Using Remote Sensing, Ecological Niche Modeling, and Geographic Information Systems for Rift Valley Fever Risk Assessment in the United States
| dc.contributor.author | Tedrow, Christine Atkins | |
| dc.creator | Tedrow, Christine Atkins | |
| dc.date | 2009-12-09 | |
| dc.date.accessioned | 2010-01-27T17:50:07Z | |
| dc.date.available | NO_RESTRICTION | |
| dc.date.available | 2010-01-27T17:50:07Z | |
| dc.date.issued | 2010-01-27T17:50:07Z | |
| dc.description.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. Keywords Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson’s Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS | |
| dc.identifier.uri | https://hdl.handle.net/1920/5679 | |
| dc.language.iso | en_US | |
| dc.subject | Biodefense | |
| dc.subject | Remote sensing | |
| dc.subject | Rift Valley fever | |
| dc.subject | Ecological niche modeling | |
| dc.subject | GIS | |
| dc.subject | Risk assessment | |
| dc.title | Using Remote Sensing, Ecological Niche Modeling, and Geographic Information Systems for Rift Valley Fever Risk Assessment in the United States | |
| dc.type | Dissertation | |
| thesis.degree.discipline | Biodefense | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy in Biodefense |