Malaria outbreaks are affecting nearly 40 percent of the earth's population most of whom are living in tropical and subtropical zones. Malaria is an infectious disease that is being transferred by the female mosquito of the species Anopheles. These parasites require suitable environmental parameters in order to complete their development cycles within the mosquito. These parameters are topographic factors like (elevation, slope, soil type and proximity of rivers), land use land cover of Dembia Woreda and distance to health centers As the fly-range of the mosquito is limited to 2 to 4 kilometers and since water, pools are necessary for breeding, and then the vector abundance is significantly higher around water bodies. Dembia Woreda showed a strong seasonal pattern of malaria transmission rate, which is related to the seasonal pattern of rainfall with a lag time varying from a few weeks at the beginning of the rainy season to more than a month at the end of the rainy season (Fig 4.1). The presence of a lag-time between peak malaria transmission and seasonal rainfall distribution is very important for forecasting malaria outbreak using observed weather data. Vegetation cover has an indirect role on malaria vector abundance. The vegetated areas help moisture availability in the air and on the soil. To locate the high potential region for malaria outbreaks, one could extract the map of above mentioned parameters via remote sensing images. A 7ETM+ image of Land sat platform, panchromatic images were stacked and used in this study, and maps of parameters such as elevation, slope, major soil type, river proximity, land use, and health proximity were reclassified and produced in the way of their importance. A weighted linear combination of these layers showed acceptable agreement with the positive malaria cases collected in the stations. The method was computed using multi criteria evaluation (MCE). To run MCE, the selected environmental and physical factors such as topographic factors (elevation, slope, flow distance to stream, and soil type were developed and weighted firstly. Then next land use land cover, health proximity and environmental and physical maps were weighted by using weighted overlay technique and was computed in ArcGIS9.2 Model Builder to generate malaria risk area map. The final out put based on this approach is a high risk malaria map, which is classified into five classes including, very high-risk area, high-risk area, moderate risk area and low risk area and very low-risk area. This help to plan important actions to be taken in early warning, forecasting, monitor, control and prevent malaria epidemic.

Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in GIS and Remote Sensing