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Coggeshall, LT. Anopheles Gambiae in Brazil, 1930 to 1940. Geographical Review. 1944 Apr;34(2):308-310. Connor SJ, Thomson MC, Flasse S, Williams JB. Case Studies from the South: The Use of Low- Cost Remote Sensing and GIS for Identifying and Monitoring the Environmental Factors Associated with Vector-Borne Disease Transmission. In: de Savigny D, Wijeyaratne P, editors. GIS for Health and the Environment. Ottawa, ON, Canada: IDRC (International Development Research Cent); 1995. 172 p. Craig MH, Snow RW, le Sueur D: A climate-based distribution model of malaria transmission in Sub-Saharan Africa. Parasitol Today. 1999;15:105-111. Crans WJ. A classification system for mosquito life cycles: life cycle types for mosquitoes of the northeastern United States. J Vector Ecol. 2004 Jun;29(1):1-10. Craven RB, Eliason DA, Francy P, Campos EG, Jakob WL, Smith GC, Bozzi CJ, Moore CG, Maupin GO, et al. The Use of Low-Cost Remote Sensing and GIS for Identifying and Monitoring in used tires from Asia. J Am Mosq Control Assoc. 1988;4:138-42. Craven RB, Eliason DA, Francy DB, Reiter P, Campos EG, Jakob WL, Smith GC, Bozzi CJ, Moore CG, Maupin GO, et al. Importation of Aedes albopictus and other exotic mosquito species into the United States in used tires from Asia. J Am Mosq Control Assoc. 1988 Jun;4(2):138-42. Cross ER, Perrine R, Sheffield C, Pazzaglia G. Predicting areas endemic for schistosomiasis using weather variables and database. Mil Med. 1984;149: 542–544. Daubney R, Hudson JR, Graham PC. Epizootic hepatitis or Rift Valey fever: an undescribed virus disease of sheep, cattle and man from East Africa. Journal of Pathology and Bacteriology. 1931;34:545-79. Davies FG. Observations on the epidemiology of Rift Valley fever in Kenya. J of Hyg 1975;75: 219-230. Davies FG, Karstad L. Experimental infection of the African buffalo with the virus of Rift Valley fever. Trop Anim Health Prod. 1981 Nov;13(4):185-8. Davies FG, Linthicum KJ, James AD. Rainfall and epizootic Rift Valley Fever. Bull World Health Org. 1985;63:941-943. Davies FG, Kilelu E, Linthicum KJ, Pegram RG. Patterns of Rift Valley fever activity in Zambia. Epidemiol Infect. 1992 Feb;108(1):185-91. Davies FG, Martin V. Recognizing Rift Valley fever. FAO Animal Health Manual No. 17. Rome: Food and Agriculture Organization (FAO) of the United Nations. 2003;17:1-45. 165
Diallo M, Nabeth P, Ba K, Sall AA, Ba Y, Mondo M, Girault L, Abdalahi MO, Mathiot C. Mosquito vectors of the 1998-1999 outbreak of Rift Valley Fever and other arboviruses (Bagaza, Sanar, Wesselsbron and West Nile) in Mauritania and Senegal. Med Vet Entomol. 2005 Jun;19(2):119-26. Diuk-Wasser MA, Brown HE, Andreadis TG, Fish D. Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA. Vector Borne Zoonotic Dis. 2006 Fall;6(3):283-95. EFSA Opinion of the Scientific Panel on Animal Health and Welfare on a request from the Commission related to The Risk of a Rift Valley Fever Incursion and its Persistence within the Community. The EFSA Journal. 2005;238:1-128. Egbert, SL, Martinez-Meyer E, Ortega-Herta MA, and Peterson AT. Use of datasets derived from time-series AVHRR imagery as surrogates for land cover maps in predicting species' distributions. Proceedings IEEE 2002 International Geoscience and Remote Sensing Symposium (IGARSS). 2002;4:2337-2339. Elith J, Graham CH, et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography. 2006; 29:129-151 Environmental Systems Research Institute (ESRI). ArcGIS Desktop. 9.2 ed. Redlands, CA: ESRI; 2007. Environmental Systems Research Institute (ESRI). Working with ArcGIS Spatial Analyst for Geospatial Intelligence (GEOINT). 2008; p. 7-5. Estrada-Pena A. Geostatistics and Remote Sensing as Predictive Tools of Tick Distribution: a Cokriging System to Estimate Ixodes scapularis (Acari- Ixodidae) Habitat Suitability in the United States and Canada from Advanced Very High Resolution Radiometer Satellite Imagery. J Med. Entomol. 1998;35(6): 989-995. Evans A, Gakuya F, Paweska JT, Rostal M, Akoolo L, VAN Vuren PJ, Manyibe T, Macharia JM, Ksiazek TG, Feikin DR, Breiman RF, Kariuki Njenga M. Prevalence of antibodies against Rift Valley fever virus in Kenyan wildlife. Epidemiol Infect. 2007 Nov 8;1-9. FAA Aerospace Forecast Fiscal Years 2008–2025 [Internet]. Federal Aviation Administration (FAA) [homepage on the Internet]. 2008 Aug 8 [cited 2008 Aug 10]. Available from: http://www.faa.gov/data_statistics/aviation/aerospace_forecasts/2008- 2025/media/Forecast%20Highlights.pdf. FAO Emergency Preparedness Planning [Internet]. Food and Agriculture Organization of the United Nations (FAO) [homepage on the Internet]. 2008 [cited 2008 Apr 27]. Available from: http://www.fao.org/ag/AGa/Agah/empres/Info/rvf/RVF198.htm. 166
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Using Remote Sensing, Ecological Ni
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DEDICATION This is dedicated to my
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Lastly, I would like to thank all o
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CHAPTER 4: RESULTS ................
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LIST OF FIGURES Figures Page 2.1 Kn
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AUC - Area Under the Curve LIST OF
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ABSTRACT USING REMOTE SENSING, ECOL
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Keywords Rift Valley fever, risk as
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systematically inventory and monito
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populations), human demographics an
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The geographic distribution of pote
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human and animal health, economic l
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1.8 ORGANIZATION OF DISSERTATION Th
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(e.g., ether, chloroform), detergen
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(Imam et al. 1979, Davies and Marti
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have demonstrated that wild ruminan
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flooding of grassland depressions c
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and created mosquito breeding habit
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may transmit the RVF virus mechanic
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virus during inter-epizootic period
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grown by nearly 9% per year (Tatem
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animals during RVF outbreaks or col
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sufficient and continued contact be
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Environmental factors are directly
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animals could arrive in an uninfect
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GIS technology and RS data has been
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NDVI is calculated as a normalized
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In September 2006, the RVF forecast
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In the U.S. RVF is not present and
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changes in mosquito populations in
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hosts that are widely dispersed thr
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mountain range of the Appalachian M
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The land area of Hampton Roads incl
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52 Table 3.1. Primary data sets and
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Table 3.2. Sources of Virginia mosq
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Figure 3.3. Cattle Density in Virgi
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Figure 3.6. 2006 Deer Abundance Est
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Land-Cover Global land-cover data a
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Figure 3.8. Virginia Land-cover Nor
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Figure 3.9. June 2006 Normalized Di
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Table 3.4. Worldclim interpolated c
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3.2.5 DATA PROCESSING All data sets
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normalize differences among surveil
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for three regions of Virginia - Nor
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Correlation analyses The Pearson‟
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of maximum entropy (i.e., that is m
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When test data are available, MaxEn
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Algebra tool in the ArcGIS Spatial
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CHAPTER 4. RESULTS 4.1 POTENTIAL RV
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1. If a native U.S. vector can tran
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Bioecology of Species Selected for
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ditches, animal waste lagoons, and
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90 Mosquito Species Typical Habitat
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The majority of mosquitoes were col
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Table 4.5. Virginia competent vecto
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4.3 MOSQUITO DENSITY CORRELATIONS W
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102 Table 4.8. Mean and range of ea
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Regional graphical analyses In Figu
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Population Density (mean daily trap
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Competent vector species were plott
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Page 129 and 130: Predictor correlation analyses Whil
Page 131 and 132: temperature of the coldest month (b
Page 133 and 134: data. Mosquito populations tend to
Page 135 and 136: 120 Predictor Variable Northern Vir
Page 137 and 138: 122 was 0.003. At this threshold, t
Page 139 and 140: 124 The Jackknife test of variable
Page 141 and 142: 126 4.5 RISK OF RVF ESTABLISHMENT A
Page 143 and 144: 128 The weighted suitability modeli
Page 145 and 146: CHAPTER 5. DISCUSSION 5.1 ENVIRONME
Page 147 and 148: heavily on rainfall as do the predo
Page 149 and 150: 5.2 ENVIRONMENTAL-CLIMATIC VARIABLE
Page 151 and 152: study; predicted suitable habitat w
Page 153 and 154: state. The high risk in this region
Page 155 and 156: The results of this study indicate
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Page 159 and 160: individually and over smaller time
Page 161 and 162: 146 APPENDIX A: RVF COMPETENT VECTO
Page 163 and 164: 148 Figure A.3. Ae. sollicitans dis
Page 165 and 166: 150 Figure A.5. Ae. triseriatus dis
Page 167 and 168: 152 Figure A.7. Cx. pipiens distrib
Page 169 and 170: 154 Figure A.9. Cx. salinarius dist
Page 171 and 172: Desiccation - drying. Diapause - ph
Page 173 and 174: Larva - immature form of certain or
Page 175 and 176: Vector competence - the vector comp
Page 177 and 178: REFERENCES Ackerman GA, Giroux J. A
Page 179: Brown H, Duik-Wasser M, Andreadis T
Page 183 and 184: Glass GE, Cheek JE, Patz JA, Shield
Page 185 and 186: Komar O, Robbins MB, Klenk K, Blitv
Page 187 and 188: NASA Earth Observatory (EO) Measuri
Page 189 and 190: Phillips SJ, Dudík M, Schapire RE.
Page 191 and 192: Tatem AJ, Hay SI, Rogers DJ. Global
Page 193 and 194: WHO Disease Outbreak News [Internet
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