Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa

C. Simoongaa1a2 c1, J. Utzingera3, S. Brookera4a5, P. Vounatsoua3, C. C. Appletona6, A. S. Stensgaarda7a8, A. Olsena7 and T. K. Kristensena7

a1 Ministry of Health, P.O. Box 30205, 10101 Lusaka, Zambia

a2 University of Zambia, School of Medicine, Department of Community Medicine, P.O. Box 50110, Lusaka, Zambia

a3 Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland

a4 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom

a5 Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI/Wellcome Trust Research Laboratories, Nairobi, Kenya

a6 School of Biological and Conservation Sciences, University of KwaZulu-Natal, Howard College Campus, Durban 4041, South Africa

a7 Mandahl-Barth Research Centre, DBL–Institute for Veterinary Pathobiology, Faculty of Life Science, University of Copenhagen, Thorvaldsensvej 57, DK-1871 Frederiksberg, Denmark

a8 Center for Macroecology and Evolution, Department of Biology, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen O, Denmark


Beginning in 1970, the potential of remote sensing (RS) techniques, coupled with geographical information systems (GIS), to improve our understanding of the epidemiology and control of schistosomiasis in Africa, has steadily grown. In our current review, working definitions of RS, GIS and spatial analysis are given, and applications made to date with RS and GIS for the epidemiology and ecology of schistosomiasis in Africa are summarised. Progress has been made in mapping the prevalence of infection in humans and the distribution of intermediate host snails. More recently, Bayesian geostatistical modelling approaches have been utilized for predicting the prevalence and intensity of infection at different scales. However, a number of challenges remain; hence new research is needed to overcome these limitations. First, greater spatial and temporal resolution seems important to improve risk mapping and understanding of transmission dynamics at the local scale. Second, more realistic risk profiling can be achieved by taking into account information on people's socio-economic status; furthermore, future efforts should incorporate data on domestic access to clean water and adequate sanitation, as well as behavioural and educational issues. Third, high-quality data on intermediate host snail distribution should facilitate validation of infection risk maps and modelling transmission dynamics. Finally, more emphasis should be placed on risk mapping and prediction of multiple species parasitic infections in an effort to integrate disease risk mapping and to enhance the cost-effectiveness of their control.

(Received January 30 2009)

(Revised March 27 2009)

(Accepted April 04 2009)

(Online publication July 23 2009)


c1 Corresponding author. Christopher Simoonga, Ministry of Health, P.O. Box 30205, 10101 Lusaka, Zambia. Tel.: +260 211 253-053; Fax: +260 211 253-053; E-mail: