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Malherbe.J, F.A Engelbrecht, W. A. Landman, and C.J Engelbrecht,“Tropical systems from the Southwest Indian Ocean making landfall over the Limpopo River Basin, Southern Africa: a historical perspective”, International Journal of Climatology, Royal Meteorological Society, 2012.

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Article

Spatial Modelling of Malaria Risk Zones Using Environmental, Anthropogenic Variables and GeograPhical Information Systems Techniques

1Department of Physics, Geography and Environmental Science, Great Zimbabwe University, Masvingo


Journal of Geosciences and Geomatics. 2013, Vol. 1 No. 1, 8-14
DOI: 10.12691/jgg-1-1-2
Copyright © 2013 Science and Education Publishing

Cite this paper:
David Chikodzi. Spatial Modelling of Malaria Risk Zones Using Environmental, Anthropogenic Variables and GeograPhical Information Systems Techniques. Journal of Geosciences and Geomatics. 2013; 1(1):8-14. doi: 10.12691/jgg-1-1-2.

Correspondence to: David  Chikodzi, Department of Physics, Geography and Environmental Science, Great Zimbabwe University, Masvingo. Email: dchikodzi@hotmail.com

Abstract

Malaria is one of the major public health problems in Zimbabwe. The research was aimed at deriving a predictive model for malaria epidemiology in the Masvingo province of Zimbabwe at a scale that is sensitive to local changes in risk factors. Eight risk factors were used in the model build up. Each risk factor was first spatially classified in a geographic information system (GIS) according to how it promotes malaria incidence. The factors were then weighted using a pair wise comparison matrix which is part of analytical hierarchy process (AHP). The final malaria prediction model was then prepared by combining all risk factors and their derived weights through the index overlay model in a GIS. Results showed that northern districts of Chivi, Masvingo and Gutu have the least risk of malaria epidemic while as the southern districts of Chiredzi and Mwenezi have the highest risk. In terms of area, places classified as low risk covered 18.86%, moderate risk 35.67% and high risk 45.45% of the total area of the province. Predictions made by the derived model compared favourably with observations from field trials, health clinics and other models being used in Zimbabwe but had finer spatial coverage than previous models.

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