Wang YB, Qing SY, Liang ZY, Ma C, Bai YC, Xu CJ. Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China. World J Gastroenterol 2023; 29(42): 5716-5727 [PMID: 38075851 DOI: 10.3748/wjg.v29.i42.5716]
Corresponding Author of This Article
Yong-Bin Wang, MD, Researcher, Teacher, Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang 453003, Henan Province, China. 191035@xxmu.edu.cn
Research Domain of This Article
Public, Environmental & Occupational Health
Article-Type of This Article
Retrospective Study
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Wang YB, Qing SY, Liang ZY, Ma C, Bai YC, Xu CJ. Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China. World J Gastroenterol 2023; 29(42): 5716-5727 [PMID: 38075851 DOI: 10.3748/wjg.v29.i42.5716]
Yong-Bin Wang, Si-Yu Qing, Zi-Yue Liang, Chang Ma, Yi-Chun Bai, Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453003, Henan Province, China
Chun-Jie Xu, Beijing Key Laboratory of Antimicrobial Agents/Laboratory of Pharmacology, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100010, China
Author contributions: Wang YB conceived, initiated, and performed this work; Qing SY, Ma C, Liang ZY, Bai YC, and Xu CJ collected, analyzed, and interpreted the data for this study; Wang YB, Qing SY, Liang ZY, Bai YC, Xu CJ, and Ma C edited and improved this original manuscript; all authors reviewed and approved the manuscript.
Supported bythe Key Scientific Research Project of Universities in Henan Province, No. 21A330004; and Natural Science Foundation in Henan Province, No. 222300420265.
Institutional review board statement: This study was reviewed and approved by the institutional review board of Xinxiang Medical University (No: XYLL-2019072). All methods were carried out under relevant guidelines and regulations.
Informed consent statement: The need for informed consent was waived by the Ethics Committee of Xinxiang Medical University because the HB and HC cases were shared anonymously and we cannot access any identifying information of the patients (available from: https://www.phsciencedata.cn/Share/).
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: All the data supporting the findings of the work are contained within the study or technical appendix, statistical code, and dataset available from the corresponding author at 191035@xxmu.edu.cn.
Corresponding author: Yong-Bin Wang, MD, Researcher, Teacher, Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang 453003, Henan Province, China. 191035@xxmu.edu.cn
Received: August 5, 2023 Peer-review started: August 5, 2023 First decision: September 18, 2023 Revised: September 28, 2023 Accepted: October 23, 2023 Article in press: October 23, 2023 Published online: November 14, 2023 Processing time: 97 Days and 21.1 Hours
ARTICLE HIGHLIGHTS
Research background
Hepatitis B (HB) and hepatitis C (HC) have the largest burden in China, and a goal of eliminating them as a major public health threat by 2030 has been raised.
Research motivation
Accurate prediction helps to anticipate possible scenarios and make proactive choices, enabling policymakers to make informed decisions, plan strategies, and prepare for potential challenges and opportunities.
Research objectives
This study aimed to evaluate the usefulness of seasonal autoregressive fractionally integrated moving average (SARFIMA) in monitoring HB and HC epidemics (projection into 2030) in mainland China and to assess the forecasting potential of SARFIMA compared to seasonal autoregressive integrated moving average (SARIMA).
Research methods
The monthly incidence cases of HB and HC from January 2004 to June 2023 were obtained. Then, the two periods (from January 2004 to June 2022 and from January 2004 to December 2015, respectively) were used as the training sets to build the SARFIMA and SARIMA models, while the remaining periods served as the test sets to evaluate the forecasting accuracy of both models.
Research results
During the study period, a total of 23400874 HB cases and 3590867 HC cases were reported. In the 12-step-ahead HB, 90-step-ahead HB, 12-step-ahead HC, and 90-step-ahead HC forecasts, the best SARFIMA generated lower error rates compared with the best SARIMA. The predicted HB incidents totaled 9865400 [95% confidence interval (95%CI): 7508093-12222709] and HC totaled 1659485 (95%CI: 856681-2462290) during 2023-2030.
Research conclusions
The SARFIMA provides a more sophisticated and adaptable framework for capturing intricate patterns and interdependencies in monitoring HB and HC epidemics compared with the SARIMA. This ultimately leads to enhanced forecasting capabilities and a deeper comprehension of the underlying process.
Research perspectives
The integration of SARFIMA into public health decision-making for managing HB and HC epidemics can result in more informed and efficacious interventions.
