This study aimed to find a suitable model for forecasting the appropriate stock of vaccines to avoid shortage and oversupply. The Auto-Regressive Integrated Moving Average (ARIMA) and Multilayer Perceptron Neural Network (MLPNN) models were used for forecasting time series data. The monthly vaccination coverage was used to develop the models from January 2014 until December 2019. The dataset consists of 72 months of observation, the 60 months of data are used for model fitting from January 2014 to December 2019, and the remaining 12 months of data from January 2019 to December 2019 are used to test the accuracy of the forecast. The most suitable forecast model was selected based on the lowest Root Mean Square Error (RMSE) value and the Mean Absolute Error (MAE). The analytical result shows that the MLPNN model outperformed the ARIMA model in forecasting monthly demand for vaccines. The results will help policymakers improve the proper use of vaccination resources.

This study aimed to find a model to forecast monthly measles immunization coverage using Autoregressive Integrated Moving Average (ARIMA). The monthly registered data for measles immunization coverage from January 2014 to December 2018 were used for the development of the model. The best model with the smallest Normalized Bayesian Information Criterion (BIC) of 8.673 is ARIMA (0, 1, 0). ARIMA (0, 1, 0) was used to forecast the monthly measles immunization coverage for the next 36 months from January 2018 to December 2020. The results obtained prove that this model can be used for forecasting future immunization coverage and will help decision-makers to establish strategies, priorities, and proper use of immunization resources.