Landscape Epidemiology and RS/GIS

Landscape epidemiology involves the identification of geographical areas where disease is transmitted. It is a holistic approach that involves the interactions and associations between elements of the physical and cultural environments. First expressed by the Russian epidemiologist Pavlovsky (1966), the theory behind landscape epidemiology is that by knowing the vegetation and geologic conditions necessary for the maintenance of specific pathogens in nature, one can use the landscape to identify the spatial and temporal distribution of disease risk. Key environmental elements, including elevation, temperature, rainfall, and humidity, influence the presence, development, activity, and longevity of pathogens, vectors, zoonotic reservoirs of infection, and their interactions with humans (Meade et al., 1988).

Vegetation type and distribution are also influenced by the environmental variables mentioned above, and can be expressed as landscape elements that can be sensed remotely and whose relationships can be modeled spatially. For the past 29 years, aerospace-based technologies have proved to be valuable tools for describing the earth's landscape. Since the launch of ERTS-1 in 1972, NASA has initiated programs to integrate these technologies into the areas of forestry, agriculture, geology, and public health. Within a decade of that initial launch, operational programs had been developed in all areas-except public health. In large part, this was because priorities within the public health community were focused on vaccine development, therapies, and the continuation of traditional ground-based surveillance and control methods. Within two decades of the launch, however, health agencies began to re-evaluate other approaches in the face of worsening health conditions around the world. Simultaneous with this change have been significant advances in computer processing, improvements in data acquisition (i.e., additional RS sensors with higher spatial, spectral, and temporal resolution), reduced hardware/software costs, and the development of computer-based GIS technology; all these factors have led parts of the public health community to question their earlier reservations regarding the utility of aerospace-based technologies. To better apply these technologies and satellite-sensor capabilities, CHAART has developed a set of web pages to evaluate sensors for health applications. These pages indicate new opportunities for exploring the connections between the landscape and health using existing and yet to be acquired remotely sensed data.

Previous research involving RS data to study disease has focused on identifying and mapping vector habitats, or assessing environmental factors related to vector habitat quality (Barnes and Cibula, 1979; Wagner et al., 1979; Cross et al., 1984; Hayes et al., 1985; Linthicum et al., 1987, 1999; Welch et al., 1989; Rogers and Randolph, 1991; Hugh-Jones et al., 1992; Pope et al., 1992). Recent studies have begun investigating the application of RS and spatial analysis techniques to identify and map landscape elements that collectively define vector and human population dynamics related to disease transmission risk (Daniel and Kolar, 1990; Wood et al., 1991; Glass et al., 1992; Wood et al., 1992b, 1992c; Dister et al., 1993, 1997; Beck et al., 1994, 1997, 2000). At the country or continental scale, imagery from meteorological satellites has been used to relate the temporal patterns and variations in rainfall and vegetation green-up. Much of this was trypanosomiasis work using AVHRR data acquired over Africa (Rogers et al., 1996; Rogers and Randolph, 1991, 1993). See Hay et al., 1996, for a review of how data from meteorological satellites have been used to study arthropod vectors of disease.

The use of GIS has many implications for landscape epidemiology because it provides to users the ability to store, integrate, query, display, and analyze data from the molecular level to that of satellite resolution through their shared spatial components. Field observations on environmental conditions, including vegetation, water, and topography, can be combined in a GIS to direct interpretation of RS data and facilitate characterization of the landscape in terms of vector and pathogen prevalence. The associations between disease risk variables (e.g., vector, pathogen, and reservoir host abundance and distribution) and environmental variables can be quantified using the spatial analysis capabilities of the GIS. Landscape pattern analysis, combined with statistical analysis, allows us to define landscape predictors of disease risk that can be applied in larger regions where field data are unavailable. This makes RS/GIS a powerful set of tools for disease surveillance, predicting potential disease outbreaks, and targeting intervention programs (CDC, 1994; 1998).


Barnes, C., and W. Cibula. 1979. Some implications of remote sensing technology in insect control programs including mosquitoes. Mosq. News 39:271-282.

Beck, L.R., B.M. Lobitz, and B.L. Wood. 2000. Remote sensing and human health: New sensors and new opportunities. Emerging Infectious Diseases 6(3):217-227.

Beck, L.R., M.H. Rodríguez, S.W. Dister, A.D. Rodríguez, R.K. Washino, D.R. Roberts, and M.A. Spanner. 1997. Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am. J. Trop. Med. Hyg. 56(1):99-106.

Beck, L.R., M.H. Rodríguez, S.W. Dister, A.D. Rodríguez, E. Rejmankova, A. Ulloa, R.A. Meza, D.R. Roberts, J.F. Paris, M.A. Spanner, R.K. Washino, C. Hacker, and L.J. Legters. 1994. Remote sensing as a landscape epidemiological tool to identify villages at high risk for malaria transmission. Am. J. Trop. Med. Hyg. 51(3):271-280.

Centers for Disease Control and Prevention (CDC). 1994. Addressing emerging infectious disease threats: A strategy for the United States. Atlanta, GA: U.S. Dept. of Health and Human Services, CDC.

CDC. 1998. Preventing emerging infectious diseases: A strategy for the 21st Century. Atlanta, GA: U.S. Department of Health and Human Services.

Cross, E., R. Perrine, C. Sheffield, and G. Passaglia. 1984. Predicting areas endemic for schistosomiasis using weather variables and a Landsat data base. Mil. Med. 149:542-544.

Daniel, M., and J. Kolar. 1990. Using satellite data to forecast occurrence of the common tick Ixodes ricinus (L.). J. Hygiene Epidem. Microbiol. Immun. 34:243-252.

Dister, S.W., D. Fish, S. Bros, D.H. Frank, and B.L. Wood. 1997. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am. J. Trop. Med. Hyg. 57(6):687-692.

Dister, S.W., L.R. Beck, B.L. Wood, R. Falco, and D. Fish. 1993. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. Proc., Seventh Annual Symposium on GIS in Forestry, Environment and Natural Resources Management, 15-18 February 1993, Vancouver, British Columbia, Canada.

Glass, G.E., J.M. Morgan III, D.T. Johnson, P.M. Noy, E. Israel, and B.S. Schwartz. 1992. Infectious disease epidemiology and GIS: a case study of Lyme disease. GeoInfo Systems 2:65-69.

Hay, S., C. Tucker, D. Rogers, and M. Packer. 1996. Remotely sensed surrogates of meteorological data for the study of the distribution and abundance of arthropod vectors of disease. Ann. Trop. Med. Parasitol. 90(1):1-19.

Hayes, R.O., E.L. Maxwell, C.J. Mitchell, and T.L. Woodzick. 1985. Detection, identification and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites. Bull. World Health Org. 63:361-374.

Hugh-Jones, M. N. Barre, G. Nelson, K. Wehnes, J. Warner, J. Garvin, and G. Garris. 1992. Landsat-TM identification of Amblyomma variegatum (Acari: Ixodidae) habitats in Guadeloupe. Remote Sens. Environ. 40:43-55.

Linthicum, K.J., A. Anyamba, C.J. Tucker, P.W. Kelley, M.F. Myers, and C.J. Peters. 1999. Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science 285:397-4000.

Linthicum, K.J., C.L. Bailey, F.G. Davies, and C.J. Tucker. 1987. Detection of Rift Valley fever viral activity in Kenya by satellite remote sensing imagery. Science 235:1656-1659.

Meade, M.S., J.W. Florin, and W.M. Gesler. 1988. Medical Geography. The Guilford Press, New York.

Pavlovsky, E.N. 1966. The natural nidality of transmissible disease (N.D. Levine, ed.). University of Illinois Press, Urbana.

Pope, K.O., E.J. Sheffner, K.J. Linthicum, C.L. Bailey, T.M. Logan, E.S. Kasischke, K. Birney, A.R. Njogu, and C.R. Roberts. 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 Sens. Environ. 40:185-196.

Rogers, D.J., and S.E. Randolph. 1991. Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature 351:739-741.

Rogers, D.J., S.I. Hay, and M.J. Packer. 1996. Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. Ann. Trop. Med. Parasitol. 90:225-241.

Rogers, D.J., and S.E. Randolph. 1993. Distribution of tsetse and ticks in Africa, past, present and future. Parasitol. Today 9:266-271.

Wagner, V.E., R. Hill-Rowley, S.A. Narlock, and H.D. Newson. 1979. Remote sensing: A rapid and accurate method of data acquisition for a newly formed mosquito control district. Mosq. News 39:282-287.

Welch, J.B., J.K. Olson, W.G. Hart, S.G. Ingle, and M.R. Davis. 1989. Use of aerial color-infrared photography as a survey technique for Psorophora columbiae oviposition habitats in Texas ricelands. J. Am. Mosq. Control Assoc. 5:147-160. Wood, B.L., L.R. Beck, R.K. Washino, S. Palchick, and P. Sebesta. 1991. Spectral and spatial characterization of rice field mosquito habitat. Int. J. Remote Sens. 12:621-626.

Wood, B.L., L.R. Beck, J.G. Lawless, and J.F. Vesecky. 1992a. Preliminary considerations for a small satellite to monitor environmental change associated with vector-borne. J. Imaging Science and Tech. 36(5):431-439.

Wood, B.L., L.R. Beck, R.K. Washino, K. Hibbard, and J.S. Salute. 1992b. Estimating high mosquito-producing rice fields using spectral and spatial data. Int. J. Remote Sens. 13:2813-2826.

Wood, B.L., R.K. Washino, L.R. Beck, K. Hibbard, M. Pitcairn, D. Roberts, E. Rejmankova, J. Paris, C. Hacker, J.S. Salute, P. Sebesta, and L. Legters. 1992c. Distinguishing high and low anopheline-producing rice fields using remote sensing and GIS technologies. Prev. Vet. Med. 11:277-288.

Back to the CHAART referring page