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Infection and Drug Resistance Volume 12, 2019 - Issue
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Original Research

Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population

Zhongqi Li1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China;2 Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Zhizhong Wang3 Department of Epidemiology and Health Statistic, School of Public Health, NingXia Medical University, Yinchuan, People’s Republic of China
,
Huan Song1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Qiao Liu1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Biyu He1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Peiyi Shi1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Ye Ji1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
,
Dian Xu1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China
&
Jianming Wang1 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China;2 Key Laboratory of Infectious Diseases, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of ChinaCorrespondence[email protected]
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Pages 1011-1020 | Published online: 29 Apr 2019

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Shuangshuang Chen, Xinqiang Wang, Jiawen Zhao, Yongzhong Zhang & Xiaohong Kan. (2022) Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022. Infection and Drug Resistance 15, pages 3503-3512.
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Wenhao Ding, Yanyan Li, Yichun Bai, Yuhong Li, Lei Wang & Yongbin Wang. (2021) Estimating the Effects of the COVID-19 Outbreak on the Reductions in Tuberculosis Cases and the Epidemiological Trends in China: A Causal Impact Analysis. Infection and Drug Resistance 14, pages 4641-4655.
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Yuhan Xiao, Yanyan Li, Yuhong Li, Chongchong Yu, Yichun Bai, Lei Wang & Yongbin Wang. (2021) Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China. Infection and Drug Resistance 14, pages 3849-3862.
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Chongchong Yu, Chunjie Xu, Yuhong Li, Sanqiao Yao, Yichun Bai, Jizhen Li, Lei Wang, Weidong Wu & Yongbin Wang. (2021) Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model. Infection and Drug Resistance 14, pages 2809-2821.
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Jizhen Li, Yuhong Li, Ming Ye, Sanqiao Yao, Chongchong Yu, Lei Wang, Weidong Wu & Yongbin Wang. (2021) Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China. Infection and Drug Resistance 14, pages 1941-1955.
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Yongbin Wang, Chunjie Xu, Sanqiao Yao, Yingzheng Zhao, Yuchun Li, Lei Wang & Xiangmei Zhao. (2020) Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India. Infection and Drug Resistance 13, pages 3335-3350.
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Yongbin Wang, Chunjie Xu, Jingchao Ren, Weidong Wu, Xiangmei Zhao, Ling Chao, Wenjuan Liang & Sanqiao Yao. (2020) Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework. Infection and Drug Resistance 13, pages 733-747.
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Yongbin Wang, Chunjie Xu, Yuchun Li, Weidong Wu, Lihui Gui, Jingchao Ren & Sanqiao Yao. (2020) An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China. Infection and Drug Resistance 13, pages 867-880.
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Qiao Liu, Zhongqi Li, Ye Ji, Leonardo Martinez, Ui Haq Zia, Arshad Javaid, Wei Lu & Jianming Wang. (2019) Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infection and Drug Resistance 12, pages 2311-2322.
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Articles from other publishers (19)

Lennart B. Sina, Cristian A. Secco, Midhad Blazevic & Kawa Nazemi. (2023) Hybrid Forecasting Methods—A Systematic Review. Electronics 12:9, pages 2019.
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Stephen Siamba, Argwings Otieno & Julius Koech. (2023) Application of ARIMA, and hybrid ARIMA Models in predicting and forecasting tuberculosis incidences among children in Homa Bay and Turkana Counties, Kenya. PLOS Digital Health 2:2, pages e0000084.
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Wenjuan Liang, Ailing Hu, Pan Hu, Jinqin Zhu & Yongbin Wang. (2022) Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China. International Journal of Biometeorology 67:1, pages 55-65.
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Nur Şebnem ERSÖZ, Pınar GÜNER, Ayhan AKBAŞ & Burcu BAKİR-GUNGOR. (2022) COMPARATIVE PERFORMANCE ANALYSIS OF ARIMA, PROPHET AND HOLT-WINTERS FORECASTING METHODS ON EUROPEAN COVID-19 DATACOMPARATIVE PERFORMANCE ANALYSIS OF ARIMA, PROPHET AND HOLT-WINTERS FORECASTING METHODS ON EUROPEAN COVID-19 DATA. International Journal of 3D Printing Technologies and Digital Industry 6:3, pages 556-565.
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Md. Siddikur Rahman & Arman Hossain Chowdhury. (2022) A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers. PLOS ONE 17:9, pages e0273319.
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Md. Siddikur Rahman, Arman Hossain Chowdhury & Miftahuzzannat Amrin. (2022) Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh. PLOS Global Public Health 2:5, pages e0000495.
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Zhaohui Xia, Lei Qin, Zhen Ning & Xingyu Zhang. (2022) Deep learning time series prediction models in surveillance data of hepatitis incidence in China. PLOS ONE 17:4, pages e0265660.
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Siguo Li, Zhao Peng, Yan Zhou & Jinzhou Zhang. (2021) Time series analysis of foodborne diseases during 2012–2018 in Shenzhen, China. Journal of Consumer Protection and Food Safety 17:1, pages 83-91.
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Daren Zhao, Huiwu Zhang, Qing Cao, Zhiyi Wang, Sizhang He, Minghua Zhou & Ruihua Zhang. (2022) The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China. PLOS ONE 17:2, pages e0262734.
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Yongbin Wang, Chunjie Xu, Sanqiao Yao, Lei Wang, Yingzheng Zhao, Jingchao Ren & Yuchun Li. (2021) Estimating the COVID-19 prevalence and mortality using a novel data-driven hybrid model based on ensemble empirical mode decomposition. Scientific Reports 11:1.
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Yongbin Wang, Chunjie Xu, Jingchao Ren, Yuchun Li, Weidong Wu & Sanqiao Yao. (2020) Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China. Environmental Science and Pollution Research 28:6, pages 7281-7294.
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Yanling Zheng, Xueliang Zhang, Xijiang Wang, Kai Wang & Yan Cui. (2021) Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China. BMJ Open 11:1, pages e041040.
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Zhong-Qi Li, Hong-Qiu Pan, Qiao Liu, Huan Song & Jian-Ming Wang. (2020) Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infectious Diseases of Poverty 9:1.
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Mirxat Alim, Guo-Hua Ye, Peng Guan, De-Sheng Huang, Bao-Sen Zhou & Wei Wu. (2020) Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study. BMJ Open 10:12, pages e039676.
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Yongbin Wang, Chunjie Xu, Jingchao Ren, Yingzheng Zhao, Yuchun Li, Lei Wang & Sanqiao Yao. (2020) The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004–2018. Scientific Reports 10:1.
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Cong Yang, Yali Yang, Zhiwei Li & Yan Li. (2020) Analysis and Prediction of Pulmonary Tuberculosis Using an ARIMA Model in Shaanxi Province, China. Journal of Physics: Conference Series 1624:2, pages 022013.
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Yongbin Wang, Chunjie Xu, Weidong Wu, Jingchao Ren, Yuchun Li, Lihui Gui & Sanqiao Yao. (2020) Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019. Scientific Reports 10:1.
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Yongbin Wang, Chunjie Xu, Sanqiao Yao & Yingzheng Zhao. (2020) Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α -Sutte Indicator . Epidemiology and Infection 148.
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