SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence

The usefulness of forecasting method in predicting the number of disease incidence is important. It motivates development of a system that can predict the future number of disease occurrences. Fluctuation analysis of forecasting result can be used to support the making of policy from the stake holder. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of diasease incidence in human. The dataset for model development was collected from time series data of Malaria occurrences in United States obtained from a study published by Centers for Disease Control and Prevention (CDC). It resulted SARIMA (0,1,1)(1,1,1)
₁₂
as the selected model. The model achieved 21,6% for Mean Absolute Percentage Error (MAPE). It indicated the capability of final model to closely represent and made prediction based on the Malaria historical dataset.