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Disruption of winter influenza activity in Wuxi, China during and after the Covid-19 pandemic (2013–2025)

A counterfactual time-series analysis

Bibliographic Data

ID22067265
AuthorsMiao Wang (0000-0002-4102-8582, Nanjing Medical University), Yan Wang (0009-0009-9633-0023, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention), Yue Wang (0000-0001-8527-9175, Taiwan Centers for Disease Control), Chao Shi (0000-0002-3808-8699, Taiwan Centers for Disease Control), Gao Y (0000-0003-2059-3168, Taiwan Centers for Disease Control), Yumeng Gao (0009-0000-8171-8011, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention), Shuo Liu (0000-0001-7825-3006, Taiwan Centers for Disease Control), Yuan Shen (0009-0001-4787-9644, Taiwan Centers for Disease Control, corresponding author)
Year2026
Volume14
Pages1851342-1851342
Publication date2026-06-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1851342
PMID42395294
OpenAlexW7165151648
LanguageEN
References cited38

Background: The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) altered the circulation of respiratory viruses, and influenza activity declined worldwide during 2020-2022. However, the magnitude of these disruptions and their post-pandemic effects on epidemic dynamics remain insufficiently characterized. Methods: We analyzed weekly influenza surveillance data from 2013 to 2025 in Wuxi, eastern China. We constructed a composite influenza activity index using principal component analysis (PCA). We then trained a Bayesian structural time series (BSTS) model on pre-pandemic (2013-2019) values of this index to generate counterfactual estimates of influenza activity. We classified epidemic intensity using the Moving Epidemic Method (MEM) and quantified epidemic timing, including onset, end, and duration, using a modified Maximum Curvature Method (MCM) for both observed and counterfactual data. Results: Influenza activity was substantially suppressed during the COVID-19 pandemic (2020-2022), characterized by a near-complete interruption of transmission in the 2020/2021 season. During the subsequent post-pandemic period (2023-2025), influenza activity rebounded relative to counterfactual projections, although recovery trajectories varied across seasons. Overall, the trajectory of influenza activity was marked by a sharp decline during the pandemic, followed by a partial and heterogeneous recovery. MEM-based analysis indicated reduced epidemic intensity during the pandemic, while observed peak activity exceeded counterfactual estimates in some post-pandemic seasons. MCM-based analysis showed that peak timing remained broadly stable overall, although both delayed and advanced peaks were observed across seasons, particularly during the pandemic and transition period. Epidemics were generally shorter and ended earlier during the early pandemic, whereas post-pandemic seasons exhibited heterogeneous patterns in timing and duration with no consistent directional trends across seasons. Conclusions: Influenza activity was markedly suppressed during the COVID-19 pandemic, reflecting disrupted transmission dynamics. The subsequent rebound, accompanied by heterogeneous epidemic patterns, suggests alterations in influenza seasonal dynamics and highlights the need for adaptive surveillance and response strategies

Counterfactual thinking · Influenza A virus · Influenza pandemic · Outbreak · Pandemic · Psychological intervention · COVID-19 epidemiological studies · Influenza Virus Research Studies · Respiratory viral infections research

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