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.
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.