Modelling the Spatial Distribution of the Anopheles Mosquito for Malaria Risk Zoning Using Remote Sensing and GIS

Remote Sensing and Geographic Information System was used to develop a spatial risk malaria distribution model based on environmental suitability for survival of the Anopheles gambie sp. Complex (A. arabiensis and A. gambae), the vector known to transmit malaria in Zimbabwe (Masendu, 1996). Employing geostatistical techniques, spatial analysis of environmental factors that contribute to the spread of the malaria vector was conducted to develop a malaria risk model that could be used in effective malaria control planning in Zimbabwe. The study was conducted in the Piriwiri, Umfuli and Magondi communal lands of Zimbabwe. A model was developed that defined malaria hot spots in the communal lands where attention must be given in developing plans and strategies for malaria control. Environmental data collected from satellite images and validated by fieldwork were used in the study. Based on expert knowledge, specific environmental factors favourable for Anopheles malaria vector were identified. This information was then used to predict the suitability of the area for the Anopheles mosquito using Indicator Kriging Algorithm (Isaacs et al., 1989). This method calculated the probability of exceeding an environmental indicator threshold (this allowed the prediction that a particular area (location) in the communal lands is suitable for the survival and spread of the Anopheles) and integrated them into a potential vector distribution model for the area. This model was used to determine areas that are potentially risky for malaria. Again the spatial distribution of malaria was calculated, based on clinical malaria data and accessibility to the clinics, and compared with the potential vector distribution zones to determine areas with high malaria risk. Except a few areas in Umfuli that were highly favourable for the Anopheles mosquito, most of the communal lands were not suitable for anopheles to survive indicating that malaria incidences are generally associated with highly favourable areas for the vector. Combining GIS and remote sensing applications with geostatistical analysis is a promising approach to define malaria risk areas in Zimbabwe. However, further quantitative research is necessary to validate the relationships within the malaria transmission system, especially on the vector and the human environment aspects.