We propose a four-year interdisciplinary research program developing statistical methodology for disease ecology, the study of environmental and ecological impacts on disease incidence and spread. Many infectious diseases involve multiple hosts and vectors, each with unique behaviors, and each impacted by climate and landscape. The proposed research draws from the fields of ecology, conservation biology, environmental health, remote sensing, epidemiology, and global health. Geographic space links these disparate fields of inquiry and we use utilize spatial statistics to achieve our three specific aims: 1) Spatio-temporal inference for the local phylogeography of emerging diseases. We propose methods quantifying landscape impacts on the genetic structure of a virus in the ongoing disease outbreak of raccoon rabies in the eastern United States as motivation and application for methodological development, application and evaluation. Associations of interest involve geographic and genetic bottlenecks, and the impact of intervention programs in stalling disease spread. 2) Spatial inference linking disease incidence and environmental/ecological data from imperfectly measured systems. We propose to develop models linking spatial disease surveillance data to environmental landscapes, vectors, and reservoir hosts. We focus on surveillance data subject to spatially varying levels of quality, e.g., spatially varying probabilities of diagnosis and reporting. We use ongoing field data regarding Buruli ulcer in Ghana, West Africa to motivate and illustrate the proposed methodology. 3) Assess spatial design and performance criteria for the developed techniques. For the proposed techniques to have broad impact, it is essential to measure their performance in the context within which they will be applied. We focus primarily on spatial criteria: e.g., where one needs to collect more information and where methods reduce probability of detection or increase rates of false alarms. The results of this program will allow researchers to measure the impact of landscape features on the spread of an emerging disease allowing more accurate predictions of spread, planning of appropriate responses, and improved design of intervention strategies. The public health data gathered during this study will be used to design models, analytic techniques, and software that will assist ministries of health, non- government organizations, and researchers identify areas in which to focus surveillance, determine placement/enhancement of health treatment facilities, increase laboratory capacity, provide educational outreach activities, and organize trainings for healthcare workers, outreach coordinators, and laboratory personnel.SPATIAL STATISTICS FOR DISEASE ECOLOGY PROJECT NARRATIVE The proposed research project will develop analytic methods for mapping and quantifying the spread of infectious diseases in time and space through a complicated landscape. The project focuses on two diseases: a strain of rabies that is typically found in raccoons (but transmittable to humans) in the eastern United States and Buruli ulcer (a bacterial infection resulting in deep skin wounds) in Ghana, West Africa. The primary goal of the research is to develop accurate measurements of the impact of environmental factors (such as rivers and mountain ranges) on the spread of diseases across a diverse landscape in order to design effective, geographically specific public health responses.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dilworth, Caroline H
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Emory University
Biostatistics & Other Math Sci
Schools of Public Health
United States
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Zhu, Li; Waller, Lance A; Ma, Juan (2013) Spatial-temporal disease mapping of illicit drug abuse or dependence in the presence of misaligned ZIP codes. GeoJournal 78:463-474
Gruenewald, Paul J; Ponicki, William R; Remer, Lillian G et al. (2013) Mapping the spread of methamphetamine abuse in California from 1995 to 2008. Am J Public Health 103:1262-70
Bagamian, Karoun H; Towner, Jonathan S; Kuenzi, Amy J et al. (2012) Transmission ecology of Sin Nombre hantavirus in naturally infected North American deermouse populations in outdoor enclosures. PLoS One 7:e47731
Bagamian, Karoun H; Douglass, Richard J; Alvarado, Arlene et al. (2012) Population density and seasonality effects on Sin Nombre virus transmission in North American deermice (Peromyscus maniculatus) in outdoor enclosures. PLoS One 7:e37254
Hickson, Demarc A; Waller, Lance A; Gebreab, Samson Y et al. (2011) Geographic representation of the jackson heart study cohort to the African-American population in Jackson, Mississippi. Am J Epidemiol 173:110-7
Wheeler, David C; Hickson, Demarc A; Waller, Lance A (2010) Assessing Local Model Adequacy in Bayesian Hierarchical Models Using the Partitioned Deviance Information Criterion. Comput Stat Data Anal 54:1657-1671
Fitzpatrick, Matthew C; Preisser, Evan L; Porter, Adam et al. (2010) Ecological boundary detection using Bayesian areal wombling. Ecology 91:3448-55; discussion 3503-14
Berke, Olaf; Waller, Lance (2010) On the effect of diagnostic misclassification bias on the observed spatial pattern in regional count data--a case study using West Nile virus mortality data from Ontario, 2005. Spat Spatiotemporal Epidemiol 1:117-22
Waller, Lance A (2010) Bridging gaps between statistical and mathematical modeling in ecology. Ecology 91:3500-2; discussion 3503-14
Wheeler, David C; Waller, Lance A; Biek, Roman (2010) Spatial analysis of feline immunodeficiency virus infection in cougars. Spat Spatiotemporal Epidemiol 1:151-61

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