Description:
Charles DiMaggio, PhD, Assistant Professor of Epidemiology, Columbia University, discusses the benefits of incorporating Bayesian hierarchical modeling into spatial analysis of public health data. He asserts that 21st century advances in statistics and computing allow the full appreciation and use of methods first described 2 centuries ago by Rev. Bayes. He also provides examples of how this form of spatial analysis can benefit disaster preparedness and response efforts. This course also covers the basic hierarchy of the Bayesian approach and conditional auto-regression. Participants in this course should have a basic understanding of geospatial analysis
Learning Objectives:
- Describe how Bayesian statistics and computing allow for new forms of spatial analyses that can benefit first responders, epidemiologists and social science researchers
- Identify and explain the relevance of conditional auto-regression
- Define and summarize the similarities and differences between frequentist and Bayesian statistical approaches
- Summarize the purpose and importance of unstructured heterogeneity in spatial data
- Describe the utility of Ripley's K function
PHEP Capabilities:
Public Health Surveillance and Epidemiological Investigation
Emergency Public Information and Warning
Information Sharing
Topic:
Epidemiology & Surveillance
Format:
Online Course
Time:
1 hour
Level:
Introductory
University:
Columbia University
PERLC:
Columbia Regional Learning Center