This module has reached capacity and is now closed. 

Live session timeframe (exact schedule with live sessions will be posted by module instructors prior to the start of the module): Monday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Tuesday: 8 a.m. – 2:30 p.m. Pacific (11 a.m. – 5:30 p.m. Eastern); Wednesday: 8 a.m. – 11 a.m. Pacific (11 a.m. – 2 p.m. Eastern).

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.