Reminder:
> *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.
>
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