GIS and REMOTE SENSING INTEGRETED FOR MALARIA RISK MAPPING, THE CASE OF KERSA DISTRICT, Ethiopia

Every year, malaria claims over a million lives around the globe. From which Ethiopia is a predominantly malaria prone country like most of sub Saharan African countries with about 75% of the landscape being favorable for the breeding of malaria vector and putting over 50 million people at risk. Attempts have been made to control the disease by eliminating the parasite. However, unknown spatial distribution of mosquito known to cause malaria, eradication of the parasite remains a difficult task. As a result, increased efforts and resources have been channeled towards finding ways of minimizing the disease. Integrating malaria data into a decision support system (DSS) using Geographic Information System and Remote sensing as a tool that can provide information within shortest period of time, so that, decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss. This paper was attempt to asses and produce maps of malaria prone areas including the most important natural factors. Therefore it is expected to integrate the concept and methods of the innovative development and application of GIS and RS regarding malaria prevalence in Kersa District. The input data were based on the geospatial factors including climatic aspects, social aspects and Topographic aspects from secondary data. The study aimed at mapping malaria hazard areas, Vulnerability map, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE). To run MCE, the selected environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/ land cover and Breeding site were developed and weighted. Then weighted overlay technique were computed in ArcGIS 9.2 environment to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2%, 30.8%, 25.1%, 16.6% and 8.3% of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst/ module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid using ArcGIS9.2 environment. The final output based on this approach is a malaria risk map, which is classified into 5 classes which include, very High-risk area, High-risk area, moderate risk area, low risk area and very low risk area. The risk map produced from the overlay analysis showed that 20.5%, 11.6%, 23.8%, 34.1% and 26.4% of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics. It is also possible to suggest that it will be cost effective and time as well as energy saving if the results of this study incorporated into the ongoing malaria eradication programs.