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Application of geo-spatial technology in schistosomiasis modelling in Africa: a review

Tawanda Manyangadze, Moses John Chimbari, Michael Gebreslasie, Samson Mukaratirwa
  • Tawanda Manyangadze
    School of Nursing and Public Health, Department of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa | manyangadze.tawanda@gmail.com
  • Moses John Chimbari
    School of Nursing and Public Health, Department of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa
  • Michael Gebreslasie
    School of Agriculture, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa
  • Samson Mukaratirwa
    School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa

Abstract

Schistosomiasis continues to impact socio-economic development negatively in sub-Saharan Africa. The advent of spatial technologies, including geographic information systems (GIS), Earth observation (EO) and global positioning systems (GPS) assist modelling efforts. However, there is increasing concern regarding the accuracy and precision of the current spatial models. This paper reviews the literature regarding the progress and challenges in the development and utilization of spatial technology with special reference to predictive models for schistosomiasis in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geo-spatial analysis OR remote sensing OR modelling OR earth observation OR geographic information systems OR prediction OR mapping AND schistosomiasis AND Africa were used. Statistical uncertainty, low spatial and temporal resolution satellite data and poor validation were identified as some of the factors that compromise the precision and accuracy of the existing predictive models. The need for high spatial resolution of remote sensing data in conjunction with ancillary data viz. ground-measured climatic and environmental information, local presence/absence intermediate host snail surveys as well as prevalence and intensity of human infection for model calibration and validation are discussed. The importance of a multidisciplinary approach in developing robust, spatial data capturing, modelling techniques and products applicable in epidemiology is highlighted.

Keywords

Earth observation; Geographic information systems; Geo-spatial technologies; Schistosomiasis modelling; Africa

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Submitted: 2015-02-04 09:43:34
Published: 2015-11-04 12:43:14
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Copyright (c) 2015 Tawanda Manyangadze, Moses John Chimbari, Michael Gebreslasie, Samson Mukaratirwa

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