Training Courses - Introduction to Disease Clustering and Spatial Epidemiology using ClusterSeer

If you are interested in attending this course please contact us

Focus:

Public Health, Epidemiology, Veterinary Science, Agriculture, Forestry and related disciplines.

Upon course completion, you will have

  • The software you need to undertake complete spatial investigations of disease clusters and incidence. All training participants will receive a student license of TerraSeer's ClusterSeer software to use during the class and take with you upon course conclusion.
  • A workbook to review and recreate the course exercises at your own pace.
  • A practical understanding about how to systematically carry out disease cluster investigations, using the latest in spatial analytical tools.

This two day course offers an overview of techniques for the analysis of disease patterns and health outcomes in space, time and space-time using ClusterSeer software. The course is aimed at public health professionals and other researchers and analysts interested in extending the use of a GIS beyond mapping towards the identification and visualization of clusters of events. Particularly relevant applications are the study of data on crime and public health statistics. Only basic knowledge of GIS and probability is assumed.

The course reviews methods to identify temporal, spatial and space-time clusters of health events for both point- (e.g. residential address) and area-based data (e.g. disease rates and frequencies).

Techniques for local, global and focused cluster analysis will be presented, including population-adjusted Moran's I, Lawson and Waller's score test, Cuzick and Edward's test, Jacquez's test for space-time interaction, Besag and Newell's method, Kulldorff's scan test (both spatial and space-time versions), the Knox and Mantel tests, and several others.

The emphasis is on the effective and appropriate use of the techniques to accurately identify whether there is a statistically significant excess of events. This is accomplished by stressing hands-on analysis of sample data sets.