This research work describes a study that used measles disease data collected through Knoema health surveillance system to evaluate univariate time series method namely; autoregressive integrated moving average (ARIMA). The data obtained from 1980 to 2016 were used as modeling data and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). A low normalized BIC of21.817 was recorded. The accuracy of the statistical model in forecasting future measles disease proved its effectiveness in measles disease breakout surveillance. Although the outcome of this research work, has shown that measles outbreak in the nearest future wi II take a downward trend from 2017 to 2019, as shown in the forecasted output. It was also observed that 40.9% estimate of the proportion of the total variation in the series (measles_!) is explained by the model. The result of th is research work has shown that funds for measles can be diverted to other diseases as little fund is required to facilitate measles vaccine and improve measles vaccination in the country.
