Research
Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama
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* Corresponding author: Benjamin G Jacob bjacob@uab.edu
1 School of Medicine, Department of Infectious Diseases, University of Alabama at Birmingham, 845 19th Street South, Birmingham Alabama, USA, 35294
2 Department of Entomology and Plant Pathology, Auburn University, 301 Funchess Hall, Auburn, Alabama, USA 36849
3 NASA -NSSTC, Global Hydrology and Climate Center, 320 Sparkman Drive, Huntsville, Alabama, USA 35805
4 Department of Anthropology, University of Alabama at Birmingham, 1401 University BLVD, Heritage Hall Room 360, Birmingham, Alabama, USA
5 Department of Biological Sciences, Auburn University, Room 101, Rouse Life Science Building, Auburn, Alabama USA, 36849
6 Department of Entomology and Plant Pathology, Auburn University, 301 Funchess Hall, Auburn, Alabama, USA 36849
7 Global Infectious Disease Research Program, Department of Public Health, College of Public Health, University of South Florida, 3720 Spectrum Blvd, Suite 304, Tampa, Florida, USA 33612
International Journal of Health Geographics 2010, 9:12 doi:10.1186/1476-072X-9-12
Published: 24 February 2010Abstract
Background
A site near Tuskegee, Alabama was examined for vector-host activities of eastern equine encephalomyelitis virus (EEEV). Land cover maps of the study site were created in ArcInfo 9.2® from QuickBird data encompassing visible and near-infrared (NIR) band information (0.45 to 0.72 μm) acquired July 15, 2008. Georeferenced mosquito and bird sampling sites, and their associated land cover attributes from the study site, were overlaid onto the satellite data. SAS 9.1.4® was used to explore univariate statistics and to generate regression models using the field and remote-sampled mosquito and bird data. Regression models indicated that Culex erracticus and Northern Cardinals were the most abundant mosquito and bird species, respectively. Spatial linear prediction models were then generated in Geostatistical Analyst Extension of ArcGIS 9.2®. Additionally, a model of the study site was generated, based on a Digital Elevation Model (DEM), using ArcScene extension of ArcGIS 9.2®.
Results
For total mosquito count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.041 km, nugget of 6.325 km, lag size of 7.076 km, and range of 31.43 km, using 12 lags. For total adult Cx. erracticus count, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 5.764 km, nugget of 6.114 km, lag size of 7.472 km, and range of 32.62 km, using 12 lags. For the total bird count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 4.998 km, nugget of 5.413 km, lag size of 7.549 km and range of 35.27 km, using 12 lags. For the Northern Cardinal count data, a first-order trend ordinary kriging process was fitted to the semivariogram at a partial sill of 6.387 km, nugget of 5.935 km, lag size of 8.549 km and a range of 41.38 km, using 12 lags. Results of the DEM analyses indicated a statistically significant inverse linear relationship between total sampled mosquito data and elevation (R2 = -.4262; p < .0001), with a standard deviation (SD) of 10.46, and total sampled bird data and elevation (R2 = -.5111; p < .0001), with a SD of 22.97. DEM statistics also indicated a significant inverse linear relationship between total sampled Cx. erracticus data and elevation (R2 = -.4711; p < .0001), with a SD of 11.16, and the total sampled Northern Cardinal data and elevation (R2 = -.5831; p < .0001), SD of 11.42.
Conclusion
These data demonstrate that GIS/remote sensing models and spatial statistics can capture space-varying functional relationships between field-sampled mosquito and bird parameters for determining risk for EEEV transmission.