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has been cited by the following article:

• TITLE: Statistical and Machine Learning Methods for Vaccine Demand Forecasting: A Comparative Analysis

  AUTHORS: Rachel T. Alegado, Gilbert M. Tumibay

  KEYWORDS: Vaccine Demand, Forecasting, ARIMA, Machine Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.10, October 28, 2020

  ABSTRACT: This study aimed to find a suitable model for forecasting the appropriate stock of vaccines to avoid shortage and over-supply. 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.