Sordhiq/Climate-Malaria-Prediction-in-Damaturu

Predictive modelling in predicting malaria prevalence using climatic variables in Damaturu, Yobe State, Nigeria # Climate-Malaria Prediction in Damaturu The aim of this project is to understand what major climatic factors are responsible for malaria prevalence in Damaturu, using climatic variables. Also, the study seek to understand what machine learning algorithm is the most suitable in predicting malaria occurrences ahead of time, for assist health workers in taking proactive measures in stemming the tide – especially within hotbead areas and during peak periods. Findings indicated that there are more cases of malaria in the rainy season than the dry and harmattan seasons. Also, there is a moderate positive correlation between rainfall and malaria cases whereas there is a weak negative correlation between temperature and recorded malaria cases while Decision tree model was adopted as the most suitable model in predicting malaria prevalences in Damaturu based on its ability to model non-linear relationship among the climatic predictors.