A global network for the control of snail-borne disease using satellite surveillance and geographic information systems.

At a team residency sponsored by the Rockefeller Foundation in Bellagio, Italy, 10-14 April 2000 an organizational plan was conceived to create a global network of collaborating health workers and earth scientists dedicated to the development of computer-based models that can be used for improved control programs for schistosomiasis and other snail-borne diseases of medical and veterinary importance. The models will be assembled using GIS methods, global climate model data, sensor data from earth observing satellites, disease prevalence data, the distribution and abundance of snail hosts, and digital maps of key environmental factors that affect development and propagation of snail-borne disease agents. A work plan was developed for research collaboration and data sharing, recruitment of new contributing researchers, and means of access of other medical scientists and national control program managers to GIS models that may be used for more effective control of snail-borne disease. Agreement was reached on the use of compatible GIS formats, software, methods and data resources, including the definition of a 'minimum medical database' to enable seamless incorporation of results from each regional GIS project into a global model. The collaboration plan calls for linking a 'central resource group' at the World Health Organization, the Food and Agriculture Organization, Louisiana State University and the Danish Bilharziasis Laboratory with regional GIS networks to be initiated in Eastern Africa, Southern Africa, West Africa, Latin America and Southern Asia. An Internet site, www.gnosisGIS.org, (GIS Network On Snail-borne Infections with special reference to Schistosomiasis), has been initiated to allow interaction of team members as a 'virtual research group'. When completed, the site will point users to a toolbox of common resources resident on computers at member organizations, provide assistance on routine use of GIS health maps in selected national disease control programs and provide a forum for development of GIS models to predict the health impacts of water development projects and climate variation.

New tools: potential medical applications of data from new and old environmental satellites.

The last 40 years, beginning with the first TIROS (television infrared observational satellite) launched on 1 April 1960, has seen an explosion of earth environmental satellite systems and their capabilities. They can provide measurements in globe encircling arrays or small select areas, with increasing resolutions, and new capabilities. Concurrently there are expanding numbers of existing and emerging infectious diseases, many distributed according to areal patterns of physical conditions at the earth's surface. For these reasons, the medical and remote sensing communities can beneficially collaborate with the objective of making needed progress in public health activities by exploiting the advances of the national and international space programs. Major improvements in applicability of remotely sensed data are becoming possible with increases in the four kinds of resolution: spatial, temporal, radiometric and spectral, scheduled over the next few years. Much collaborative research will be necessary before data from these systems are fully exploited by the medical community.

Geographic information systems and the distribution of Schistosoma mansoni in the Nile delta.

New computer-based sensor technology and geographic methods have led to emerging interest in use of satellite environmental assessment tools for design of disease control programs, especially for those that are vector borne. The long-range goal of work reported here by John Malone and colleagues on behalf of this Egyptian Ministry of Health-USAID Schistosomiasis Research Project team (Box 1) is to utilize data from sensor systems on board earth-observing satellites to develop more-sensitive disease-prediction and -control models. If successful, methods developed may provide a potentially vital capability for use by disease control program managers, particularly in less-developed countries, where mapping resources are not well advanced. Longer term, broader basic questions on the interaction of environment and disease in anticipation of predicted global climate change may be addressed. These studies focused on the lower Nile river basin of Egypt. The specific objective was to link data on environmental requirements for propagation and transmission of schistosomiasis with parameters measurable from space.

Temperature data from satellite imagery and the distribution of schistosomiasis in Egypt.

Polar orbiting environmental satellites operated by the National Oceanographic and Atmospheric Administration acquire daytime and nighttime thermal infrared measurements of the earth's surface around the world at a spatial resolution of 1.1 km. Day-night pairs of this imagery from the Advanced Very High Resolution Radiometer (AVHRR) were processed to produce temperature maximum, temperature minimum, and diurnal temperature difference (dT) maps of the lower Nile River valley. Nile delta subsets of the dT maps for August 16, 1990 and February 14, 1991 were analyzed in detail. Values of dT at specific locations were derived using the median of 5 x 5 pixels centered on the latitude and longitude of 41 survey sites listed in 1935, 1983, and 1990 schistosomiasis surveys of the Nile Delta. A Spearman correlation coefficient matrix revealed an inverse relationship between site dT values for August 16, 1990 and February 14, 1991 and prevalence of Schistosoma mansoni in the 1935 and 1983 surveys. For S. haematobium, a positive association of site dT values and prevalence was seen for 1935 only. A significant association was observed between 1935 S. mansoni prevalence and that observed in 1983 and 1990; S. haematobium prevalence in 1935 was not correlated with the later surveys. The results suggest that AVHRR thermal difference maps reflect regional hydrologic conditions that can be used as a predictor of environmental risk of schistosomiasis for control program management.