Chen Wei, Wang Huabin, Ten Xianlin, Fu Miao, Lin Meili, Xu Xiaoping, Ma Yongjun
Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, No. 365 Renmin East Road, Jinhua, Zhejiang, 321000, China.
Department of Clinical Laboratory, Affiliated Jinhua Hospital, Wenzhou Medical University, No. 267 Danxi East Road, Jinhua, Zhejiang, 321000, China.
BMC Infect Dis. 2025 Jul 18;25(1):925. doi: 10.1186/s12879-025-11279-6.
After experiencing the global COVID-19 pandemic, whether there have been new changes in the epidemiological characteristics of influenza has become a topic of great concern. This study aims to investigate the impact of implementation and lifting of COVID-19 control measures on influenza positivity among patients with acute respiratory infections (ARI) from 2018 to 2023.
The data were collected from January 2018 to December 2023 in two designated sentinel hospitals in Jinhua. We performed an interrupted time series analysis (ITSA) using a beta regression model and a generalized additive model (GAM), adopting a two-model cross-validation strategy to assess the effect of two major interventions on influenza positivity: the COVID-19 control measures implemented in early 2020 and lifted at the end of 2022. We also analyzed influenza epidemiological characteristics and seasonality before, during, and after the pandemic.
A total of 98,244 cases were included in this study, and the overall influenza positivity rate was 39.34%. Females and the 6-17-year age group had higher positivity rates. Before the pandemic, influenza primarily showed a winter peak pattern, whereas during the pandemic, the positivity rate declined significantly with no distinct peak. After the pandemic ended, an unusual dual-peak pattern emerged. The interrupted time series analysis revealed that, following the implementation of non-pharmaceutical interventions (NPIs) in early 2020, influenza positivity immediately decreased significantly in the beta regression model (β = -1.75, p = 0.003). After the lifting of measures in late 2022, a marginally lagged increasing trend was observed in the beta regression model (β = 0.14, p = 0.096) and a significant increasing trend was found in the GAM model (edf = 7.00, p < 0.001). Seasonal effects differed between the models: the beta regression model exhibited significant annual seasonal fluctuations (sin12 = 0.67, p < 0.001), while the GAM model did not exhibit a significant association independent of the time trend.
COVID-19 and its control measures substantially reduced influenza positivity rates; however, once these measures were lifted, influenza activity resurged, and its seasonal epidemic pattern changed. The intensity of influenza appeared to exceed pre-pandemic levels, underscoring the importance of NPIs in controlling respiratory infectious diseases. Strengthened surveillance and optimized strategies remain necessary to mitigate the threat of influenza in the post-pandemic era.
经历全球新冠疫情后，流感的流行病学特征是否出现新变化成为备受关注的话题。本研究旨在调查2018年至2023年新冠防控措施的实施与解除对急性呼吸道感染（ARI）患者流感阳性率的影响。
数据收集于2018年1月至2023年12月期间金华市的两家指定哨点医院。我们使用β回归模型和广义相加模型（GAM）进行中断时间序列分析（ITSA），采用双模型交叉验证策略评估两项主要干预措施对流感阳性率的影响：2020年初实施并于2022年底解除的新冠防控措施。我们还分析了疫情前、疫情期间和疫情后的流感流行病学特征及季节性。
本研究共纳入98244例病例，总体流感阳性率为39.34%。女性及6至17岁年龄组的阳性率较高。疫情前，流感主要呈现冬季高峰模式，而在疫情期间，阳性率显著下降且无明显高峰。疫情结束后，出现了异常的双峰模式。中断时间序列分析显示，2020年初实施非药物干预（NPIs）后，β回归模型中流感阳性率立即显著下降（β = -1.75，p = 0.003）。2022年底措施解除后，β回归模型中观察到略有滞后的上升趋势（β = 0.14，p = 0.096），GAM模型中发现显著的上升趋势（edf = 7.00，p < 0.001）。各模型的季节效应有所不同：β回归模型表现出显著的年度季节性波动（sin12 = 0.67，p < 0.001），而GAM模型未表现出独立于时间趋势的显著关联。
新冠疫情及其防控措施大幅降低了流感阳性率；然而，这些措施一旦解除，流感活动就会反弹，其季节性流行模式也会改变。流感的强度似乎超过了疫情前的水平，凸显了非药物干预在控制呼吸道传染病方面的重要性。在疫情后时代，加强监测和优化策略对于减轻流感威胁仍然必要。
