Malaria Risk Assessment Using Geographical Information System and Remote Sensing Techniques in Mecha District, West Gojjam, Ethiopia
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Date
2015-06-06
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Addis Ababa University
Abstract
Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. It affects 3.5–5.0 billion people worldwide with environmental factors contributing for about 70–90% of the disease risk. Geographical information System (GIS) has emerged as the core of the spatial technology, which integrates a wide range of dataset available from different sources including Remote Sensing (RS) and Global Positioning System (GPS). In the present study, malaria risk map was carried out by statistically establishing the relationship of various parameters. To identify the statistical correlations between malaria cases and parameters the regression analysis and Normalized Difference Vegetation Index (NDVI) were applied. The study used weighted overlay technique of multi-criteria evaluation in ArcGIS environment to come up with the final risk map. The aim of the present study has to identify and categorize the malaria risk areas of Mecha District of Ethiopia. Eight factors viz., temperature, rainfall, altitude, distance from streams, distance from swamps and ponds, population density, health facilities and land-use/land-use were used to prepare Malaria-risk areas. To produce the final malaria-risk map, three components of malaria risk layers (malaria hazard, element at risk and vulnerability layer) were overlaid using multi-criteria decision-making technique, and further verified by ground truth and village-wise reports of the malaria cases. Four categories of malaria-risk ranging from very high to low were derived. Most of the study area was found to be in strong agreement with 97.99% high and moderate malaria risk in Mecha District. Highest significant correlation was found between rainfall, altitude and temperature and malaria incidence. Based on the output villages such as Merawi town, Amarit, Andinet, Inguty, Qurt Bahir, Tagel Wedefit, Adis Amba and Idget Behibret were identified with very high and high malaria risk areas and require immediate attention from health agencies as well as the local community for designing effective malaria control measures. Hence, it is suggested that GIS and RS tools can be applied for effective identification and mapping of malaria-risk levels, and this help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria risk.
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Keywords
Geographic Information System, Malaria, Regression Analysis, Remote Sensing, Weighted Overlay