The UGA Center for Geospatial Research (CGR) has worked cooperatively with the UGA Southeastern Cooperative Wildlife Disease Study (SCWDS) on a number of projects involving the use of remote sensing, geographic information systems (GIS) and Global Positioning System (GPS) technologies for applications in wildlife infectious disease studies. All projects involve the visualization of disease occurrences or landscape analysis to assess environmental factors influencing disease outbreaks.
Specific examples include:
- Mapping distributions of exotic ticks known to be the vectors of diseases to animals and humans.
- Web-based update of feral swine distributions related to domestic swine populations.
- Spatial patterns of wildlife associated with dairy farms for Johne’s Disease in Georgia and Wisconsin.
- Deriving spatial data sets for logistic regression linking West Nile virus live bird surveillance in Georgia to environmebtal and climatic variables.
- Spatio-temporal analysis and modeling of a 20-year database of nation-wide county surveys on reports on hemorrhagic disease in white-tailed deer (Odocoileus virginianus).
Feral Hog Distribution
The feral hog distribution in the United States has increased significantly in the past 25 years and there are concerns that diseases in wild hogs such as PRV and Brucella suis will be transferred to domestic swine.
The CGR along with the Southeastern Cooperative Wildlife Disease Study (SCWDS) have created an interactive map for reporting and updating the national feral swine distribution. The map is updated on a monthly basis using data collected from state wildlife agencies and USDA-APHIS-Wildlife Services.
Hemorrhagic Disease (HD)
Hemorrhagic disease (HD) represents the most important viral disease affecting white-tailed deer throughout their range in the U.S. HD is caused by viruses in the Epizootic Hemorrhagic Disease and Bluetongue serogroups of the genus Orbivirus. SCWDS has conducted HD surveillance in white-tailed deer since 1980. Reported by county, these data represent one of the most comprehensive data bases for wildlife morbidity and mortality anywhere.
Long 8- to 10-year cycles of outbreaks probably related to combined effects of herd immunity, and natural or weather-induced fluctuations in vector populations. Although it is reasonable to assume weather patterns affect the distribution and abundance of Culicoides (no-see-ums) vector populations, predictive models relating climate and its influence on HD are lacking (Gaydos, et al. 2002).
UGA geography doctoral student, Bo Xu is constructing a logistic regression model to explain the geographic distribution of HD in white tailed deer using climatic data and remotely-sensed data (1980 to 2003) in six Southeast US states. The dependent variable is county-based binomial occurrence of HD and the independent climate variables are mean, median, maximum, minimum, and range of annual temperature, precipitation, relative humidity, and wind speed. The independent remotely-sensed variables are mean, median, maximum, minimum, and range of normalized difference vegetation index, vapor pressure deficit, surface temperature, and middle infrared radiance calculated from specific bands of Advanced High Resolution Radiometric (AVHRR) satellite data. She expects that the combination of climate and remotely-sensed data will explain HD distribution and can be used to predict future occurrences.