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Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.
Epidemiol Infect. 2017 04; 145(6):1118-1129.EI

Abstract

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Authors+Show Affiliations

Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.Wuxi Medical School,Jiangnan University,Wuxi,Jiangsu,People's Republic of China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28115032

Citation

Wang, K W., et al. "Hybrid Methodology for Tuberculosis Incidence Time-series Forecasting Based On ARIMA and a NAR Neural Network." Epidemiology and Infection, vol. 145, no. 6, 2017, pp. 1118-1129.
Wang KW, Deng C, Li JP, et al. Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol Infect. 2017;145(6):1118-1129.
Wang, K. W., Deng, C., Li, J. P., Zhang, Y. Y., Li, X. Y., & Wu, M. C. (2017). Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiology and Infection, 145(6), 1118-1129. https://doi.org/10.1017/S0950268816003216
Wang KW, et al. Hybrid Methodology for Tuberculosis Incidence Time-series Forecasting Based On ARIMA and a NAR Neural Network. Epidemiol Infect. 2017;145(6):1118-1129. PubMed PMID: 28115032.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. AU - Wang,K W, AU - Deng,C, AU - Li,J P, AU - Zhang,Y Y, AU - Li,X Y, AU - Wu,M C, Y1 - 2017/01/24/ PY - 2017/1/25/pubmed PY - 2017/6/14/medline PY - 2017/1/25/entrez KW - ARIMA KW - NAR KW - hybrid model KW - tuberculosis (TB) SP - 1118 EP - 1129 JF - Epidemiology and infection JO - Epidemiol. Infect. VL - 145 IS - 6 N2 - Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB. SN - 1469-4409 UR - https://www.unboundmedicine.com/medline/citation/28115032/Hybrid_methodology_for_tuberculosis_incidence_time_series_forecasting_based_on_ARIMA_and_a_NAR_neural_network_ L2 - https://www.cambridge.org/core/product/identifier/S0950268816003216/type/journal_article DB - PRIME DP - Unbound Medicine ER -