{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:32:13Z","timestamp":1783596733097,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":222,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transboundary and Emerging Diseases"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    The COVID\u201019 pandemic disrupted global influenza transmission. We aimed to elucidate how meteorological and air pollution drivers influenced seasonal influenza A subtypes and B lineage in Southern China pre\u2010, during\u2010, and postpandemic.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    We analyzed weekly influenza surveillance data from Southern China (2011\u20132024) and corresponding meteorological data. Using an interpretable machine learning framework combining XG\u2010Boost and SHapley Additive exPlanation (SHAP), we quantified the dynamic contributions of environmental factors to influenza subtype positivity rates. GAM models were employed to identify climate thresholds for influenza transmission.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Findings:<\/jats:bold>\n                    The pandemic reshaped influenza transmission and environmental dependencies in Southern China. Post\u2010pandemic circulation showed marked subtype\u2011specific shifts: A\/H3N2 winter positivity nearly tripled (12.670% vs. 4.861% pre\u2010pandemic,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009 &lt; 0.001), A\/H1N1 increased over four\u2011fold (4.325% vs. 0.935% pre\u2010pandemic,\n                    <jats:italic>p<\/jats:italic>\n                    = 0.064), while B\/Victoria circulation declined significantly (0.594% vs. 1.623% pre\u2010pandemic,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009 &lt; 0.001). Environmental driver hierarchies underwent notable temporal reorganization. For A\/H3N2, PM\n                    <jats:sub>10<\/jats:sub>\n                    influence surged from 9.18% to 30.37% during the pandemic before dropping to 3.95% post\u2010pandemic, while visibility emerged as the dominant driver (58.05% vs. 37.26% pre\u2010pandemic). A\/H1N1 showed peak humidity sensitivity during the pandemic (18.59%) that later diminished (3.78%), with transmission promotion occurring only below a threshold of \u226411.44\u2009g\/m\u00b3 absolute humidity. B\/Victoria maintained consistent sea\u2011level pressure sensitivity, peaking post\u2010pandemic (36.36%), with transmission optima at 8.58\u2009g\/m\u00b3. Seasonal modeling revealed subtype and phase\u2011specific environmental thresholds that shifted between \u201cpromotion\u201d and \u201cinhibition\u201d zones for humidity and visibility, indicating key alterations in climate\u2011influenza relationships across the pandemic transition.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Interpretation:<\/jats:bold>\n                    These findings show that pandemic\u2011era disruptions recalibrated the environmental architecture of influenza transmission. New environmental thresholds and driver hierarchies reveal shifts in viral\u2011climate relationships. The identification of distinct \u201cpromotion\u2011inhibition\u201d zones for each subtype establishes a new paradigm for climate\u2011pathogen interactions. These insights require immediate integration into surveillance frameworks and predictive models.\n                  <\/jats:p>","DOI":"10.1155\/tbed\/5581162","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T06:53:06Z","timestamp":1754981586000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Temporal Shifts in Climate\u2010Influenza Dynamics: A Multi\u2010Subtype Analysis in Southern China Spanning the COVID\u201019 Era"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7277-5226","authenticated-orcid":false,"given":"Lirong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9951-9593","authenticated-orcid":false,"given":"Jiajia","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3109-1098","authenticated-orcid":false,"given":"Jiaxiong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiao","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fancun","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8584-3335","authenticated-orcid":false,"given":"Suyang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaocheng","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6215-6850","authenticated-orcid":false,"given":"Zicheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40249-015-0077-6"},{"key":"e_1_2_12_2_2","unstructured":"World 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