Spatial Statistics in Epidemiology and Public Health
Meeting Times:
- Monday, July 15, 8:30 AM – 5:00 PM
- Tuesday July 16, 8:30 AM – 5:00 PM
- Wednesday July 17, 8:30 AM – 12:00 PM
Classroom: TBA
Module Summary:
Spatial methods are now used in many disciplines and play an important role in epidemiology and public health. This module introduces methods crucial for analysis of infectious disease data with a spatial component.
Prerequisites:
This module assumes knowledge of probability and inference covered in an introductory statistical course. Familiarity with regression modeling and R programming is recommended. The course will provide R code to perform data wrangling, exploratory analyses, model fitting, and visualization.
Module Content:
- Introduction to spatial data
- Geographical information systems and mapping
- Measuring spatial correlation and detecting clusters for areal data
- Spatial regression and small area estimation
- Disease mapping with conditional autoregressive models
- Spatially-varying coefficient models
- Spatial Gaussian process models and kriging
- Modeling spatial point patterns
- Methods for multivariate spatial data
- Spatial infectious disease (SIR) and ecology
- Extensions to spatial-temporal models
Instructors
Lance Waller, PhD
Professor, Department of Biostatistics & Bioinformatics, Emory University
Dr. Waller’s research involves the development and application of statistical methods for spatially referenced data including applications in environmental justice, neurology, epidemiology, disease surveillance, conservation biology, and disease ecology. He has published in a variety of biostatistical, statistical, environmental health, and ecology journals and is co-author with Carol Gotway of the text Applied Spatial Statistics for Public Health Data(2004, Wiley). Dr. Waller currently serves as co-Chair of the Committee on Applied and Theoretical Statistics for the National Academies of Science, Engineering, and Medicine.
Howard Chang, PhD
Professor, Department of Biostatistics & Bioinformatics, Emory University
Dr. Chang’s primary research interest is in the development and application of statistical methods for analyzing complex spatial-temporal exposure and health data. His current projects focus on two broad areas of population health: (1) exposure assessment for air quality and extreme weather events, especially under a changing climate, and (2) health effect estimation and impact assessment of climate-sensitive risk factors, such as air pollution, dust storm, respiratory infection activity, and extreme heat/cold.
Required Software:
R programming with the following packages for spatial analysis (sf, maps, spdep, spatialreg, RINLA, CARBayes, geoR, tidyverse, tidycensus).
Recommended Reading:
- Waller, L. and Gotway, C. (2004). Applied Spatial Statistics for Public Health Data. New York, John Wiley and Sons.
- Geocomputation with R
- Spatial Data Science
- Spatial-Temporal Statistics with R
- Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny
- Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
- Analyzing US Census Data: Methods, Maps, and Models
