Tags

Type your tag names separated by a space and hit enter

Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population.
Infect Drug Resist. 2019; 12:1011-1020.ID

Abstract

Objective:

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.

Authors+Show Affiliations

Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology and Health Statistic, School of Public Health, NingXia Medical University, Yinchuan, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China. Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31118707

Citation

Li, Zhongqi, et al. "Application of a Hybrid Model in Predicting the Incidence of Tuberculosis in a Chinese Population." Infection and Drug Resistance, vol. 12, 2019, pp. 1011-1020.
Li Z, Wang Z, Song H, et al. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infect Drug Resist. 2019;12:1011-1020.
Li, Z., Wang, Z., Song, H., Liu, Q., He, B., Shi, P., Ji, Y., Xu, D., & Wang, J. (2019). Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infection and Drug Resistance, 12, 1011-1020. https://doi.org/10.2147/IDR.S190418
Li Z, et al. Application of a Hybrid Model in Predicting the Incidence of Tuberculosis in a Chinese Population. Infect Drug Resist. 2019;12:1011-1020. PubMed PMID: 31118707.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. AU - Li,Zhongqi, AU - Wang,Zhizhong, AU - Song,Huan, AU - Liu,Qiao, AU - He,Biyu, AU - Shi,Peiyi, AU - Ji,Ye, AU - Xu,Dian, AU - Wang,Jianming, Y1 - 2019/04/29/ PY - 2018/10/10/received PY - 2019/04/04/accepted PY - 2019/5/24/entrez PY - 2019/5/24/pubmed PY - 2019/5/24/medline KW - ARIMA KW - GRNN KW - forecasting KW - incidence KW - model KW - tuberculosis SP - 1011 EP - 1020 JF - Infection and drug resistance JO - Infect Drug Resist VL - 12 N2 - Objective: 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. SN - 1178-6973 UR - https://www.unboundmedicine.com/medline/citation/31118707/Application_of_a_hybrid_model_in_predicting_the_incidence_of_tuberculosis_in_a_Chinese_population_ L2 - https://dx.doi.org/10.2147/IDR.S190418 DB - PRIME DP - Unbound Medicine ER -
Try the Free App:
Prime PubMed app for iOS iPhone iPad
Prime PubMed app for Android
Prime PubMed is provided
free to individuals by:
Unbound Medicine.