eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2018-09-03 6 4 170 175 10.12691/ajams-6-4-7 AJAMS2018647 article Adeboye Nureni Olawale nureni.adeboye@federalpolyilaro.edu.ng 1 Ezekiel Imekela Donaldson 1 Department of Mathematics & Statistics, Federal Polytechnic, Ilaro, Nigeria, P.M.B 50 That malaria contributes substantially to the poor health situation in Africa is an understatement. To help government in the continuous provision of necessary measures needful to curb increasing spread of the parasites, there is a need to build an appropriate time domain model which can be used to forecast the future rate of spread. As a result of this, ARIMA model was built for analyzing the secondary data collected on the incidence of malaria. It was discovered that the data was not stationary and stationarity was achieved through 2nd order differencing. The ACF and PACF of the differenced data suggested possible models for selection. AIC and MSE were used to select the models that really provided a best fit for the time series. From various ARIMA models generated, ARIMA model was found to best fit the malaria data. Ljung-Box test and Shapiro-Wilk test met all necessary conditions for independence and normality. The model was used for forecast and it was observed that there is going to be a steady increase in malaria prevalence. http://pubs.sciepub.com/ajams/6/4/7/ajams-6-4-7.pdf autocorrelation function partial autocorrelation function stationarity malaria morbidity ARIMA