Geographic Information System and Remote Sensing Based Malaria Risk Mapping Using Environmental Factors: A Case of Arba Minch Zuria Woreda, Southern Nations Nationalities and Peoples’ Regional State

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Addis Ababa University


Malaria is one of the world’s serious and complex public health problems and it remains one of the greatest killers of human beings in developing countries. Due to its tropical location and other environmental factors, part of Ethiopia is favorable for mosquito breeding and malaria transmission. The purpose of this project was generating Geographic Information System and Remote Sensing based malaria risk map of Arba Minch Zuria Woreda of the Gamo Gofa Zone, Southern Nations Nationalities and Peoples’ Regional State, by using environmental factors. To generate the risk map, seven parameters were selected depending on previous works and based on discussion made with malaria experts. The environmental factors considered for the analysis are temperature, rainfall, elevation, slope, soil, land use land cover and proximity to water bodies. Weight was assigned for these parameters by pairwise comparison method and weighted overlay was used in Arc GIS spatial analyst tools to produce the final malaria risk map of the study area. The final risk map indicates that from the total of the study area 58 % is mapped as high, 35.7 % as moderate and 6.3 % as low malaria risk level. Malaria risk level with kebele wise comparison shows that eight kebeles are fully belong to high malaria risk and other eight kebeles partially mapped in high risk level. Only six kebeles mapped under low malaria risk areas. Other six kebeles fully mapped under moderate malaria risk level and the rest kebeles mapped partially under moderate and high risk level. In general, based on environmental factors the majority of study area fell in high risk level, which is followed by moderate and low malaria risk level, respectively. To make malaria control and eradication program of the Woreda time and cost efficient, it is important to consider the risk level of kebeles Key Words: GIS, Malaria, Pairwise comparison, Remote Sensing, Risk map, weighted overlay



GIS, Malaria, Pairwise Comparison, Remote Sensing, Risk Map, Weighted Overlay