A review of spatial technologies with applications for malaria transmission modelling and control in Africa

  • Michael T. Gebreslasie | gebreslasie@ukzn.ac.za School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Durban, South Africa.

Abstract

Spatial technologies, i.e. geographic information systems, remote sensing, and global positioning systems, offer an opportunity for rapid assessment of malaria endemic areas. These technologies coupled with prevalence/incidence data can provide reliable estimates of population at risk, predict disease distributions in areas that lack baseline data and provide guidance for intervention strategies, so that scarce resources can be allocated in a cost-effective manner. This review focuses on the spatial technology applications that have been used in epidemiology and control of malaria in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geospatial technology OR Geographic Information Systems OR Remote Sensing OR Earth Observation OR Global Positioning Systems OR geospatial modelling OR malaria incidence OR malaria prevalence OR malaria risk prediction OR malaria mapping AND malaria AND Africa were used. These included mapping malaria incidence and prevalence, assessing the relationship between malaria and environmental variables as well as applications for malaria early warning systems. The potential of new spatial technology applications utilising emerging satellite information, as they hold promise to further enhance infectious risk mapping and disease prediction, are outlined. We stress current research needs to overcome some of the remaining challenges of spatial technology applications for malaria so that further and sustainable progress can be made to control and eliminate this disease.

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Published
2015-11-26
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Issue
Section
Reviews
Keywords:
Malaria, Geographic information systems, Remote sensing, Mapping and prediction, Africa
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How to Cite
Gebreslasie, M. T. (2015). A review of spatial technologies with applications for malaria transmission modelling and control in Africa. Geospatial Health, 10(2). https://doi.org/10.4081/gh.2015.328