Vector-borne Disease Associated with Irrigation, Agriculture, and Environmental Change in Southeastern Turkey: Application of Satellite Image Analysis

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Project institution: Department of Epidemiology and Public Health, Yale University, New Haven, Connecticut

Principal investigator: Dr. Mark L. Wilson1,2
Co-investigators: B. Mahanty1,2, A. Wannebo2, P. MacDonald1, A. Gleason2,3, R. Smith2,3, and S. Aksoy1

1 Department of Epidemiology and Public Health, Yale University
2 Center for Earth Observation, Bingham Laboratory, Yale University
3 Department of Geology and Geophysics, Yale University

The overall objectives of the first year of this project were to characterize the temporal changes in LU/LC [land use/land cover] throughout the entire GAP [Guneydogu Anadolu Projesini] region of southeastern Turkey, and to analyze the spatial distribution of sandfly vectors and human disease as they relate to LU/LC and other environmental characteristics in Urfa province. Three specific aims follow:

  1. To classify and compare geo-referenced satellite images of the GAP region obtained during the past decade and to determine the extent of variation in LU/CU. These results will be used to define the temporal pattern of LU/LC changes resulting from irrigation.

  2. To integrate region-wide LU/LC results with other environmental variables (e.g., soil type, altitude) and with human settlement patterns. Using GIS, data overlays will be constructed by digitizing government maps, and then analyzed. Spatial statistical associations that may influence risk of various vector-borne diseases including leishmaniasis will be produced.

  3. To characterize the present spatial distribution of habitats in Urfa province, and to integrate this information with other data on the abundance and distribution of sandfly vectors and incidence of leishmaniasis. Using a recent, high-resolution satellite image of Urfa, LU/LC will be classified. As part of the other project, data on sandfly species diversity and relative abundance in various habitats will be combined through a GIS. Finally, human cases diagnosed during the past year will be referenced by residence, and another overlay of neighborhood-specific incidences will be created. Spatially referenced data sets will be analyzed for spatial patterns that characterize the eco-epidemiology of leishmaniasis risk associated with specific habitats.

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Last updated: Mar 2000