> *Dissertation Defense Announcement
> To:  The George Mason University Community*
> *Maj. Christine A. Tedrow
> PhD Biodefense Candidate
> College of Science
> *
> *Date:   Thursday November 5, 2009
> Time:   1:00 p.m.
> Place:  George Mason University, Prince William campus
> 	     Discovery Hall Auditorium 
> Dissertation Chair: Dr. Charles L. Bailey, Ph.D., National Center for Biodefense and Infectious Diseases
> Title:  "Using Remote Sensing, Ecological Niche Modeling, and Geographic Information Systems for *
*Rift Valley Fever Risk Assessment in the United States" *
> *A copy of the dissertation is on reserve in the Johnson Center 
> Library, Fairfax campus.  The doctoral project will not be read at the 
> meeting, but should be read in advance. *
> *All members of the George Mason University community are invited to 
> attend.*
>             ABSTRACT
> The primary goal in this study was to explore remote sensing, 
> ecological niche modeling, and Geographic Information Systems (GIS) as 
> aids in predicting candidate Rift Valley fever (RVF) competent vector 
> abundance and distribution in Virginia, and as means of estimating 
> where risk of establishment in mosquitoes and risk of transmission to 
> human populations would be greatest in Virginia.  A second goal in 
> this study was to determine whether the remotely-sensed Normalized 
> Difference Vegetation Index (NDVI) can be used as a proxy variable of 
> local conditions for the development of mosquitoes to predict mosquito 
> species distribution and abundance in Virginia.  As part of this 
> study, a mosquito surveillance database was compiled to archive the 
> historical patterns of mosquito species abundance in Virginia.  In 
> addition, linkages between mosquito density and local environmental 
> and climatic patterns were spatially and temporally examined
> The present study affirms the potential role of remote sensing imagery 
> for species distribution prediction, and it demonstrates that 
> ecological niche modeling is a valuable predictive tool to analyze the 
> distributions of populations.  The MaxEnt ecological niche modeling 
> program was used to model predicted ranges for potential RVF competent 
> vectors in Virginia.  The MaxEnt model was shown to be robust, and the 
> candidate RVF competent vector predicted distribution map is presented.
> The Normalized Difference Vegetation Index (NDVI) was found to be the 
> most useful environmental-climatic variable to predict mosquito 
> species distribution and abundance in Virginia.  However, these 
> results indicate that a more robust prediction is obtained by 
> including other environmental-climatic factors correlated to mosquito 
> densities (e.g., temperature, precipitation, elevation) with NDVI.
> The present study demonstrates that remote sensing and GIS can be used 
> with ecological niche and risk modeling methods to estimate risk of 
> virus establishment in mosquitoes and transmission to humans.  Maps 
> delineating the geographic areas in Virginia with highest risk for RVF 
> establishment in mosquito populations and RVF disease transmission to 
> human populations were generated in a GIS using human, domestic 
> animal, and white-tailed deer population estimates and the MaxEnt 
> potential RVF competent vector species distribution prediction.
> The candidate RVF competent vector predicted distribution and RVF risk 
> maps presented in this study can help vector control agencies and 
> public health officials focus Rift Valley fever surveillance efforts 
> in geographic areas with large co-located populations of potential RVF 
> competent vectors and human, domestic animal, and wildlife hosts.