Module dates/times: Wednesday, July 15; Thursday, July 16, and Friday, July 17. Live sessions will start no earlier than 8 a.m. Pacific and end no later than 2:30 p.m. Pacific, except for Wednesdays. For modules that end on Wednesday, live sessions will end by 11 a.m. Pacific. For modules that start on Wednesday, live sessions will begin no earlier than 11:30 a.m.
Prerequisites: This module assumes knowledge of the material in Module 1: Probability and Statistical Inference, though not necessarily from taking that module. Some prior knowledge of R would be helpful.
Spatial methods are now used in many disciplines and play an important role in epidemiology and public health. This module gives an introduction to spatial methods. In particular, we will present methods for assessment of clustering, cluster detection, spatial regression, small area estimation, and disease mapping. Methods will be described for both point data (in which cases and non-cases (or a sample thereof) have an associated point location) and count data (in which the numbers of cases and non-cases in a set of geographical areas are available).
An introduction to Geographic Information Systems (GIS) will be provided. The important extension to space-time analysis will be described, which is crucial for the analysis of infectious disease data with a spatial component.
Many examples will be presented, with analysis carried out in the R programming environment.
Reference: Waller, L. and Gotway, C. (2004). Applied Spatial Statistics for Public Health Data. New York, John Wiley and Sons.