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).
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