Spatial Epidemiology - Fall 2009

Course Webpage: http://www.unc.edu/~rls/s940/s940.html

Coordinator: Richard Smith, Department of STOR, UNC (rls@email.unc.edu)

Prerequisites: Solid background in mathematical statistics.

Course Day and Time: Course will be hold at SAMSI in RTP on Tuesdays, 4:30-7:00 p.m. in Room 150.

Schedule: First class Tuesday, August 25; last class Tuesday, December 8; no class September 15 (during opening workshop).

Course Description:
Much of modern epidemiology is concerned with relationships between environmental factors and various types of human health outcome. When data are collected at many spatial locations, we may refer to the problem as one of spatial epidemiology. However in most cases, this includes a temporal component as well. Since modeling spatial dependence is often critical to the method of statistical inference, it is necessary to use methods from spatial or spatio-temporal statistics. Very often health data are aggregated (e.g. into zip code or county totals) so models for data at discrete spatial locations, such as Markov random fields, are more appropriate than geostatistical methods. Another kind of problem is exemplified by the NMMAPS study (http://www.ihapss.jhsph.edu/): an air pollution-mortality relationship is developed initially for many time series at individual cities, but imferences are then drawn by combining data across spatial locations. A third kind of problem is when there is uncertainty about the pollution field itself, for example, when data collected at monitors are interpolated to other locations. Sometimes this interpolation is performed by spatial statistics methods, but there is a growing trend to use air pollution models such as CMAQ (the EPA's Community Multiscale Air Quality model).

Specific topics (tentative): Models for spatially distributed health data. Markov random fields; extensions to spatial-temporal processes. Multi-city time series studies; combining data across multiple studies at different spatial locations. Measurement error problems that involve spatial interpolation; use of air quality models.

August 25, Class 1: Howard Chang (Duke). Multi-city time series analysis. Presentation 1 - Updated 08/31 -- Presentation 2
September 1, Class 2: Howard Chang Presentation 3 -- Presentation 4 -- Presentation 5
September 8, Class 3: Howard Chang Presentation 6 -- Presentation 7
September 15, No class (Opening workshop)
September 22, Class 4: Sudipto Banerjee (University of Minnesota, visiting SAMSI). Markov random fields, hierarchical models and epidemiological applications. Presentation 1
September 29, Class 5: Sudipto Banerjee
October 6, Class 6: Sudipto Banerjee Presentation
October 13, Class 7: Brian Reich (NCSU) - exposure models Presentation
October 20, No class (UNC Fall Break)
October 27, Class 8: Richard Smith (UNC-STOR) - Bayesian methods for incorporating exposure measurement error Presentation -- Crooks et al. paper
November 3, Class 9: Sudipto Banerjee Presentation
November 10, Class 10: Amy Herring (UNC Biostatistics) - Case Study: Characterizing the Neighborhood Environment
November 17, Class 11: Sudipto Banerjee Presentation
November 24, Class 12: Sudipto Banerjee --- GMCAR.txt --- LiBanerjee.pdf --- Banerjee.pdf
December 1, Class 13: Murali Haran (Penn State, visiting SAMSI) - infectious disease modeling --- Presentation
December 8, Class 14: Student presentations, Schedule:

4:30 Yingqi Zhao, "Power Analysis in Association between Source-Specific Swine Markers and Acute Changes in Health Status Measures." Presentation - Write-up
4:50 Jingwen Zhou, "Spatial-temporal model development for NMMAPS data" Presentation
5:10 Rob Erhardt, "Spatial Pattern of the Dengue Vector in Iquitos, Peru" Presentation - Write-up
5:30 Laura Boehm, "Analysis of Cardiac Birth Defects and Air Pollution in Texas" Presentation - Write-up
5:50 break
6:00 David Vock, "Using Estimating Equations for Spatially Correlated Areal Data" Presentation
6:20 Eric Kalendra, "Extending the CAR Model to Account for General Temporal Neighborhood Structures" Presentation - Write-up
6:40 Soyoung Jeon, "Measurement error caused by spatial misalignment in environmental epidemiology" Presentation - Write-up

Registration for this course is being processed through your university.

Course Numbers:
Duke: 294-02
NCSU: MA/ST 810.003
UNC: MATH 891-002/STOR 940-001