Tuesdays 4:30pm-7:00pm at SAMSI, Research Triangle Park, NC, beginning Tuesday, August 25, 2009
No class September 15, 2009 (Opening Workshop)
Last class: Tuesday, December 8, 2009
Richard Smith, Department of STOR, UNC (email@example.com)
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.