A Forecast System for Making Decision of Vaccine Demand

R.K. Chiu, C.-L. Chan, and C.K. Feng (Taiwan)


Computational Intelligence, Vaccine Demand, Computer Based Forecast Model, Neural Network, Auto-Regressive Integrated Moving Average.


This paper presents a computer-based forecast model to build a decision support system aiming to forecast the annual vaccine demand for specific vaccines of Taiwan so that the governmental immunization authority can take advantage of this outcome generated from this system to make a better decision for budgeting and purchasing the annual demand for specific vaccines . The system employs the models of Auto-Regressive Integrated Moving Average and Neural Network, respectively to forecast the relative demand for designated vaccines. The results generated by these two models are then used to compute the estimation of annual workload and the amount of vaccine demand, so that the proper and reasonable demand and cost spent may be decided in making decision for vaccine preparation, budgeting, and purchasing.

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