title: Performance of univariate forecasting on seasonal diseases: the case of tuberculosis.
abstract: The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
authors: Department of Computer and Information Science, Universiti Teknonologi PETRONAS, Bandar Seri Iskandar, 31750, Tronoh, Perak, Malaysia, 1.
authors: Adhistya Erna
authors: AE
authors: Permanasari
authors: Dayang Rohaya Awang
authors: DR
authors: Rambli
authors: P Dhanapal Durai
authors: PD
authors: Dominic
mesh_terms: D019295:Computational Biology; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D005544:Forecasting / Q000379:methods; D006801:Humans; D015994:Incidence; D016014:Linear Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012621:Seasons; D014376:Tuberculosis / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology