Modelling malaria susceptibility using geographic information system

Identifying and/or predicting the geography of malaria will help decision makers locate the particular area with the health problem, and to design area-specific interventions. Using GIS (ArcMap 10.1), a spatial analysis of environmental factors that contribute to the spread of malaria vector was conducted to develop a malaria susceptibility model that could be used in effective malaria control planning. The study first determined malaria susceptibility index and combined it with geospatial modelling to predict malaria susceptibility. Clinical malaria cases were then geocoded and tested to determine the accuracy of the prediction. The results show that 72.3, 24.5, 3.1 and 0.1 % of the clinical malaria incidence were found in areas that were predicted to have very high, high, low and very low susceptibility levels. Hence, the model, to a large extent, predicted malaria occurrences. The conclusion is that modelling such as this can help determine spatio-temporal prediction and mapping of malaria incidence to aid in the design and administration of appropriate interventions.