Spatial Analysis of Malaria Epidemiology in the Amanse West District.

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Malaria has become a major global health problem. It affects 3.5-5.0 billion people worldwide with environmental factors contributing about 70-90% of the disease risk. The World Health Organization has estimated that over one million cases of Malaria are reported each year, with more than 80% of these found in Sub-Saharan Africa. The malaria situation in Ghana is typical of sub-Saharan Africa, presenting a serious health problem in Ghana. It is hyper endemic with a crude parasite rate ranging from 10 – 70% with Plasmodium falciparum dominating. Disease Risk mapping has long been effective in disease modeling, monitoring, evaluation and providing major intervention for areas at risk. The spatial dependency of the malaria risk was explored using Poisson variograms and the risk was used to create surface maps from 2004 to 2009 to identify areas at high risk. Bayesian geostatitical approach was then used to correlate the relationship between the elevation and the disease risk. Geographic Information System (GIS) was used to create the risk surfaces and overlays in the study. A buffered distances of 500m, 1000m, 1500 and 2000m was used to overlay the disease risk map with forest, rivers/streams to find out its effects with the disease prevalence. The risk map created in this study, which integrates Poisson statistical methods showed areas at risk, especially in the central portions of the district capital. It also showed an average of 20% rise yearly from 2004 to 2009. The results in the semi-variogram analysis with an average range of 2000m showed that the disease incidence was local and not global. The local nature of the disease occurrence gives credence to the fact that the covariates used which were rivers/streams, forest, temperature, rainfall and elevation had different and independent influence on the malaria prevalence. Areas which were more than 2km away from the water source (rivers/streams) recorded relatively higher cases except for some few within 1km of the Offin and Oda rivers. There was a varied effect of elevation with the disease prevalence as evidenced in the Bayesian regression model. There was a general trend of high disease incidence between 1-3 km from the forest edge. The study also showed that rainfall had an effect on the yearly disease incidence. However, there was no trend as far as the temperature over the disease prevalence was concerned.
A Thesis submitted to the Department of Geomatic Engineering Kwame Nkrumah University of Science and Technology in Partial Fulfilment of the Requirements for the Degree OF MASTER OF SCIENCE, Department of Geomatic Engineering.