*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 


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.