Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda
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.
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| Format: | Journal Article biblioteca |
| Language: | English |
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2006
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| Subjects: | trypanosoma rhodesiense, trypanosomiasis, geographical information systems, remote sensing, villages, |
| Online Access: | https://hdl.handle.net/10568/29673 |
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dig-cgspace-10568-296732023-02-15T09:47:43Z Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda Odiit, M. Bessell, P.R. Fèvre, Eric M. Robinson, Timothy P. Kinoti, J. Coleman, P.G. Welburn, S.C. McDermott, John J. Woolhouse, Mark E.J. trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages 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. 2006 2013-06-11T09:24:26Z 2013-06-11T09:24:26Z Journal Article Transactions of the Royal Society of Tropical Medicine and Hygiene;100(4): 354-362 0035-9203 https://hdl.handle.net/10568/29673 en Limited Access p. 354-362 Transactions of the Royal Society of Tropical Medicine and Hygiene |
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trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages |
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trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages Odiit, M. Bessell, P.R. Fèvre, Eric M. Robinson, Timothy P. Kinoti, J. Coleman, P.G. Welburn, S.C. McDermott, John J. Woolhouse, Mark E.J. Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| description |
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. |
| format |
Journal Article |
| topic_facet |
trypanosoma rhodesiense trypanosomiasis geographical information systems remote sensing villages |
| author |
Odiit, M. Bessell, P.R. Fèvre, Eric M. Robinson, Timothy P. Kinoti, J. Coleman, P.G. Welburn, S.C. McDermott, John J. Woolhouse, Mark E.J. |
| author_facet |
Odiit, M. Bessell, P.R. Fèvre, Eric M. Robinson, Timothy P. Kinoti, J. Coleman, P.G. Welburn, S.C. McDermott, John J. Woolhouse, Mark E.J. |
| author_sort |
Odiit, M. |
| title |
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| title_short |
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| title_full |
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| title_fullStr |
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| title_full_unstemmed |
Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda |
| title_sort |
using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in uganda |
| publishDate |
2006 |
| url |
https://hdl.handle.net/10568/29673 |
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