Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-xm8r8 Total loading time: 0 Render date: 2024-07-07T23:32:16.754Z Has data issue: false hasContentIssue false

9 - Remote sensing, GIS and spatial statistics: powerful tools for landscape epidemiology

Published online by Cambridge University Press:  28 July 2009

By
Louisa R. Beck ,
Uriel Kitron  and
Matthew R. Bobo
Edited by
P. Martens  and
A. J. McMichael
Louisa R. Beck
Affiliation:
Ecosystem Science and Technology, Branch, NASA Ames Research Center, Moffet Field, USA
Uriel Kitron
Affiliation:
College of Veterinary Medicine, University of Illinois, Urbana, USA
Matthew R. Bobo
Affiliation:
Ecosystem Science and Technology Branch, NASA Ames Research Center, Moffet Field, USA
P. Martens
Affiliation:
Universiteit Maastricht, Netherlands
A. J. McMichael
Affiliation:
Australian National University, Canberra
Get access

Summary

Acronyms

  1. AATSR Advanced Along Track Scanning Radiometer

  2. ADEOS Advanced Earth Observation Satellite

  3. ALOS Advanced Land Observing Satellite

  4. ARIES Australian Resource Information & Environment Satellite

  5. ASTER Advanced Spaceborne Thermal Emission & Reflection Radiometer

  6. AVHRR Advanced Very High Resolution Radiometer

  7. AVIRIS dvanced Visible and Infrared Imaging Spectrometer

  8. AVNIR Advanced Visible & Near Infrared Radiometer

  9. CBERS China Brazil Earth Resources Satellite

  10. ENVISAT Environmental Satellite

  11. EO-1 Earth Orbiter-1

  12. ERS ESA (European Space Agency) Remote Sensing

  13. ETM+ Enhanced Thematic Mapper (Landsat)

  14. GLI Global Imager

  15. IRS Indian Remote Sensing Satellite

  16. LISS Linear Imaging Self Scanning System

  17. MARA Mapping Malarial Risk in Africa

  18. MODIS Moderate Resolution Imaging Spectro Radiometer

  19. MSS Multispectral Scanner

  20. NOAA National Oceanographic & Atmospheric Administration

  21. PAN Panchromatic

  22. RS Remote Sensing

  23. SAR Synthetic Aperture Radar

  24. SPOT Système Pour l'Observation de la Terre

  25. TM Thematic Mapper (Landsat)

  26. WiFS Wide Field Scanner

  27. XS Multispectral (SPOT)

Introduction

There has been much speculation about the potential impacts of climate change on the map of human health, particularly on the patterns of vector-borne diseases (e.g. Longstreth & Wiseman, 1989; WHO, 1990; Dobson & Carper, 1992; Shope, 1992; Kovats, 2000; Chapters 7 & 8). The impact of climate change on the transmission patterns of these diseases can be both direct (e.g. effect of changes in precipitation on populations of arthropod vectors) and indirect (e.g. human population dynamics and their effects on exposure risk, changes in vegetation, hydrology and other landscape features).

Type
Chapter
Information
Environmental Change, Climate and Health
Issues and Research Methods
 , pp. 226 - 252
Publisher: Cambridge University Press
Print publication year: 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahearn, S. C. & Rooy, C. (1996). Monitoring the effects of dracunculiasis remediation on agricultural productivity using satellite data. International Journal of Remote Sensing, 17, 917–29CrossRefGoogle Scholar
Anderson, A. M. & May, R. M. (1991). Population Biology of Infectious Diseases of Humans. Oxford: Oxford University Press
Beck, L. R., Rodríguez, M. H., Dister, S. W., Rodríguez, A. D., Rejmánková, E., Ulloa, A., Meza, R. A., Roberts, D. R., Paris, J. F., Spanner, M. A., Washino, R. K., Hacker, C. & Legters, L. J. (1994). Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. American Journal of Tropical Medicine and Hygiene, 51, 271–80CrossRefGoogle ScholarPubMed
Beck, L. R., Wood, B. L. & Dister, S. W. (1995). Remote sensing and GIS: new tools for mapping human health. Geo. Info. Systems, 5, 3237Google Scholar
Beck, L. R., Rodríguez, M. H., Dister, S. W., Rodríguez, A. D., Washino, R. K., Roberts, D. R. & Spanner, M. A. (1997). An assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. American Journal of Tropical Medicine and Hygiene, 56, 99–106CrossRefGoogle ScholarPubMed
Beck, L. R., Lobitz, B. M. & Wood, B. L. (1999). A stratified approach to mapping malaria risk in Mali using multi-scaled remotely sensed data. Center for Health Applications of Aerospace Related Technologies, NASA Ames Research Center, Moffett Field, CA. Unpublished
Beck, L. R., Lobitz, B. M. &Wood, B. L. (2000). Remote sensing and human health: new sensors and new opportunities. Emerging Infectious Diseases, 6, 217–27CrossRefGoogle ScholarPubMed
Boone, J. D., McGwire, K. C., Otteson, E. W., DeBaca, R. S., Kuhn, E. A., Villard, P., Brussard, P. F. & St. Jeor, S. C. (2000). Remote sensing and geographic information systems: charting Sin Nombre virus infections in deer mice. Emerging Infectious Diseases, 6, 248–57CrossRefGoogle ScholarPubMed
Carey, A. B., Giles, R. H. & McLean, R. G. (1978). The landscape epidemiology of rabies in Virginia. American Journal of Tropical Medicine and Hygiene, 27, 573–80CrossRefGoogle ScholarPubMed
Clarke, K. C., Osleeb, J. R., Sherry, J. M., Meert, J. P. & Larsson, R. W. (1990). The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm) eradication effort. Preventive Veterinary Medicine, 11, 229–35CrossRefGoogle Scholar
Clarke, K. C.McLafferty, S. L. & Tempalski, B. J. (1996). On epidemiology and geographic information systems: a review and discussion of future directions. Emerging Infectious Diseases, 2, 85–92CrossRefGoogle ScholarPubMed
Connor, S. J. (1999). Malaria in Africa: the view from space. Biologist, 46, 22–5Google Scholar
Cross, E. R., Newcomb, W. W. & Tucker, C. J. (1996). Use of weather data and remote sensing to predict the geographic and seasonal distribution of Phlebotomus paptasi in Southwest Asia. American Journal of Tropical Medicine and Hygiene, 54, 530–6CrossRefGoogle Scholar
Cullinan, V. I. & Thomas, J. M. (1992). A comparison of quantitative methods for examining landscape pattern and scale. Landscape Ecology, 7, 211–27CrossRefGoogle Scholar
Curran, P. J., Atkinson, P. M., Foody, G. M. &Milton, E. J. (2000). Linking remote sensing, land cover and disease. Advances in Parasitology, 47, 37–72CrossRefGoogle ScholarPubMed
Daniel, M. & Kolár, J. (1990). Using satellite data to forecast the occurrence of the common tick Ixodes ricinus (L.). Journal of Hygiene, Epidemiology, Microbiology and Immunology, 34, 243–52Google Scholar
Daniel, M., Kolár, J., Zeman, P., Pavelka, K. & Sádlo, J. (1998). Predictive map of Ixodes ricinus high-incidence habitats and a tick-borne encephalitis risk assessment using satellite data. Experimental and Applied Acarology, 22, 417–33CrossRefGoogle Scholar
Dennis, T. D., Nekomoto, T. S., Victor, J. C., Paul, W. S. & Piesman, J. (1998). Reported distribution of Ixodes scapularis and Ixodes pacificus in the United States. Journal of Medical Entomology, 35, 629–38CrossRefGoogle ScholarPubMed
Dister, S. W., Beck, L. R. & Wood, B. L. (1993). The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In Proceedings of the GIS '93 Seventh Annual Symposium: Geographic Information Systems in Forestry, Environmental and Natural Resource Management, 15–18 February 1993, Vancouver, B.C., Canada
Dister, S. W., Fish, D., Bros, S., Frank, D. H. & Wood, B. L. (1997). Landscape characterization of domestic risk for Lyme disease using satellite imagery. American Journal of Tropical Medicine and Hygiene, 57, 687–92CrossRefGoogle Scholar
Dobson, A. & Carper, R. (1992). Global warming and potential changes in host-parasite and disease-vector relationships. In Global Warming and Biodiversity, ed. R. L. Peters & T. E. Lovejoy. New Haven, CT: Yale University Press
Dye, C. & Reiter, P. (2000). Temperatures without fevers? Science, 289, 1697–8
Fish, D. & Howard, C. A. (1999). Methods used for creating a national Lyme disease risk map. Morbidity and Mortality Weekly Report, 48, 21–4Google Scholar
Ford, J. (1971). The Role of Trypanosomiasis in African Ecology. Oxford: Clarendon
Forman, R. T. & Godron, M. (1986). Landscape Ecology. New York: Wiley
Freier, J. W. (1993). Eastern equine encephalomyelitis. The Lancet, 342, 1281–3CrossRefGoogle ScholarPubMed
Galuzo, I. G. (1975). Landscape epidemiology (epizootiology). In Advances in Veterinary Science and Comparative Medicine, Vol. 2, ed. C. A. Brandly & C. E. Cornelius, pp. 73–95. New York: Academic Press
Gardner, R. H., Milne, B. T., Turner, M. G. & O'Neill, R. O. (1987). Neutral models for the analysis of broad-scale landscape pattern. Landscape Ecology, 1, 19–28CrossRefGoogle Scholar
Glass, G. E., Amerasinghe, F. P., Morgan, J. M. I. & Scott, T. W. (1994). Predicting Ixodes scapularis abundance on white-tailed deer using geographic information systems. American Journal of Tropical Medicine and Hygiene, 51, 538–44CrossRefGoogle ScholarPubMed
Glass, G. E., Schwartz, B. S., Morgan, J. M., Johnson, D. T., Noy, M. P. M. & Israel, E. (1995). Environmental risk factors for Lyme disease identified with geographic information systems. American Journal of Public Health, 85, 944–8CrossRefGoogle ScholarPubMed
Glass, G. E., Cheek, J. E., Patz, J. A., Shields, T. M., Doyle, T. J., Thoroughman, D. A., Hunt, D. K., Enscore, R. E., Gage, K. L., Irland, C., Peters, C. J. & Bryan, R. (2000). Using remotely sensed data to identify areas at risk for hantavirus pulmonary syndrome. Emerging Infectious Diseases, 6, 238–47CrossRefGoogle ScholarPubMed
Goetz, S. J., Prince, S. D. & Small, J. (2000). Advances in satellite remote sensing of environmental variables for epidemiological applications. Advances in Parasitology, 47, 289–304CrossRefGoogle ScholarPubMed
Goovaerts, P. (1998). Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89, 1–45CrossRefGoogle Scholar
Gustafson, E. J. (1998). Quantifying landscape spatial pattern: what is the state of the art?Ecosystems, 1, 143–56CrossRefGoogle Scholar
Hassan, A. N., Beck, L. R. & Dister, S. (1998a). Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies. Journal of the Egyptian Society of Parasitology, 28, 75–87Google Scholar
Hassan, A. N., Dister, S. & Beck, L. R. (1998b). Spatial analysis of lymphatic filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology. Journal of the Egyptian Society of Parasitology, 28, 119–31Google Scholar
Hay, S. I. (1997). Remote sensing and disease control: past, present and future. Transactions of the Royal Society of Tropical Medicine and Hygiene, 91, 105–6CrossRefGoogle ScholarPubMed
Hay, S. I., Packer, M. J. & Rogers, D. J. (1997). The impact of remote sensing on the study and control of invertebrate intermediate hosts and vectors for disease. International Journal of Remote Sensing, 18, 2899–930CrossRefGoogle Scholar
Hay, S. I., Snow, R. W. & Rogers, D. J. (1998a). Prediction of malaria seasons in Kenya using multi-temporal meteorological satellite sensor data. Transactions of the Royal Society of Tropical Medicine and Hygiene, 92, 12–20CrossRefGoogle Scholar
Hay, S. I., Snow, R. W. & Rogers, D. J. (1998b). From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives. Parasitology Today, 14, 306–13CrossRefGoogle Scholar
Hay, S. I., Omumbo, J. A., Craig, M. H. & Snow, R. W. (2000a). Earth observation, geographic information systems, and Plasmodium falciparum malaria in sub-Saharan Africa. Advances in Parasitology, 47, 173–215CrossRef
Hay, S. I., Myers, M. F., Burke, D. S., Vaughn, D. W., Endy, T., Ananda, N., Shanks, D., Snow, R. W. & Rogers, D. J. (2000b). The etiology of inter-epidemic periods of mosquito-borne disease. Proceedings of the National Academy of Sciences of the USA, 97, 9335–9CrossRefGoogle Scholar
Holmes, E. E. (1997). Basic epidemiological concepts in a spatial context. In Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions, ed. D. Tilman & P. Kareiva, pp. 111–36. Princeton, NJ: Princeton University Press
Hoogstraal, H. (1979). The epidemiology of tick-borne Crimean-Congo hemorrhagic fever in Asia, Europe, and Africa. Journal of Medical Entomology, 15, 307–417CrossRefGoogle ScholarPubMed
Hunsaker, C. T., Graham, R. L., Suter, G. W., O'Neill, R. V., Barnthouse, L. W. & Gardnes, R. H. (1990). Assessing ecological risk on a regional scale. Environmental Management, 14, 325–32CrossRefGoogle Scholar
Hunsaker, C. T., Nisbet, R. A., Lam, D. C. L., Browder, J. A., Baker, W. L., Turner, M. G. & Botkin, D. B. (1993). Spatial models of ecological systems and processes: the role of GIS. In Environmental Modeling with GIS, ed. M. F. Goodchild, B. O. Parks & L. T. Steyaert, pp. 248–64. Oxford: Oxford University Press
Jensen, J. R. (1986). Introductory Digital Image Processing: A Remote Sensing Perspective. Englewood Cliffs, NJ: Prentice-Hall
Jensen, J. R. (2000). Remote Sensing of the Environment: an Earth Resource Perspective. Englewood Cliffs, NJ: Prentice Hall
Kitron, U. (1998). Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. Journal of Medical Entomology, 35, 435–45CrossRef
Kitron, U. (2000). Risk maps: transmission and burden of vector-borne diseases. Parasitology Today, 16, 324–5CrossRefGoogle ScholarPubMed
Kitron, U. & Kazmierczak, J. J. (1997). Spatial analysis of the distribution of Lyme disease in Wisconsin. American Journal of Epidemiology, 145, 558–66CrossRefGoogle ScholarPubMed
Kitron, U. & Mannelli, A. (1994). Modeling the ecological dynamics of tick-borne zoonoses. In Ecological Dynamics of Tick-borne Zoonoses, ed. D. E. Sonenshine & T. N. Mathes, pp. 198–239. New York City, NY: Oxford University Press
Kitron, U., Bouseman, J. K. & Jones, C. J. (1991). Use of the ARC/INFO GIS to study the distribution of Lyme disease ticks in Illinois. Preventive Veterinary Medicine, 11, 243–8CrossRefGoogle Scholar
Kitron, U., Jones, C. J., Bouseman, J. K., Nelson, J. A. & Baumgartner, D. L. (1992). Spatial analysis of the distribution of Ixodes dammini (Acari: Ixodidae) on white tailed deer in Ogle County, Illinois. Journal of Medical Entomology, 29, 259–66CrossRefGoogle ScholarPubMed
Kitron, U., Otieno, L. H., Hungerford, L. L., Odulaja, A., Brigham, W. U., Okello, O. O., Joselyn, M., Mohamed-Ahmed, M. M. & Cook, E. (1996). Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. Journal of Animal Ecology, 65, 371–80CrossRefGoogle Scholar
Komar, N. & Spielman, A. (1994). Emergence of eastern encephalitis in Massachusetts. Annals of the New York Academy of Science, 740, 157–68CrossRefGoogle ScholarPubMed
Kotlar, N. B. & Wiens, J. A. (1990). Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos, 59, 253–60CrossRefGoogle Scholar
Kovats, R. S. (2000). El Niño and human health. Bulletin of the World Health Organization, 78, 1127–35Google ScholarPubMed
Lavers, C. P. & Haines-Young, R. (1993). Equilibrium landscapes and their aftermath: spatial heterogeneity and the role of new technology. In Landscape Ecology and Geographic Information Systems, ed. R. Haines-Young, D. R. Green & S. Cousins, pp. 57–73. London: Taylor & Francis
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73, 1943–67CrossRefGoogle Scholar
Lillesand, T. M. & Kiefer, R. W. (1999). Remote Sensing and Image Interpretation. New York: John Wiley and Sons
Linthicum, K. J., Bailey, C. L., Davies, F. G. & Tucker, C. J. (1987). Detection of Rift Valley fever viral activity in Kenya by satellite remote sensing imagery. Science, 235, 1656–9CrossRefGoogle ScholarPubMed
Linthicum, K. J., Bailey, C. L., Tucker, C. J., Mitchell, K. D., Logan, T. M., Davies, F. G., Kamau, C. W., Thande, P. C. & Wagateh, J. N. (1990). Applications of polar-orbiting, meteorological satellite data to detect flooding in Rift Valley fever virus vector mosquito habitats in Kenya. Medical Veterinary Entomology, 4, 433–8CrossRefGoogle Scholar
Linthicum, K. J., Bailey, C. L., Tucker, C. J., Gordon, S. W., Logan, T. M., Peters, C. J. & Digoutte, J. P. (1994). Man-made ecological alterations of Senegal River basin on Rift Valley fever transmission. Sistema Terra, Year III, 45–7Google Scholar
Linthicum, K. J., Anyamba, A., Tucker, C. J., Kelley, P. W., Myers, M. F. & Peters, C. J. (1999). Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science, 285, 397–400CrossRefGoogle ScholarPubMed
Lobitz, B., Beck, L., Huq, A., Wood, B., Fuchs, G., Faruque, A. S. G. & Colwell, R. (2000). Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proceedings of the National Academy of Science of the USA, 97, 1438–43CrossRefGoogle ScholarPubMed
Longstreth, J. D. & Wiseman, J. (1989). The potential impact of climate change on patterns of infectious disease in the United States. In The Potential Effects of Global Climate Change on the United States: Appendix G, Health, ed. J. B. Smith & D. A. Tirpak. Washington D.C.: U.S. Environmental Protection Agency
Malone, J. B., Huh, O. K., Fehler, D. P., Wilson, P. A., Wilensky, D. E., Holmes, R. A. & Elmagdoub, A. (1994). Temperature data from satellite images and the distribution of schistosomiasis in Egypt. American Journal of Tropical Medicine and Hygiene, 50, 714–22CrossRefGoogle Scholar
Malone, J. B., Abdel-Rahman, M. S., El Bahy, M. M., Huh, O. K., Shafik, M. & Bavia, M. (1997). Geographic information systems and the distribution of Schistosoma mansoni in the Nile Delta. Parasitology Today, 13, 112–19CrossRefGoogle ScholarPubMed
McGwire, K. Desert Research Institute, Reno, Nevada, unpublished report
Meade, M. S., Florin, J. W. & Gesler, W. M. (1988). Medical Geography. New York: The Guilford Press
Merler, S., Furlanello, C., Chemini, C. & Nicolini, G. (1996). Classification tree methods for analysis of mesoscale distribution of Ixodes ricinus (Acari: Ixodidae) in Trentino, Italian Alps. Journal of Medical Entomology, 33, 888–93CrossRefGoogle ScholarPubMed
Meltzer, M. I. (1991). The potential use of fractals in epidemiology. Preventive Veterinary Medicine, 11, 255–60CrossRefGoogle Scholar
Moore, D. A. & Carpenter, T. E. (1999). Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiologic Reviews, 21, 143–61CrossRefGoogle ScholarPubMed
Nicholson, M. C. & Mather, T. N. (1996). Methods for evaluating Lyme disease risks using geographic information systems and geospatial analysis. Journal of Medical Entomology, 33, 711–20CrossRefGoogle ScholarPubMed
Nix, H. (1986). A biogeographic analysis of Australian elapid snakes. In Atlas of Elapid Snakes of Australia, ed. R. Longmore. Australia Flora and Fauna series, no. 7, pp. 4–15. Canberra: Australian Government Publishing Service
Norval, R. A. I., Perry, B. D. & Young, A. S. (1992). The Epidemiology of Theileriosis in Africa. London: Academic Press
Pascual, M., Rodó, X., Ellner, S. P., Colwell, R. & Bouma, M. J. (2000). Cholera dynamics and El Niño Southern Oscillation. Science, 289, 1766–9CrossRefGoogle ScholarPubMed
Pavlovsky, E. N. (1966). The Natural Nidality of Transmissible Disease, ed. N. D. Levine. Urbana: University of Illinois Press
Pickett, S. T. A. & Cadenasso, M. L. (1995). Landscape ecology: spatial heterogeneity in ecological systems. Science, 269, 331–4CrossRefGoogle ScholarPubMed
Pope, K. O., Sheffner, E. J., Linthicum, K. J., Bailey, C. L., Logan, T. M., Kasischke, E. S., Birney, K., Njogu, A. R. & Roberts, C. R. (1992). Identification of central Kenyan Rift Valley fever virus vector habitats with Landsat TM and evaluation of their flooding status with Airborne Imaging Radar. Remote Sensing of Environment, 40, 185–96CrossRefGoogle Scholar
Pope, K. O., Rejmánková, E., Savage, H. M., Arredondo-Jimenez, J. I., Rodríguez, M. H. & Roberts, D. R. (1993). Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecological Applications, 4, 81–90CrossRefGoogle Scholar
Quattrochi, D. A. & Pelletier, R. E. (1990). Remote sensing for analysis of landscapes: an introduction. In Ecological Studies, Vol. 82, Quantitative Methods in Landscape Ecology, ed. M. G. Turner & R. H. Gardner, pp. 51–76. New York: Springer-Verlag
abundance of the tick Rhipicephalus appendiculatus in southern Africa: a new perspective
Randolph, S. E. (2000). Ticks and tick-borne disease systems in space and from space. In Advances in Parasitology, Vol. 47, ed. S. Hay, S. Randolph & D. Rogers, pp. 217–43. London: Academic PressCrossRef
Reisen, W. K., Lothrop, H. D., Presser, S. B., Hardy, J. L. & Gordon, E. W. (1997). Landscape ecology of arboviruses in southeastern California: Temporal and spatial patterns of enzootic activity in Imperial Valley, 1991–1994. Journal of Medical Entomology, 34, 179–88CrossRefGoogle ScholarPubMed
Rejmánková, E., Roberts, D. R., Pawley, A., Manguin, S. & Polanco, J. (1995). Predictions of adult Anopheles albimanus densities in villages based on distance to remotely sensed larval habitats. American Journal of Tropical Medicine and Hygiene, 53, 482–8CrossRefGoogle ScholarPubMed
Risser, P. G., Karr, J. R. & Forman, R. T. T. (1984). Landscape Ecology: Directions and Approaches. Illinois Natural History Survey Special Publication No. 2
Roberts, D. R., Paris, J. F., Manguin, S., Harbach, R. E., Woodruff., R., Rejmánková, E., Polanco, J., Wullschleger, B. & Legters, L. J. (1996). Predictions of malaria vector distribution in Belize based on multispectral satellite data. American Journal of Tropical Medicine and Hygiene, 54, 304–8CrossRefGoogle ScholarPubMed
Robinson, T. P. (2000). Spatial statistics and geographical information systems in epidemiology and public health. Advances in Parasitology, 47, 82–120Google ScholarPubMed
Robinson, T. P., Rogers, D. J. & Williams, B. (1997a). Univariate analysis of tsetse habitat in the common fly belt of Southern Africa using climate and remotely sensed vegetation data. Medical Veterinary Entomology, 11, 223–34CrossRefGoogle Scholar
Robinson, T. P., Rogers, D. J. & Williams, B. (1997b). Mapping tsetse habitat suitability in the common fly belt of Southern Africa using multivariate analysis of climate and remotely sensed data. Medical Veterinary Entomology, 11, 235–45CrossRefGoogle Scholar
Rodríguez, A. D., Rodríguez, M. H., Hernández, J. E., Dister, S. W., Beck, L. R., Rejmánková, E. & Roberts, D. R. (1996). Landscape surrounding human settlements and malaria mosquito abundance in southern Chiapas, Mexico. Entomological Society of America, 33, 39–48Google Scholar
Rogers, D. J. (1991). Satellite imagery, tsetse and trypanosomiasis. Preventive Veterinary Medicine, 11, 201–20CrossRefGoogle Scholar
Rogers, D. J. & Randolph, S. E. (1993). Distribution of tsetse and ticks in Africa, past, present and future. Parasitology Today, 9, 266–71CrossRefGoogle Scholar
Rogers, D. J. & Randolph, S. E. (1994). Satellite imagery, tsetse flies and sleeping sickness in Africa. Sistema Terra, Year III 40–3
Rogers, D. J. & Randolph, S. E. (2000). The global spread of malaria in a warmer world. Science, 289, 1763–9CrossRefGoogle Scholar
Rogers, D. J. & Williams, B. G. (1993). Monitoring trypanosomiasis in space and time. Parasitology [Suppl.], 106, 77–92CrossRefGoogle ScholarPubMed
Rogers, D. J. & Williams, B. G. (1994). Tsetse distribution in Africa: seeing the wood and the trees. Large scale ecology and conservation. In Proceedings of the 35th Symposium of the British Ecology Society for Conservation Biology, ed. P. J. Edwards, R. May & N. R. Webb, pp. 247–71. Southampton, UK: University of Southampton
Rogers, D. J., Hay, S. I. & Packer, M. J. (1996). Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Annals of Tropical Medicine and Parasitology, 90, 225–41CrossRefGoogle ScholarPubMed
Schofield, C. J., Apt, W. & Miles, M. A. (1982). The ecology of Chagas' disease in Chile. Ecology of Disease, 1, 117–29Google ScholarPubMed
Shope, R. E. (1992). Impacts of global climate change on human health: spread of infectious disease. In Global Climate Change: Implications, Challenges and Mitigation Measures, ed. S. K. Majumdar, L. S. Kalkstein, B. Yarnal, E. W. Miller & L. M. Rosenfeld Chapter. Easton, PA: The Pennsylvania Academy of Science
Snow, R. W., Marsh, K. & Sueur, D. (1996). The need for a map of transmission intensity to guide malaria control in Africa. Parasitology Today, 12, 455–7CrossRefGoogle Scholar
Snow, R. W., Craig, M. H., Deichmann, U. & Sueur, D. (1999). A preliminary continental risk map for malaria mortality among African children. Parasitology Today, 15, 99–104CrossRefGoogle ScholarPubMed
Star, J. & Estes, J. (1990). Geographic Information Systems: An Introduction. Englewood Cliffs, NJ: Prentice-Hall
Sutherst, R. W. & Maywald, G. F. (1985). A computerised system for matching climates in ecology. Agriculture, Ecosystems and Environment, 13, 281–99CrossRefGoogle Scholar
Thompson, D. F., Malone, J. B., Harb, M., Faris, R., Huh, O. K., Buck, A. A. & Cline, B. L. (1996). Brancroftian filariasis distribution and diurnal temperature differences in the southern Nile Delta. Emerging Infectious Diseases, 2, 234–5CrossRefGoogle Scholar
Thomson, M. C., Connor, S. J., Milligan, P. J. M. & Flasse, S. P. (1996). The ecology of malaria – as seen from Earth-observation satellites. Annals of Tropical Medicine and Parasitology, 90, 243–64CrossRefGoogle ScholarPubMed
Thomson, M. C., Connor, S. J., Milligan, P. J. M. & Flasse, S. P. (1997). Mapping malaria risk in Africa: what can satellite data contribute?Parasitology Today, 13, 313–18CrossRefGoogle ScholarPubMed
Thomson, M. C., Connor, S. J., D'Alessandro, U., Rowlingson, B., Diggle, P., Cresswell, M. & Greenwood, B. (1999). Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results. American Journal of Tropical Medicine and Hygiene, 61, 2–8CrossRefGoogle Scholar
Thomson, M. C., Connor, S. J., O'Neill, K. & Meert, J-P. (2000). Environmental information for prediction of epidemics. Parasitology Today, 16, 137–8CrossRefGoogle ScholarPubMed
Tolezano, J. E. (1994). Ecoepidemiological aspects of American cutaneous leishmaniasis in the state of Sao Paulo, Brazil. Mem Instit Oswaldo Cruz, 89, 427–34CrossRefGoogle ScholarPubMed
Turner, M. G. & Gardner, R. H. (1991). Quantitative methods in landscape ecology: an introduction. In Ecological Studies, Vol. 82, Quantitative Methods in Landscape Ecology, ed. M. G. Turner and R. H. Gardner, pp. 3–14. New York: Springer-VerlagCrossRef
University of Wisconsin-Madison, Environmental Remote Sensing Center, www.ersc.wisc.edu/ersc
Washino, R. K. & Wood, B. L. (1993). Application of remote sensing to vector arthropod surveillance and control. American Journal of Tropical Medicine and Hygiene, 50, 134–44CrossRefGoogle Scholar
Weins, J. A. (1989). Spatial scaling in ecology. Functional Ecology, 3, 385–97CrossRefGoogle Scholar
White, P. C., Brown, J. A. & Harris, S. (1993). Badgers (Meles meles), cattle and bovine tuberculosis (Mycobacterium bovis): an hypothesis to explain the influence of habitat on the risk of disease transmission in southwest England. Proceedings of the Royal Society of London Series B Biological Sciences, 253, 277–84CrossRefGoogle ScholarPubMed
Wood, B. L., Beck, L. R., Lobitz, B. M. & Bobo, M. R. (2000). Education, outreach, and the future of remote sensing in human health. Advances in Parasitology, 47, 331–44CrossRefGoogle ScholarPubMed
World Health Organization (WHO). (1985). Ten Years of Onchocerciasis Control in West Africa: Review of the Work of the Onchocerciasis Control Program in the Volta River Basin Area from 1974–1984. R 386–786–1086–388, Geneva, Switzerland
World Health Organization (WHO). (1990). Potential Health Effects of Climatic Change. Report of a WHO Task Group. WHO/PEP/90/10, Geneva

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×