Statistical Methods for Disease Clustering, 2010 Statistics for Biology and Health Series

This book is intended to provide a text on statistical methods for detecting clus ters and/or clustering of health events that is of interest to ?nal year undergraduate and graduate level statistics, biostatistics, epidemiology, and geography students but will also be of relevance to public health practitioners, statisticians, biostatisticians, epidemiologists, medical geographers, human geographers, environmental scien tists, and ecologists. Prerequisites are introductory biostatistics and epidemiology courses. With increasing public health concerns about environmental risks, the need for sophisticated methods for analyzing spatial health events is immediate. Further more, the research area of statistical tests for disease clustering now attracts a wide audience due to the perceived need to implement wide ranging monitoring systems to detect possible health related bioterrorism activity. With this background and the development of the geographical information system (GIS), the analysis of disease clustering of health events has seen considerable development over the last decade. Therefore, several excellent books on spatial epidemiology and statistics have re cently been published. However, it seems to me that there is no other book solely focusing on statistical methods for disease clustering. I hope that readers will ?nd this book useful and interesting as an introduction to the subject.

Clustering and Clusters.- Disease Mapping: Visualization of Spatial Clustering.- Tests for Temporal Clustering.- General Tests for Spatial Clustering: Regional Count Data.- General Tests for Spatial Clustering : Case-Control Point Data.- Tests for Space-Time Clustering.- Focused Tests for Spatial Clustering.- Space-Time Scan Statistics.

Historical overview of methods for disease clustering Detailed treatment of selected methods useful for practical investigation of disease clustering Analysis and illustration of methods for a variety of real data sets Includes supplementary material: sn.pub/extras