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Course Description

Statistical methods for disease data that include geographic information. Disease maps and relative risk estimation. Mapping and geographic information systems. Bayesian methods of estimation for conditional autoregressive models.  Disease cluster detection. Regression and ecological analysis.

Course Outline

  • Introduction to Bayesian Statistical Methods
  • Disease Map Reconstruction and Relative Risk Estimation
  • Disease Cluster Detection
  • Regression and Ecological Analysis

Learner Outcomes

  • Analyze disease rates across geographical regions.
  • Determine the effects of predictor variables on disease rates across geographical regions.
  • Communicate results of spatial models applied to health data.

Prerequisites

GEO5600 - R for Spatial Data Analytics or equivalent experience.

Duration

30 Hours | 5 Days or 10 Nights
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