Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model.

IF 3.5 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

BMC Public Health Pub Date : 2025-04-08 DOI:10.1186/s12889-025-22305-2

Xinxiao Li, Yanyan Li, Shushuo Xu, Penghao Wang, Meng Hu, Haibin Li, Yongbin Wang

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引用次数: 0

Abstract

Background: There may be evidence that COVID-19 affects illness patterns. This study aimed to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications and to estimate the epidemiological trends of HB in Henan.

Methods: The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges.

Results: The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% to -31%) from January to March 2020, by 24% (95% CI: -29% to -17%) from January to June 2020, by 15% (95% CI: -19% to -9.2%) from January to December 2020, by 11% (95% CI: -15% to -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% to -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% to -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 to 115,391).

Conclusions: During the COVID-19 pandemic, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

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基于贝叶斯结构时间序列模型的新型冠状病毒肺炎对河南省乙型肝炎的影响及流行趋势评价

背景：可能有证据表明COVID-19影响疾病模式。本研究旨在评估新冠肺炎疫情对河南省乙型肝炎（HB）病例报告下降的影响，并估计河南省HB的流行趋势。方法：基于2013年1月至2022年9月的HB月发病率，采用贝叶斯结构时间序列（BSTS）方法，探讨COVID-19与HB病例下降的因果关系。为了评估BSTS算法执行预测的效果，我们将观察结果分为不同的训练和测试范围。结果：河南省乙肝发病率总体呈周期性和季节性下降趋势。季节指数以9月和2月最小（0.91和0.93），3月最大（1.19）。由于COVID-19大流行，2020年1月至3月，每月平均报告的HB病例数下降了38%(95%可信区间[CI]: -44%至-31%)，2020年1月至6月下降了24% (95% CI: -29%至-17%)，2020年1月至12月下降了15% (95% CI: -19%至-9.2%)，2020年1月至2021年6月下降了11% (95% CI: -15%至-6.7%)，2020年1月至2021年12月下降了11% （95% CI: -15%至-7.3%）。从2020年1月到2022年9月，下降了12% （95% CI: -16%至-8.1%）。从2021年到2022年，COVID-19对HB的影响减弱。在训练集和测试集上，BSTS模型产生的平均绝对百分比误差（10.03%）小于ARIMA模型产生的平均绝对百分比误差（14.4%）。BSTS模型产生的平均绝对误差、均方根误差和均方根百分比误差均小于ARIMA模型。BSTS模型预测的2022年10月至2023年12月河南省HB病例趋势保持稳定，总病例数为81,650例（95% CI: 47,372 ~ 115,391）。结论：新冠肺炎大流行期间，河南省HB发病率呈下降趋势，具有明显的季节性和周期性趋势。BSTS模型预测河南省乙肝发病率趋势优于ARIMA模型。这些信息可作为参考，并为制定预防和控制乙肝的战略和行动提供技术援助。采取额外措施加快消除乙肝的进程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Public Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

6.50

自引率

4.40%

发文量

2108

审稿时长

1 months

期刊介绍： BMC Public Health is an open access, peer-reviewed journal that considers articles on the epidemiology of disease and the understanding of all aspects of public health. The journal has a special focus on the social determinants of health, the environmental, behavioral, and occupational correlates of health and disease, and the impact of health policies, practices and interventions on the community.