{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:55:31Z","timestamp":1781762131173,"version":"3.54.5"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) altered the circulation of respiratory viruses, and influenza activity declined worldwide during 2020\u20132022. However, the magnitude of these disruptions and their post-pandemic effects on epidemic dynamics remain insufficiently characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20132019) 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Influenza activity was substantially suppressed during the COVID-19 pandemic (2020\u20132022), characterized by a near-complete interruption of transmission in the 2020\/2021 season. During the subsequent post-pandemic period (2023\u20132025), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1851342","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:36:19Z","timestamp":1781760979000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Disruption of winter influenza activity in Wuxi, China during and after the COVID-19 pandemic (2013\u20132025): a counterfactual time-series analysis"],"prefix":"10.3389","volume":"14","author":[{"given":"Miao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Public Health, Nanjing Medical University","place":["Nanjing, 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuo","family":"Liu","sequence":"additional","affiliation":[{"name":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Public Health, Nanjing Medical University","place":["Nanjing, China"]},{"name":"The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention","place":["Wuxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(17)33293-2","article-title":"Estimates of global seasonal influenza-associated respiratory mortality: a 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