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

Published online by Cambridge University Press:  23 July 2009

C. Simoonga*
Affiliation:
Ministry of Health, P.O. Box 30205, 10101 Lusaka, Zambia University of Zambia, School of Medicine, Department of Community Medicine, P.O. Box 50110, Lusaka, Zambia
J. Utzinger
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
S. Brooker
Affiliation:
Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, United Kingdom Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI/Wellcome Trust Research Laboratories, Nairobi, Kenya
P. Vounatsou
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
C. C. Appleton
Affiliation:
School of Biological and Conservation Sciences, University of KwaZulu-Natal, Howard College Campus, Durban 4041, South Africa
A. S. Stensgaard
Affiliation:
Mandahl-Barth Research Centre, DBL–Institute for Veterinary Pathobiology, Faculty of Life Science, University of Copenhagen, Thorvaldsensvej 57, DK-1871 Frederiksberg, Denmark Center for Macroecology and Evolution, Department of Biology, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen O, Denmark
A. Olsen
Affiliation:
Mandahl-Barth Research Centre, DBL–Institute for Veterinary Pathobiology, Faculty of Life Science, University of Copenhagen, Thorvaldsensvej 57, DK-1871 Frederiksberg, Denmark
T. K. Kristensen
Affiliation:
Mandahl-Barth Research Centre, DBL–Institute for Veterinary Pathobiology, Faculty of Life Science, University of Copenhagen, Thorvaldsensvej 57, DK-1871 Frederiksberg, Denmark
*
*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: [email protected]

Summary

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.

Type
SECTION 1 ADVOCACY AND DEFINING AREAS IN NEED OF CONTROL
Copyright
Copyright © Cambridge University Press 2009

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