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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 Vegetatio...


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

To formally quantify the level of under-detection of Trypanosoma brucei rhodesiense sleeping sickness (SS) during an epidemic in Uganda, a decision tree (under-detection) model was developed; concurrently, to quantify the subset of undetected cases that sought health care but were not diagnosed, ...


Odiit, M.Coleman, P.G.Liu, W.C.McDermott, John J.Fèvre, Eric M.Welburn, S.C.Woolhouse, Mark E.J.[Quantifying the level of under-detection of Trypanosoma brucei rhodesiense sleeping sickness cases]Quantifying the level of under-detection of Trypanosoma brucei rhodesiense sleeping sickness cases

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