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A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.
Am J Trop Med Hyg. 2017 Sep; 97(3):799-805.AJ

Abstract

It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.

Authors+Show Affiliations

Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Department of Infectious Diseases, Heng County Centers for Disease Control and Prevention, 16 Gongyuan Road, Heng County, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China. Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China. Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China. Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Geriatrics Digestion Department of Internal Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China.Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China. Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28820678

Citation

Wei, Wudi, et al. "A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China." The American Journal of Tropical Medicine and Hygiene, vol. 97, no. 3, 2017, pp. 799-805.
Wei W, Jiang J, Gao L, et al. A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China. Am J Trop Med Hyg. 2017;97(3):799-805.
Wei, W., Jiang, J., Gao, L., Liang, B., Huang, J., Zang, N., Ning, C., Liao, Y., Lai, J., Yu, J., Qin, F., Chen, H., Su, J., Ye, L., & Liang, H. (2017). A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China. The American Journal of Tropical Medicine and Hygiene, 97(3), 799-805. https://doi.org/10.4269/ajtmh.16-0648
Wei W, et al. A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China. Am J Trop Med Hyg. 2017;97(3):799-805. PubMed PMID: 28820678.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China. AU - Wei,Wudi, AU - Jiang,Junjun, AU - Gao,Lian, AU - Liang,Bingyu, AU - Huang,Jiegang, AU - Zang,Ning, AU - Ning,Chuanyi, AU - Liao,Yanyan, AU - Lai,Jingzhen, AU - Yu,Jun, AU - Qin,Fengxiang, AU - Chen,Hui, AU - Su,Jinming, AU - Ye,Li, AU - Liang,Hao, Y1 - 2017/08/18/ PY - 2017/8/19/pubmed PY - 2017/10/19/medline PY - 2017/8/19/entrez SP - 799 EP - 805 JF - The American journal of tropical medicine and hygiene JO - Am. J. Trop. Med. Hyg. VL - 97 IS - 3 N2 - It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models. SN - 1476-1645 UR - https://www.unboundmedicine.com/medline/citation/28820678/A_New_Hybrid_Model_Using_an_Autoregressive_Integrated_Moving_Average_and_a_Generalized_Regression_Neural_Network_for_the_Incidence_of_Tuberculosis_in_Heng_County_China_ L2 - http://www.ajtmh.org/content/journals/10.4269/ajtmh.16-0648?crawler=true&mimetype=application/pdf DB - PRIME DP - Unbound Medicine ER -