporal disease analysis
- Generalized linear models and generalized linear mixed models for small-area surveillance
- Spatial surveillance and cumulative sum methods
- Scan statistics for geographical disease surveillance : an overview
- Distance-based methods for spatial and spatio-temporal surveillance
- Multivariate surveillance
- Bayesian network approaches to detection
- Efficient scan statistic computations
- Bayesian data mining for health surveillance
- Advanced modeling for surveillance : clustering of relative risk changes