Spatial and Syndromic Surveillance for Public Health

Content:
Chapter 1 Introduction: Spatial and Syndromic Surveillance for Public Health (pages 1–10): Andrew B. Lawson and Ken Kleinman
Chapter 2 Overview of Temporal Surveillance (pages 11–29): Yann Le Strat
Chapter 3 Optimal Surveillance (pages 31–52): Marianne Frisen and Christian Sonesson
Chapter 4 Spatial and Spatio?Temporal Disease Analysis (pages 53–76): Andrew B. Lawson
Chapter 5 Generalized Linear Models and Generalized Linear Mixed Models for Small?Area Surveillance (pages 77–94): Ken Kleinman
Chapter 6 Spatial Surveillance and Cumulative Sum Methods (pages 95–114): Peter A. Rogerson
Chapter 7 Scan Statistics for Geographical Disease Surveillance: An Overview (pages 115–131): Martin Kulldorff
Chapter 8 Distance?Based Methods for Spatial and Spatio?Temporal Surveillance (pages 133–152): Laura Forsberg, Marco Bonetti, Caroline Jeffery, Al Ozonoff and Marcello Pagano
Chapter 9 Multivariate Surveillance (pages 153–166): Christian Sonesson and Marianne Frisen
Chapter 10 Bayesian Network Approaches to Detection (pages 167–187): Weng?Keen Wong and Andrew W. Moore
Chapter 11 Efficient Scan Statistic Computations (pages 189–202): Daniel B. Neill and Andrew W. Moore
Chapter 12 Bayesian Data Mining for Health Surveillance (pages 203–221): David Madigan
Chapter 13 Advanced Modeling for Surveillance: Clustering of Relative Risk Changes (pages 223–243): Andrew B. Lawson