SPATIAL ANALYSIS OF THE DISTRIBUTION OF EHRLICHIA CHAFFEENSIS, CAUSATIVE AGENT OF HUMAN MONOCYTOTROPIC EHRLICHIOSIS, ACROSS A MULTI-STATE REGION

MICHAEL J. YABSLEY Southeastern Cooperative Wildlife Disease Study, D. B. Warnell School of Forest Resources, and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia

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MICHAEL C. WIMBERLY Southeastern Cooperative Wildlife Disease Study, D. B. Warnell School of Forest Resources, and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia

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DAVID E. STALLKNECHT Southeastern Cooperative Wildlife Disease Study, D. B. Warnell School of Forest Resources, and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia

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SUSAN E. LITTLE Southeastern Cooperative Wildlife Disease Study, D. B. Warnell School of Forest Resources, and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia

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WILLIAM R. DAVIDSON Southeastern Cooperative Wildlife Disease Study, D. B. Warnell School of Forest Resources, and Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia

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Ehrlichia chaffeensis, which causes human monocytotrophic ehrlichiosis (HME), is an important emerging tick-borne pathogen in the southeastern and southcentral United States. The endemnicity probability of E. chaffeensis and, by implication, locations with risk for HME, was predicted by using two modeling methods. This is first large-scale study to use geospatial analyses to estimate the distribution of E. chaffeensis, and it was conducted using data from a prototypic surveillance system that used white-tailed deer as natural sentinels. Analyses included the E. chaffeensis serostatus for 563 counties from 18 states. Both kriging and logistic regression models provided very reliable portrayals of E. chaffeensis occurrence and predicted that E. chaffeensis distribution had good concordance with human case data. The integration of a deer surveillance system with geospatial analyses was useful in developing HME risk maps that will be useful for identifying high-risk areas for public health interventions such as prevention and control efforts.

Author Notes

Reprint requests: Michael J. Yabsley, Wildlife Health Building, Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine; University of Georgia, Athens, GA 30602, Telephone: 706-542-1741, Fax: 706-542-5865, E-mail: myabsley@vet.uga.edu.
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