2002 Denver Annual Meeting (October 27-30, 2002)

Paper No. 2
Presentation Time: 8:35 AM


GUPTILL, Stephen C. and PRICE, Susan D., U. S. Geol Survey, 521 National Center, Reston, VA 20192, sguptill@usgs.gov

Today, geographic information systems, remote sensing satellites and other technologies are providing scientists with the tools and the data to make clear the geographic relationships between the habitats of disease agents, their vectors and vertebrate hosts, and the occurrence of disease in the human population. A program of joint research is underway with scientists at the U.S. Geological Survey, the Centers for Disease Control and Prevention, and state health agencies, to examine the environmental influences on vector-borne diseases (such as Lyme disease, plague, and viral encephalitis).

In this presentation, we use the La Crosse encephalitis virus-vector-host system to demonstrate the current and potential uses of geographic information science and related technologies for the surveillance, prevention, and control of disease. Cases of La Crosse encephalitis recently have been concentrated within the Appalachian region of the United States, particularly in southern Ohio, West Virginia, Tennessee, and North Carolina.

USGS, CDC, the West Virginia Dept. of Health, and Virginia Tech University scientists are engaged in a comprehensive study of the ecology and epidemiology of La Crosse encephalitis in West Virginia. Components of the study include case findings, serologic surveys, vector and vertebrate ecology, and public education. Precisely located (by GPS) mosquito sampling sites and human case residences provide “ground truth” data for calibration of remotely sensed data.

Our studies to date have shown that the landscapes surrounding case locations of the disease are dominated by a mixture of deciduous forest and pasture land. However, this fourteen-class categorization of land cover appears to be insufficient to discriminate positive and negative ovitrap sites placed throughout the study area. Additional geospatial data sets are being incorporated into the analysis to increase the discrimination power. Also socio-economic data are being analyzed to determine their utility as predictors of disease risk.