Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population

• Main Authors: Li Z, Wang Z, Song H, Liu Q, He B, Shi P, Ji Y, Xu D, Wang J
• Format: Article
• Language: English
• Published: Dove Medical Press 2019-04-01
• Series: Infection and Drug Resistance
• Subjects: Model; ARIMA; GRNN; Tuberculosis; Incidence; Forecasting
• Online Access: https://www.dovepress.com/application-of-a-hybrid-model-in-predicting-the-incidence-of-tuberculo-peer-reviewed-article-IDR
• ISSN: 1178-6973

Zhongqi Li,1,2 Zhizhong Wang,3 Huan Song,1 Qiao Liu,1 Biyu He,1 Peiyi Shi,1 Ye Ji,1 Dian Xu,1 Jianming Wang1,21Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China; 2Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Epidemiology and Health Statistic, School of Public Health, NingXia Medical University, Yinchuan, People’s Republic of ChinaObjective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model.Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB.Results: The ARIMA (10, 1, 0) (0, 1, 1)12 model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)12 model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)12 model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model.Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.Keywords: model, ARIMA, GRNN, tuberculosis, incidence, forecasting