Methods for evaluating Lyme disease risks using geographic information systems and geospatial analysis.

Lyme disease is a tick-transmitted borreliosis of humans and domestic animals emerging as one of the most significant threats to public health in north temperate regions of the world. However, despite a myriad of studies into symptomology, causes, and treatment of the disease, few researchers have addressed the spatial aspects of Lyme disease transmission. Using statewide data collected in Rhode Island (United States) as a test case, we demonstrated that exposure to deer ticks and the risk of contracting Lyme disease occurs mostly in the peridomestic environment. A Geographic Information System model was developed indicating a strong association among Lyme disease in humans, the degree of nymphal blacklegged tick, Ixodes scapularis Say, abundance in the environment, and prevalence of Borrelia burgdorferi infection in ticks. In contrast, occurrence of plant communities suitable for sustaining I. scapularis populations (forests) was not predictive of Lyme disease risk. Instead, we observed a highly significant spatial trend for decreasing number of ticks and incident cases of Lyme disease with increasing latitude. Geostatistics were employed for modeling spatial autocorrelation of tick densities. These findings were combined to create a model that predicts Lyme disease transmission risk, thereby demonstrating the utility of incorporating geospatial modeling techniques in studying the epidemiology of Lyme disease.