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

• Main Authors: Tawanda Manyangadze, Moses John Chimbari, Michael Gebreslasie, Samson Mukaratirwa
• Format: Article
• Language: English
• Published: PAGEPress Publications 2015-11-01
• Series: Geospatial Health
• Subjects: Earth observation; Geographic information systems; Geo-spatial technologies; Schistosomiasis modelling; Africa
• Online Access: http://www.geospatialhealth.net/index.php/gh/article/view/326
• ISSN: 1827-1987; 1970-7096

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: <em>geo-spatial analysis</em> OR <em>remote sensing</em> OR <em>modelling</em> OR <em>earth observation</em> OR <em>geographic information systems</em> OR <em>prediction</em> OR <em>mapping</em> AND <em>schistosomiasis</em> AND <em>Africa</em> 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 <em>viz.</em> 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.