The potential of geographical information systems and remote sensing in the epidemiology and control of human helminth infections.

Geographic information systems (GIS) and remote sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of infectious diseases. For helminth infections, however, such applications have only recently begun despite the recognition that infection distribution patterns in endemic areas may have profound effects on parasite population dynamics and therefore the design and implementation of successful control programmes. Here, we review the early applications of these technologies to the major human helminths (geohelminths, schistosomes and the major lymphatic filarial worms), which demonstrate the potential of these tools to serve as: (1) an effective data capture, mapping and analysis tool for the development of helminth atlases; (2) an environment for modeling the spatial distribution of infection in relation to RS and environmental variables, hence furthering the understanding of the impact of density-independent factors in underlying observed parasite spatial distributions and their effective prediction; and (3) a focal tool in parasite control programming given their abilities to (i) better define endemic areas, (ii) provide more precise estimates of populations-at-risk, (iii) map their distribution in relation to health facilities and (iv) by facilitating the stratification of areas by infection risk probabilities, to aid in the design of optimal drug or health measure delivery systems. These applications suggest a successful role for GIS/RS applications in investigating the spatial epidemiology of the major human helminths. It is evident that further work addressing a range of critical issues include problems of data quality, the need for a better understanding of the population biological impact of environmental factors on critical stages of the parasite life-cycle, the impacts and consequences of spatial scale on these relationships, and the development and use of appropriate spatially-explicit statistical and modeling techniques in data analysis, is required if the true potential of this tool to helminthology is to be fully realized.