Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda uri icon

abstract

  • Geographic information systems (GIS) and remote sensing were used to identify villages at high risk for sleeping sickness, as defined by reported incidence. Landsat Enhanced Thematic Mapper (ETM) satellite data were classified to obtain a map of land cover, and the Normalised Difference Vegetation Index (NDVI) and Landsat band 5 were derived as unclassified measures of vegetation density and soil moisture, respectively. GIS functions were used to determine the areas of land cover types and mean NDVI and band 5 values within 1.5 km radii of 389 villages where sleeping sickness incidence had been estimated. Analysis using backward binary logistic regression found proximity to swampland and low population density to be predictive of reported sleeping sickness presence, with distance to the sleeping sickness hospital as an important confounding variable. These findings demonstrate the potential of remote sensing and GIS to characterize village-level risk of sleeping sickness in endemic regions
  • Geographic information systems (GIS) and remote sensing were used to identify villages at high risk for steeping sickness, as defined by reported incidence. Landsat Enhanced Thematic Mapper (ETM) satellite data were classified to obtain a map of land cover, and the Normalised Difference Vegetation Index (NDVI) and Landsat band 5 were derived as unclassified measures of vegetation density and soil moisture, respectively. GIS functions were used to determine the areas of land cover types and mean NDVI and band 5 values within 1.5 km radii of 389 villages where steeping sickness incidence had been estimated. Analysis using backward binary logistic regression found proximity to swampland and tow population density to be predictive of reported steeping sickness presence, with distance to the steeping sickness hospital as an important confounding variable. These findings demonstrate the potential of remote sensing and GIS to characterize village-level risk of steeping sickness in endemic regions. (C) 2005 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

publication date

  • 2006
  • 2006
  • 2006