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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

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  • 1 Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
  • | 2 Department of Infectious Diseases, Heng County Centers for Disease Control and Prevention, 16 Gongyuan Road, Heng County, China;
  • | 3 Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China;
  • | 4 Geriatrics Digestion Department of Internal Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, China

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.

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Author Notes

Address correspondence to Hao Liang, Life Sciences Institute, Guangxi Medical University, Guangxi, China, E-mail: lianghao@gxmu.edu.cn or Li Ye, Guangxi Key Laboratory of AIDS Prevention and Treatment, School of Public Health, Guangxi Medical University, Guangxi, China, E-mail: yeli@gxmu.edu.cn.

These authors contributed equally to this paper.

Financial support: The study was supported by National Natural Science Foundation of China (NSFC, 81560326, 81460305, 31360033, 81460511), Guangxi Scientific and Technological Development Project (Gui Ke Gong 14124003-1), Key Project of Guangxi Universities Scientific Research (2013ZD012, YB2014062), Guangxi University “100-Talent” Program & Guangxi university innovation team and outstanding scholars program (Gui Jiao Ren 2014[7]).

Authors’ addresses: Wudi Wei, Junjun Jiang, Bingyu Liang, Jiegang Huang, Jingzhen Lai, Jun Yu, Fengxiang Qin, and Jinming Su, Guangxi Medical University, Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Guangxi, China, E-mails: 281299623@qq.com, johnjeang@qq.com, liangbingyu@gxmu.edu.cn, jieganghuang@gxmu.edu.cn, 523903530@qq.com, 515308986@qq.com, 465454806@qq.com, and 176334718@qq.com. Lian Gao, Department of Infectious Diseases, Heng County Centers for Disease Control and Prevention, Heng County, China, E-mail: 2215925226@qq.com. Ning Zang, Chuanyi Ning, and Yanyan Liao, Life Sciences Institute, Guangxi Medical University, Nanning, China, E-mail: 1979616281@qq.com, ningchuanyi@gxmu.edu.cn, and 604166286@qq.com. Hui Chen, Geriatrics Digestion Department of Internal Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China, E-mail: chenhui680804@163.com. Li Ye and Hao Liang, Life Sciences Institute, Guangxi Medical University, Nanning, China, and Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention and Control of Highly Prevalent Disease, Guangxi Medical University, Nanning, Guangxi, China, E-mails: yeli@gxmu.edu.cn and lianghao@gxmu.edu.cn.

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