Publisher Full Text
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.Links
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 -
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