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Infection and Drug Resistance Volume 13, 2020 - Issue
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Original Research

An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

Yongbin Wang1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China
,
Chunjie Xu2 Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People’s Republic of China
,
Yuchun Li1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China
,
Weidong Wu1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0002-7334-8679
ORCID Icon,
Lihui Gui1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China
,
Jingchao Ren1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China
&
Sanqiao Yao1 Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of ChinaCorrespondence[email protected] [email protected]
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Pages 867-880 | Published online: 24 Mar 2020

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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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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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Articles from other publishers (8)

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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Veerasak Punyapornwithaya, Pradeep Mishra, Chalutwan Sansamur, Dirk Pfeiffer, Orapun Arjkumpa, Rotchana Prakotcheo, Thanis Damrongwatanapokin & Katechan Jampachaisri. (2022) Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010–2020. Viruses 14:7, pages 1367.
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Enbin Yang, Hao Zhang, Xinsheng Guo, Zinan Zang, Zhen Liu & Yuanning Liu. (2022) A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China. BMC Infectious Diseases 22:1.
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Alla Ahmad Hassan & Tarik A Rashid. (2021) A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients. Kurdistan Journal of Applied Research, pages 44-63.
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Veerasak Punyapornwithaya, Katechan Jampachaisri, Kunnanut Klaharn & Chalutwan Sansamur. (2021) Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models. Frontiers in Veterinary Science 8.
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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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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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S Dhamodharavadhani, R Rathipriya & Jyotir Moy Chatterjee. (2020) COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models. Frontiers in Public Health 8.
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