tetano
Editor, Senior Moderator
Transbound Emerg Dis
. 2025 Aug 11:2025:5581162.
doi: 10.1155/tbed/5581162. eCollection 2025. Temporal Shifts in Climate-Influenza Dynamics: A Multi-Subtype Analysis in Southern China Spanning the COVID-19 Era
Lirong Zhang[SUP] 1 [/SUP], Jiajia Lin[SUP] 1 [/SUP], Jiaxiong Zheng[SUP] 1 [/SUP], Yixiao Niu[SUP] 1 [/SUP], Fancun Meng[SUP] 1 [/SUP], Suyang Liu[SUP] 1 [/SUP], Qiaocheng Chang[SUP] 1 [/SUP], Zicheng Cao[SUP] 1 2 3 4 [/SUP]
Affiliations
Background: The COVID-19 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-, during-, and postpandemic. Methods: We analyzed weekly influenza surveillance data from Southern China (2011-2024) and corresponding meteorological data. Using an interpretable machine learning framework combining XG-Boost 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. Findings: The pandemic reshaped influenza transmission and environmental dependencies in Southern China. Post-pandemic circulation showed marked subtype‑specific shifts: A/H3N2 winter positivity nearly tripled (12.670% vs. 4.861% pre-pandemic, p < 0.001), A/H1N1 increased over four‑fold (4.325% vs. 0.935% pre-pandemic, p = 0.064), while B/Victoria circulation declined significantly (0.594% vs. 1.623% pre-pandemic, p < 0.001). Environmental driver hierarchies underwent notable temporal reorganization. For A/H3N2, PM[SUB]10[/SUB] influence surged from 9.18% to 30.37% during the pandemic before dropping to 3.95% post-pandemic, while visibility emerged as the dominant driver (58.05% vs. 37.26% pre-pandemic). 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 ≤11.44 g/m³ absolute humidity. B/Victoria maintained consistent sea‑level pressure sensitivity, peaking post-pandemic (36.36%), with transmission optima at 8.58 g/m³. Seasonal modeling revealed subtype and phase‑specific environmental thresholds that shifted between "promotion" and "inhibition" zones for humidity and visibility, indicating key alterations in climate‑influenza relationships across the pandemic transition. Interpretation: These findings show that pandemic‑era disruptions recalibrated the environmental architecture of influenza transmission. New environmental thresholds and driver hierarchies reveal shifts in viral‑climate relationships. The identification of distinct "promotion‑inhibition" zones for each subtype establishes a new paradigm for climate‑pathogen interactions. These insights require immediate integration into surveillance frameworks and predictive models.
Keywords: COVID-19 pandemic; Southern China; climate-influenza interactions; temporal dynamics.
. 2025 Aug 11:2025:5581162.
doi: 10.1155/tbed/5581162. eCollection 2025. Temporal Shifts in Climate-Influenza Dynamics: A Multi-Subtype Analysis in Southern China Spanning the COVID-19 Era
Lirong Zhang[SUP] 1 [/SUP], Jiajia Lin[SUP] 1 [/SUP], Jiaxiong Zheng[SUP] 1 [/SUP], Yixiao Niu[SUP] 1 [/SUP], Fancun Meng[SUP] 1 [/SUP], Suyang Liu[SUP] 1 [/SUP], Qiaocheng Chang[SUP] 1 [/SUP], Zicheng Cao[SUP] 1 2 3 4 [/SUP]
Affiliations
- PMID: 40832123
- PMCID: PMC12360877
- DOI: 10.1155/tbed/5581162
Background: The COVID-19 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-, during-, and postpandemic. Methods: We analyzed weekly influenza surveillance data from Southern China (2011-2024) and corresponding meteorological data. Using an interpretable machine learning framework combining XG-Boost 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. Findings: The pandemic reshaped influenza transmission and environmental dependencies in Southern China. Post-pandemic circulation showed marked subtype‑specific shifts: A/H3N2 winter positivity nearly tripled (12.670% vs. 4.861% pre-pandemic, p < 0.001), A/H1N1 increased over four‑fold (4.325% vs. 0.935% pre-pandemic, p = 0.064), while B/Victoria circulation declined significantly (0.594% vs. 1.623% pre-pandemic, p < 0.001). Environmental driver hierarchies underwent notable temporal reorganization. For A/H3N2, PM[SUB]10[/SUB] influence surged from 9.18% to 30.37% during the pandemic before dropping to 3.95% post-pandemic, while visibility emerged as the dominant driver (58.05% vs. 37.26% pre-pandemic). 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 ≤11.44 g/m³ absolute humidity. B/Victoria maintained consistent sea‑level pressure sensitivity, peaking post-pandemic (36.36%), with transmission optima at 8.58 g/m³. Seasonal modeling revealed subtype and phase‑specific environmental thresholds that shifted between "promotion" and "inhibition" zones for humidity and visibility, indicating key alterations in climate‑influenza relationships across the pandemic transition. Interpretation: These findings show that pandemic‑era disruptions recalibrated the environmental architecture of influenza transmission. New environmental thresholds and driver hierarchies reveal shifts in viral‑climate relationships. The identification of distinct "promotion‑inhibition" zones for each subtype establishes a new paradigm for climate‑pathogen interactions. These insights require immediate integration into surveillance frameworks and predictive models.
Keywords: COVID-19 pandemic; Southern China; climate-influenza interactions; temporal dynamics.