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.
Odiit
Martin
M
Sleeping Sickness Programme, National Agricultural Research Organization, LIRI Hospital, P.O. Box 96, Tororo, Uganda.
Bessell
Paul R
PR
Fèvre
Eric M
EM
Robinson
Tim
T
Kinoti
Jennifer
J
Coleman
Paul G
PG
Welburn
Susan C
SC
McDermott
John
J
Woolhouse
Mark E J
ME
D000818:Animals; D005544:Forecasting; D040362:Geographic Information Systems; D005843:Geography; D006801:Humans; D011156:Population Density; D012044:Regression Analysis; D012307:Risk Factors; D017215:Satellite Communications; D016833:Trypanosoma brucei rhodesiense; D014353:Trypanosomiasis, African / Q000453:epidemiology* / Q000635:transmission; D014454:Uganda / Q000453:epidemiology