Impact of COVID-19 control measures on influenza positivity among patients with acute respiratory infections, 2018–2023: an interrupted time series analysis - Wei Chen; Huabin Wang; Xianlin Ten; Miao Fu; Meili Lin; Xiaoping Xu; Yongjun Ma (BMC Infectious Diseases)
Abstract Background 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. Methods 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. Results 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. Conclusion 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.