Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies.

Remote sensing and geographic information system (GIS) technologies were used to discriminate between 130 villages, in the Nile Delta, at high and low risk for filariasis, as defined by microfilarial prevalence. Landsat Thematic Mapper (TM) data were digitally processed to generate a map of landcover as well as spectral indices such as NDVI and moisture index. A Tasseled Cap transformation was also carried out on the TM data which produced three more indices: brightness, greenness and wetness. GIS functions were used to extract information on landcover and spectral indices within one km buffers around the study villages. The relationship between satellite data and prevalence was investigated using discriminant analysis. The analysis indicated that the most important landscape elements associated with prevalence were water and marginal vegetation, while wetness and moisture index were the most important indices. Discriminant functions generated for these variables were able to correctly predict 80% and 74% of high and low prevalence villages, respectively, with an overall accuracy of 77%. The present approach provides a promising tool for regional filariasis surveillance and helps direct control efforts.