@article{eprints740,
         journal = {Egyptian Computer Science Journal},
           title = {Computational Predictive Framework towards the Control and Reduction of Malaria incidences in Africa},
            year = {2012},
          number = {2},
          author = {O. O. Oluwagbemi and S. Y. Clarence},
          volume = {36},
           month = {May},
           pages = {1--17},
        abstract = {Malaria persists as a problematic disease in Africa. It is the main cause of morbidity and mortality of children and efforts are currently being pooled to increase the control measures within endemic countries. With this in mind, we developed and applied a malaria control strategy from a computational perspective, to analyze, predict and offer appropriate recommendations and control measures of malaria data obtained from WHO ten Sub Saharan countries malaria report of 2008 . The analytical tool used is based on the C\# programming language embedded artificial neural network intelligence system. From the outcome obtained, the system demonstrated some level of intelligence and showed the effects and impacts of some controllable factors on future malaria occurrence. The system at 90\% prediction intensity showed malaria infection course to decline sharply by 2014 in all the study countries, ranging from 15.71\% in Madagascar, 35.46\% in Malawi, 38.44\% in Nigeria, 38.98\% in Sudan , 39.05\% in Ethiopia 39.09\% in Zambia, 40,08\% in Ghana, 42.61\% in Kenya, 45.21\% in Uganda and 46.63\% Mozambique respectively. Therefore, more future prevention, control and management interventions are needed in Madagascar and Mozambique by 2014 as compared to the rest of the countries studied. In conclusion, the tool can be used to produce sensible and logical results which can be applied to achieve reduction of possible future malaria occurrences by governmental, NGOs and other relevant health agencies for proper public health planning.},
             url = {http://eprints.covenantuniversity.edu.ng/740/}
}