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-
1.
Shan K, Xu L, Gai R, Wang X, Yu X, Qi H, Cui Y, Lu Y, Xu R, 2014. Spatial analysis on tuberculosis epidemic in China based on geographic information system, 2002–2011. Chin J Publ Health 30: 388–391.
-
2.
Lin D, Huang M, 2014. Trend analysis of the pulmonary tuberculosis epidemic in Guangxi Zhuang autonomous region from 2002 to 2013. Chin J Gen Pract 12: 1113–1117.
-
3.
Long Y, Huang J, He B, Lei Q, Gao L, 2015. The epidemiology characteristics of deaths from infectious disease from 2007 to 2011 in Heng County. J Med Pest Control 31: 20–22.
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4.
He J, Peng Y, Qin Z, 2013. Analysis of the effectiveness of TB control project in Heng County of the global fund. Chin Prim Health Care 27: 34–35.
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5.
Lu H, Chen J, Wang W, Wu L, Shen X, Yuan Z, Yan F, 2015. Efforts to reduce the disparity between permanent residents and temporary migrants: stop TB experiences in Shanghai, China. Trop Med Int Health 20: 1033–1040.
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6.
Zhou C, Tobe RG, Chu J, Gen H, Wang X, Xu L, 2012. Detection delay of pulmonary tuberculosis patients among migrants in China: a cross-sectional study. Int J Tuberc Lung Dis 16: 1630–1636.
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Lei G, Feng Z, Xiangwei L, Qi J, 2010. HIV/TB co-infection in mainland China: a meta-analysis. PLoS One 5: e10736.
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8.
World Health Organization, 2014. Global Tuberculosis Report 2014. Nigeria. Avialable at: http://www.who.int/tb/publications/global_report/2004/en/Nigeria.pdf. Accessed June 2014.
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9.
Gupta S, Granich R, Date A, Lepere P, Hersh B, Gouws E, Samb B, 2014. Review of policy and status of implementation of collaborative HIV-TB activities in 23 high-burden countries. Int J Tuberc Lung Dis 18: 1149–1158.
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10.
Esterhuyse MM et al.., 2015. Epigenetics and proteomics join transcriptomics in the quest for tuberculosis biomarkers. MBio 6: e01187–e01115.
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11.
Hu Y, Mathema B, Zhao Q, Chen L, Lu W, Wang W, Kreiswirth B, Xu B, 2015. Acquisition of second-line drug resistance and extensive drug resistance during recent transmission of Mycobacterium tuberculosis in rural China. Clin Microbiol Infect 21: 1093 e1099–1093 e1018.
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12.
Hu Y, Mathema B, Jiang W, Kreiswirth B, Wang W, Xu B, 2011. Transmission pattern of drug-resistant tuberculosis and its implication for tuberculosis control in eastern rural China. PLoS One 6: e19548.
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13.
Wang T, Zhou Y, Wang L, Huang Z, Cui F, Zhai S, 2015. Using autoregressive integrated moving average model to predict the incidence of hemorrhagic fever with renal syndrome in Zibo, China, 2004–2014. Jpn J Infect Dis 69: 279–284.
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14.
Li Q, Guo NN, Han ZY, Zhang YB, Qi SX, Xu YG, Wei YM, Han X, Liu YY, 2012. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am J Trop Med Hyg 87: 364–370.
-
15.
Lin Y, Chen M, Chen G, Wu X, Lin T, 2015. Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China. BMJ Open 5: e008491.
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16.
Han Q, Su H, Wang CC, Shan XW, Chang WW, Xu ZW, 2012. Prediction on the incidence of blood and sexually transmitted diseases with models of ARIMA and GRNN. Mod Prev Med 6: 1337–1340.
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Ozyildirim BM, Avci M, 2013. Generalized classifier neural network. Neural Netw 39: 18–26.
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Specht DF, 1991. A general regression neural network. IEEE Trans Neural Netw 2: 567–576.
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Leung MT, Chen A-S, Daouk H, 2000. Forecasting exchange rates using general regression neural networks. Comput Oper Res 27: 1093–1110.
-
20.
Zhou L, Yu L, Wang Y, Lu Z, Tian L, Tan L, Shi Y, Nie S, Liu L, 2014. A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PLoS One 9: e104875.
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21.
Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S, Nie S, 2014. Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One 9: e98241.
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22.
Shiyi Cao, Wang F, Tam W, Tse LA, Kim JH, Liu J, Lu Z, 2013. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak 13: 56.
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23.
Zhang G et al.., 2013. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One 8: e80969.
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24.
Wu W, Guo J, An S, Guan P, Ren Y, Xia L, Zhou B, 2015. Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu Province, China. PLoS One 10: e0135492.
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Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ, 2015. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One 10: e0116832.
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Wei W et al.., 2016. Application of a combined. model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One 11: e0156768.
| Past two years | Past Year | Past 30 Days | |
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| Abstract Views | 685 | 619 | 80 |
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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
Wudi Wei
Wudi Wei
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Junjun Jiang
Junjun Jiang
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Lian Gao
Lian Gao
Department of Infectious Diseases, Heng County Centers for Disease Control and Prevention, 16 Gongyuan Road, Heng County, China;
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Bingyu Liang
Bingyu Liang
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Jiegang Huang
Jiegang Huang
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Ning Zang
Ning Zang
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;
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Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China;
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Chuanyi Ning
Chuanyi Ning
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;
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Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China;
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Yanyan Liao
Yanyan Liao
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;
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Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China;
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Jingzhen Lai
Jingzhen Lai
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Jun Yu
Jun Yu
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Fengxiang Qin
Fengxiang Qin
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Hui Chen
Hui Chen
Geriatrics Digestion Department of Internal Medicine, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, China
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Jinming Su
Jinming Su
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Li Ye
Li Ye
Guangxi Key Laboratory of AIDS Prevention and Treatment and Guangxi Universities Key Laboratory of Prevention, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, China;
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Hao Liang
Hao Liang
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;
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Life Sciences Institute, Guangxi Medical University, 22 Shuangyong Road, Nanning, China;
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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
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.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Shan K, Xu L, Gai R, Wang X, Yu X, Qi H, Cui Y, Lu Y, Xu R, 2014. Spatial analysis on tuberculosis epidemic in China based on geographic information system, 2002–2011. Chin J Publ Health 30: 388–391.
-
2.
Lin D, Huang M, 2014. Trend analysis of the pulmonary tuberculosis epidemic in Guangxi Zhuang autonomous region from 2002 to 2013. Chin J Gen Pract 12: 1113–1117.
-
3.
Long Y, Huang J, He B, Lei Q, Gao L, 2015. The epidemiology characteristics of deaths from infectious disease from 2007 to 2011 in Heng County. J Med Pest Control 31: 20–22.
-
4.
He J, Peng Y, Qin Z, 2013. Analysis of the effectiveness of TB control project in Heng County of the global fund. Chin Prim Health Care 27: 34–35.
-
5.
Lu H, Chen J, Wang W, Wu L, Shen X, Yuan Z, Yan F, 2015. Efforts to reduce the disparity between permanent residents and temporary migrants: stop TB experiences in Shanghai, China. Trop Med Int Health 20: 1033–1040.
-
6.
Zhou C, Tobe RG, Chu J, Gen H, Wang X, Xu L, 2012. Detection delay of pulmonary tuberculosis patients among migrants in China: a cross-sectional study. Int J Tuberc Lung Dis 16: 1630–1636.
-
7.
Lei G, Feng Z, Xiangwei L, Qi J, 2010. HIV/TB co-infection in mainland China: a meta-analysis. PLoS One 5: e10736.
-
8.
World Health Organization, 2014. Global Tuberculosis Report 2014. Nigeria. Avialable at: http://www.who.int/tb/publications/global_report/2004/en/Nigeria.pdf. Accessed June 2014.
-
9.
Gupta S, Granich R, Date A, Lepere P, Hersh B, Gouws E, Samb B, 2014. Review of policy and status of implementation of collaborative HIV-TB activities in 23 high-burden countries. Int J Tuberc Lung Dis 18: 1149–1158.
-
10.
Esterhuyse MM et al.., 2015. Epigenetics and proteomics join transcriptomics in the quest for tuberculosis biomarkers. MBio 6: e01187–e01115.
-
11.
Hu Y, Mathema B, Zhao Q, Chen L, Lu W, Wang W, Kreiswirth B, Xu B, 2015. Acquisition of second-line drug resistance and extensive drug resistance during recent transmission of Mycobacterium tuberculosis in rural China. Clin Microbiol Infect 21: 1093 e1099–1093 e1018.
-
12.
Hu Y, Mathema B, Jiang W, Kreiswirth B, Wang W, Xu B, 2011. Transmission pattern of drug-resistant tuberculosis and its implication for tuberculosis control in eastern rural China. PLoS One 6: e19548.
-
13.
Wang T, Zhou Y, Wang L, Huang Z, Cui F, Zhai S, 2015. Using autoregressive integrated moving average model to predict the incidence of hemorrhagic fever with renal syndrome in Zibo, China, 2004–2014. Jpn J Infect Dis 69: 279–284.
-
14.
Li Q, Guo NN, Han ZY, Zhang YB, Qi SX, Xu YG, Wei YM, Han X, Liu YY, 2012. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am J Trop Med Hyg 87: 364–370.
-
15.
Lin Y, Chen M, Chen G, Wu X, Lin T, 2015. Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China. BMJ Open 5: e008491.
-
16.
Han Q, Su H, Wang CC, Shan XW, Chang WW, Xu ZW, 2012. Prediction on the incidence of blood and sexually transmitted diseases with models of ARIMA and GRNN. Mod Prev Med 6: 1337–1340.
-
17.
Ozyildirim BM, Avci M, 2013. Generalized classifier neural network. Neural Netw 39: 18–26.
-
18.
Specht DF, 1991. A general regression neural network. IEEE Trans Neural Netw 2: 567–576.
-
19.
Leung MT, Chen A-S, Daouk H, 2000. Forecasting exchange rates using general regression neural networks. Comput Oper Res 27: 1093–1110.
-
20.
Zhou L, Yu L, Wang Y, Lu Z, Tian L, Tan L, Shi Y, Nie S, Liu L, 2014. A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PLoS One 9: e104875.
-
21.
Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S, Nie S, 2014. Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PLoS One 9: e98241.
-
22.
Shiyi Cao, Wang F, Tam W, Tse LA, Kim JH, Liu J, Lu Z, 2013. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak 13: 56.
-
23.
Zhang G et al.., 2013. Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS One 8: e80969.
-
24.
Wu W, Guo J, An S, Guan P, Ren Y, Xia L, Zhou B, 2015. Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu Province, China. PLoS One 10: e0135492.
-
25.
Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ, 2015. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One 10: e0116832.
-
26.
Wei W et al.., 2016. Application of a combined. model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One 11: e0156768.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 685 | 619 | 80 |
| Full Text Views | 582 | 13 | 0 |
| PDF Downloads | 168 | 7 | 0 |