Schistosomiasis is a snail-borne disease. The ability to identify this health threat and monitor the disease enables public health officials to take preventive measures (e.g., vector control). Often the identification of infected human hosts and vector snails depends on labor-intensive ground survey methods for data collection. This method introduces inconsistencies that lead to inaccuracies. By contrast, satellite remote-sensing methods make it possible to obtain standardized data over large geographic areas (Abdel-Rahman et al., 2001). As a result there is increasing interest in these methods for health-related applications.

Remotely sensed data (from NOAA’s AVHRR sensor) are being used in spatial decision-support systems to manage control programs for schistosomiasis in Africa. These efforts include a program in the Lake Victoria region building on Malone et al.’s (2001) work in East Africa. The Lake Victoria program also benefits from experiences during a four-year effort in Egypt in which a schistosomiasis risk model was developed for the Ministry of Health (Abdel-Rahman et al., 2001). The model enables the ministry to make more accurate decisions in its program of controlling the spread of schistosomiasis. This GIS-based model, along with the data, constitutes the decision-support system. Two sources of remotely sensed data were used. First, diurnal temperature range and a vegetation index (NDVI) were estimated from NOAA AVHRR data. Second, Landsat Thematic Mapper imagery was used to generate a base map. These data were integrated in a GIS with a database of schistosomiasis prevalence, ground survey results on soil type and salinity, and thematic information from 1:250,000 and 1:10,000 paper maps. From this study it became clear that remote-sensing could extend the capability of the ministry to manage schistosomiasis in Egypt.