The outcomes of viral infections typically correlate with viral load in host tissues. In this study, we identified a H3N2 strain A/Environment/Guangxi/44461/2019 (GX19) that induced rapid mortality in mice by 4 days post-infection despite exhibiting low pulmonary replication capacity. Pathological analysis revealed that GX19 at 106 TCID50 (GX19-6) caused more severe lung damage than GX19 at 105 TCID50 (GX19-5), while inducing pulmonary pathology comparable to a H3N8 virus A/Changsha/1000/2022 at 106 TCID50 (CS-6). Both GX19-6 and CS-6 triggered greater cardiac damage than GX19-5. Notably, GX19-6 displayed unique neurovirulence, eliciting significantly more severe brain damage than GX19-5 and CS-6, accompanied by evident cerebral haemorrhage. Gene Set Variation Analysis (GSVA) revealed distinct cardiac gene expression profiles among viral infections. Specifically, GX19-5 up-regulated gene sets associated with arrhythmia, whereas GX19-6 triggered pathways involved in cardiac arrest. Neither of these effects was present in CS-6 infection. In the brain, GX19-6 specifically induced stronger upregulation of cerebral venous thrombosis and acute ischaemic stroke gene sets compared to other groups, consistent with its pronounced neuropathology. Transcriptomic profiling demonstrated significant alterations across all three organs in GX19-6-infected mice, showing suppression of T-cell immunity in the lungs and brain alongside elevated systemic inflammation. In the heart, increased inflammation and apoptosis were accompanied by impaired energy metabolism and reduced cardiac function, potentially contributing to the observed hypoxic responses in the heart, lungs, and brain. Collectively, these findings reveal an inflammation-driven lung-heart-brain axis in influenza virus pathogenicity.

Influenza is a serious respiratory infection that imposes significant public health challenges. However, the precise impact of pollutants on influenza virus activity remains unclear. In this study, we aimed to investigate the effects of different air pollutants on the incidence of influenza-like illness (ILI), influenza A (Flu A), and influenza B (Flu B) in China based on nationwide air pollution and influenza data from 554 sentinel hospitals across 30 provinces and municipalities from 2014 to 2017. A Distributed Lag Nonlinear Model (DLNM) was employed to discern the lagged effects of six distinct air pollutants, namely PM2.5, PM10, O3, CO, SO2, and NO2, on the incidence of ILI, Flu A, and Flu B. Our analysis indicated that the relationship between air pollutants and influenza varied among ILI, Flu A, and Flu B, with Flu B being more sensitive to SO2 than Flu A. Elevated levels of air pollutants were generally associated with an increased risk of influenza; however, relative risks declined slightly at extreme concentrations of PM2.5, SO2, and NO2. These results highlight the complex associations between air pollution and influenza.

Traditional influenza surveillance suffers from 1-2 week reporting delays that compromise outbreak response. We analyzed 21.08 million digital prescription transactions from China's largest on-demand medication delivery platform across 31 provinces (2022-2024) and demonstrated that prescription data provide causally validated epidemic proxies. Digital prescriptions exhibited 2-week predictive lead time (convergent cross-mapping skills ΔρCCM = 0.339; p < 0.001; 28/31 provinces), substantially exceeding environmental predictors (ΔρCCM = 0.032-0.196) while matching online search indices (ΔρCCM = 0.349). Critically, prescriptions demonstrated bidirectional causal coupling with laboratory-confirmed influenza positivity (forward: 28/31; reverse: 22/31 provinces). This dynamical signature was absent in online search (reverse: 2/31) and environmental variables (reverse: 0-5/31), distinguishing validated disease signals from confounded correlates. Prescriptions also exhibited greater environmental sensitivity compared with laboratory surveillance (air pollutants: 14-26/31 versus 3-7/31 provinces). Leveraging this validated proxy, a spatiotemporal deep learning framework integrating GNN, Mamba, and LSTM achieved 96-day forecasting of daily prescription rates (mean absolute error (MAE) = 1.166; MAE < 3.0 in 29/31 provinces). Digital prescriptions thus enable both immediate epidemic detection (24 h data availability) and actionable long-range forecasting, providing an additional validated data stream for multi-source epidemic surveillance.

Accurate genome assembly from metagenomic sequencing data remains challenging, particularly in mixed infections involving multiple pathogens, due to data complexity and contaminant sequences. Here, we present GMW (Genomic Microbe-Wise), a novel computational tool that improves pathogen genome assembly accuracy and enhances contaminant removal capabilities by simplifying the post-assembly graph. GMW leverages community detection algorithms, sequence similarity analysis, and coverage patterns to resolve strain mixtures and improve assembly accuracy. Using datasets of influenza A virus subtypes, we demonstrate GMW’s ability to disentangle mixed infections and reconstruct complete viral genomes with high precision. Additionally, GMW outperforms traditional sequence similarity methods in classifying target contigs from contaminants. This tool also provides interactive visualization modules to streamline the inspection of assembly outputs, including simplified representations of complex assembly graphs. By enhancing assembly quality and contamination filtering, GMW emerges as a versatile solution for applications in clinical diagnostics, microbial ecology, and pathogen surveillance.

Background. Avian influenza virus (AIV) H9N2 has a major role in the emergence of influenza pandemic. We assessed the risk of AIV H9N2 to the human population and public health. Method. The hemagglutination inhibition method was used to screen for hemagglutinin antibodies. Microneutralization tests were performed to confirm neutralizing antibodies against the AIV H9N2 subtype. Real-time polymerase chain reaction was conducted to detect the H9 subtype in environmental samples. GraphPad Prism software was used for mapping, and STATA software was used for statistical analysis. Results. The nationwide seroprevalence among these populations was 0.76%. Seroprevalence was compared across regions, genders, and occupational exposure sites. The seroprevalence rates for males and females showed no significant difference. Significant differences were found across regions and occupational exposure environments (P < .05). The south and southwest regions had the highest seroprevalence rates at 1.58% and 1.38%, respectively. The highest seroprevalence was observed in individuals exposed to live poultry market (1.51%). Significant regional differences in H9 nucleic acid positive rates (NAPRs) were found (P < .05), with the southwest and central regions showing the highest rates at 25.99% and 24.35%, respectively. H9 NAPR in live poultry markets (LPMs), farms, and slaughterhouses varied significantly by region (P < .05). Conclusions. Poultry-related environments have become a key factor in AIV H9N2 infection among occupational populations. Exposure to LPM showed the highest seroprevalence among occupational groups. The distribution characteristics of H9N2 across different poultry environments increased the risk of infection in occupationally exposed populations.

Abstract Seasonal influenza viruses accumulate antigenic changes, eroding population immunity and necessitating recurrent vaccine updates. Hemagglutination inhibition (HI) assays are the standard for measuring antigenic relationships between circulating and vaccine strains; however, their limited throughput constrains the scale and timeliness of surveillance. Here, we present fluProfiler, a foundation-model-based framework that learns a stable mapping from viral sequences to antigenic space and uses this representation to support influenza antigenic prediction, vaccine strain evaluation, and diversity-driven sampling. fluAgPredictor aligns hemagglutinin (HA) and neuraminidase (NA) sequence representations with HI-derived antigenicity, enabling accurate and consistent inference of pairwise antigenic distances across surveillance-aligned evaluation settings. Without prior annotation of antigenic sites, it identifies key residues in immunodominant epitopes and reveals the cooperative contributions of HA and NA to antigenic variation. Building on this antigenic-space representation, fluVacSelector provides antigenic coverage scores that are concordant with World Health Organization (WHO) vaccine recommendations while also flagging potential candidates that may offer broader coverage ahead of formal consultations. fluAgEnhancer further leverages the same representation to prioritize antigenically informative and diverse strains for experimental characterization, achieving comparable predictive accuracy with approximately 25% fewer HI measurements than random sampling. Together, these modules provide a high-throughput and interpretable complement to HI testing, converting routine genomic surveillance into a more proactive, data-driven support system for antigenic monitoring and vaccine strain selection.

Antigenic drift in hemagglutinin (HA) enables influenza viruses to escape host immunity. Elucidating molecular features of antigenic drift is essential for updating seasonal vaccines and pandemic preparedness. Here, we found that influenza B viruses (IBVs) isolated after 2019 escaped neutralization by several previously identified broadly neutralizing monoclonal antibodies (bnAbs). Meanwhile, we identified two IBV bnAbs, CAV-CF22 and CAV-CH76, isolated via quadrivalent vaccine. They exhibited broad neutralizing activity against Victoria- and Yamagata-lineage viruses in vitro and protected in vivo against contemporary Victoria and Yamagata strains. Phylogenetic and structural analysis revealed fixation of K136E in post-2019 Victoria HA, disrupting epitopes targeted by most previously characterized head-directed IBV-monoclonal antibodies (mAbs). High-resolution structures reveal that, rather than engaging K136, CAV-CF22 and CAV-CH76 insert HCDR3 into the receptor-binding site (RBS) to sterically mimic sialic acid. The conservation of epitope residues underlies the antibodies’ broad neutralizing activity against IBV and informs antibody- and vaccine-design strategies that are resilient to recent IBV drift.

Influenza B virus (IBV) has circulated in the human population for a long time, yet the evolutionary mechanisms responsible for host adaptation remain poorly understood. Here we show that recent IBV strains exhibit an enhanced ability to evade the innate immune response and an increased replication efficiency compared with earlier strains. Our data indicate that the nonstructural protein 1 (NS1) of recent IBV strains interacts with TUFM and LC3B to induce mitophagy, leading to degradation of MAVS, suppression of interferon production and enhanced viral replication. In contrast, NS1 of earlier strains displays minimal ability to trigger mitophagy-mediated MAVS degradation. Sequence analyses show that, over the past two decades, IBV has acquired a phenylalanine (F)-to-leucine (L) substitution at residue 247 of NS1, altering its interaction with LC3B. A rescued recent IBV strain carrying the NS1-L247F mutation exhibits diminished NS1-LC3B binding, impaired mitophagy, and attenuated replication. Our study shows that adaptive evolution involving a single mutation in NS1 enables mitophagy-mediated innate immune evasion, contributing to IBV adaptation to the host.

BACKGROUND:The determinants of the species barrier preventing human infections with avian influenza A viruses (IAV) are incompletely understood. We previously identified loss-of-function variants of the interferon-regulated antiviral factor MxA as a genetic factor for increased susceptibility to infections with the H7N9 subtype. Given the central role of type I IFNs (IFN-I) in antiviral defence, we hypothesised that IFN-I-neutralising autoantibodies may similarly predispose to zoonotic H7N9 infection. METHODS:In this observational case-control study, serum samples collected between 2013 and 2017 from 199 Chinese patients with laboratory-confirmed H7N9 infection and 531 healthy, uninfected controls (269 poultry workers, 262 close contacts) were screened for IgG autoantibodies binding IFNα2, IFNβ1b, or IFNω using a multiplex bead-based assay. Positive samples were tested for IFN-neutralising activity in a luciferase-based reporter assay. To confirm their ability to block IFNα2-mediated antiviral activity, selected samples (n = 19) were analysed in IAV infection experiments. Associations between age, sex, H7N9 case status, case fatality, and the presence of neutralising autoantibodies were evaluated by logistic regression. Available whole-genome sequencing data from 26 individuals with neutralising autoantibodies were screened for variants in genes linked to IFN-I autoimmunity. FINDINGS:Neutralising autoantibodies against at least one IFN-I were detected in 19.1% (38/199) of patients but in only 1.1% (6/531) of controls, consistent with published general population data. Most patient sera targeted IFNα2 and/or IFNω (35/199), and 18.1% (36/199) neutralised even high IFN-I concentrations of 1-10 ng/ml. The presence of neutralising autoantibodies was associated with 8.2- to 25.3-fold higher odds of H7N9 infection (p < 0.0001), depending on antibody specificity and reference group. Autoantibody prevalence increased significantly with age in patients (44.8% ≥70 years; OR = 1.05; 95% CI 1.02-1.07; p = 0.0001), but was not associated with sex (OR for males vs. females = 0.52; 95% CI 0.23-1.14; p = 0.106). All selected sera containing neutralising autoantibodies blocked IFNα2-induced antiviral activity in cell culture. No known genetic predisposition for IFN-I autoimmunity was identified. INTERPRETATION:Our findings suggest that IFN-I-targeting autoimmunity is associated with susceptibility to zoonotic IAV infection with the H7N9 subtype, and possibly also other subtypes, including panzootic H5N1. Given the ease of implementation, screening for anti-IFN-I autoantibodies could be readily integrated into surveillance or targeted testing. This could be relevant in environments with increased exposure to zoonotic IAVs. FUNDING:Shenzhen Medical Research Fund, National Natural Science Foundation of China, Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences, Guangdong Provincial Science and Technology Program, Program for Youzuzhikeyan of Shenzhen University, German Research Foundation, Swiss National Science Foundation.

Avian influenza viruses (AIV) pose a major zoonotic threat with pandemic potential. Waterbirds facilitate AIV spillovers into farm animals and humans through exposure and virus reassortment. Here, we propose waterbird activity entropy (WAE), an indicator of waterbird activity intensity based on monthly distributions of 779 species worldwide. WAE demonstrated high explanative power (AUC = 0.87 ± 0.001) for global avian influenza cases, particularly for H5N1, revealing the potential of WAE for identifying AIV exposure hotspots which cover 14% of global land area. Notably, the AIV exposure hotspots in the USA, EU, China, and India contain 52% of the globally exposed human population, 41% cattle, and 51% poultry. Despite reporting <1% of global cases, sub-Saharan Africa contains >300 Mha of hotspots area (15% globally), highlighting considerable surveillance gaps. This WAE-based framework enhances AIV risk assessment by incorporating waterbird residency time, offering critical insights for anticipating AIV emergence and improving surveillance.

A 2024 human HPAI H5N1 case in British Columbia showed mixed viral populations containing HA-190D (28%) and HA-226H (35%) variants in tracheal aspirate sequencing. In this study, solid-phase binding assay and molecular docking were used to evaluate the contribution of HA-E190D/Q226H mutations to the viral receptor profiles. Our results showed that HA-E190D marginally reduced sialic acid α2,3 receptors' affinity, while HA-Q226H impaired both sialic acid α2,3 and α2,6 receptors' binding. These results demonstrate that neither mutation strengthens viral binding to human-type receptors, indicating that such substitutions are unlikely to heighten the public health threat posed by the virus for now.

Influenza remains a major global public health concern, and growing evidence suggests that air pollution may influence its incidence. However, most existing studies have relied on syndromic surveillance data, limiting the validity of their findings. This study investigates the association between short-term exposure to six ambient air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and laboratory-confirmed influenza cases in Shanghai from 2013 to 2017. Using a time-stratified case-crossover design and individual-level exposure estimates derived via inverse distance weighting, we evaluated multiple lag structures to characterize exposure-response relationships. Results indicated that elevated concentrations of PM2.5, PM10, SO2, NO2, and CO were significantly associated with increased influenza risk, while higher O3 levels were linked to a reduced risk. Specifically, a 10 µg/m3 increase in PM10 concentration was associated with an elevated influenza risk (OR = 1.019, 95

Background: China‘s influenza vaccination coverage remains at a low rate, with significant regional socioeconomic disparities, lacking targeted distribution strategies and achievable coverage targets. This study aims to provide scientific evidence for formulating differentiated and feasible vaccination strategies across Chinese provinces based on regional economic gradients. Methods: We employed the Susceptible-Vaccinated-Exposed-Asymptomatic-Infectious-Critical-Fatal-Recovered/Removed (SVEAICFR) model to simulate various vaccination strategies, analyzing the reduction in disease burden and vaccine dose requirements across underdeveloped, developing, and developed regions. The optimal strategy and achievable coverage targets were subsequently determined. Results: The 31 provinces were clustered into three categories based on economic levels, showing significant spatiotemporal differences in epidemics (Kruskal–Wallis test, all p < 0.001). Developed regions showed the earliest onset and highest peaks (influenza-like illness positive (ILI+) index ≈ 12–13, Baidu Influenza Search Index (BISI) ≈ 310,000). Developing regions exhibited moderate lagging by 1–2 weeks, while underdeveloped regions had the lowest peaks (ILI+ 3–4) and longer epidemic cycles. During the 2023–2024 influenza season, the national predicted vaccination rate was only 2.89% with marked regional disparities. Baseline incidence, severity, and mortality rates were 13,374.93, 49.52, and 8.37 cases per 100,000 population, respectively. Modeling indicates that increasing influenza vaccination coverage rates for populations aged <18 and ≥65 to a theoretical threshold (39.73% of the total population) before the season could reduce incidence, severity, and mortality rate by 99.26%,99.42%, and 99.46%, respectively. Conclusions: Influenza prevalence in China exhibits significant regional heterogeneity, necessitating differentiated measures based on regional economic gradients. Regional support mechanisms should be implemented to promote equitable vaccine distribution. Priority vaccination for high-risk populations (aged <18 and ≥65), to reach a 40% theoretical national coverage target, is recommended via realistic implementation pathways to minimize the disease burden of influenza.

With the advancement of industrialization and the acceleration of urbanization, air pollution has become a major environmental health issue worldwide. However, the subtype-specific associations between air pollution and influenza virus infection remains unclear. In this nationwide individual-level case-crossover study, conditional logistic regression models combined with distributed lag models were applied to quantify the associations between exposure to PM2.5, PM10, O3, CO, NO2, SO2 and influenza virus infection in China. From 2013 to 2017, we included 257,763 laboratory-confirmed influenza-positive cases that had residential address information at the street or township level or finer, of which 177,794 (68.98%) were infected by influenza A. Our results indicated that for each 10 μg/m³ increase in PM2.5, PM10, CO, NO₂, and SO₂, the cumulative 7-day (lag06) effects on influenza virus infection increased by 1.54% (95% CI: 1.33-1.76%), 1.05% (95% CI: 0.90-1.19%), 0.11% (95% CI: 0.10-0.13%), 4.68% (95% CI: 4.10-5.26%), and 4.34% (95% CI: 3.84-4.83%), whereas O3 showed a significant protective effect, with 10 μg/m³ increase, the cumulative influenza virus infection would decreased by 2.96% (95% CI: 2.60-3.32%). The associations between all six air pollutants and influenza A virus infection were stronger than those for influenza B virus infection. With per 10 μg/m³ increase in PM2.5 the cumulative 7-day risk of influenza A and B virus infection increased by 1.92% (95% CI: 1.66-2.18%) and 0.75% (95% CI: 0.37-1.13%), respectively. Given the rapid urbanization process in China, our findings support professionals in developing public health policies that balance socioeconomic development with the environmental burden of influenza.

Background: Healthcare workers (HCWs) are pivotal in influenza containment, serving as both high-risk individuals and vaccine advocates. However, influenza vaccination coverage among Chinese HCWs remains suboptimal. Existing research is often constrained by limited geographic representativeness or non-robust designs. This study provides a robust, nationwide assessment of influenza vaccine uptake and recommendation behaviors among HCWs in China. Methods: A multicenter cross-sectional survey was conducted in late 2025 across four Chinese provinces (Shanghai, Shandong, Chongqing, and Hubei). A total of 390 frontline HCWs-only those defined as directly engaged in influenza management and prevention-from 48 hospitals (primary, secondary, and tertiary levels) completed validated electronic questionnaires. A multinomial logistic regression model was employed to identify determinants of personal vaccine uptake behavior among HCWs. Results: Overall influenza knowledge was moderate, with notable gaps in recognizing typical symptoms (29.23%), southern China's peak season (31.03%), and optimal vaccination timing (55.38%). A striking "recommendation-uptake disparity" was observed: while 93.6% of HCWs recommended the vaccine to patients, only 22.3% received it annually themselves. A multinomial regression revealed that being a nurse (vs. doctor: OR = 3.11, 95% CI: 1.28-7.53) or female (vs. male: OR = 3.08, 95% CI: 1.28-7.44) was positively associated with annual vaccination, whereas clinical technicians (vs. doctors: OR = 0.18, 95% CI: 0.03-0.94) showed lower odds. Primary barriers to personal vaccination included inconvenience (49.5%), perceived high cost (16.2%), and efficacy concerns (19.5%). Conclusions: This study highlights a significant gap between high recommendation rates and low personal uptake among HCWs in China. The findings underscore the need for multifaceted interventions, including workplace-based reminder systems, free vaccination policies, and tailored education, to optimize coverage and strengthen the role of HCWs in national influenza prevention.

BCR-ABL1 kinase is a critical driver of chronic myeloid leukemia (CML) pathophysiology. The approval of allosteric inhibitor asciminib brings new hope for overcoming drug resistance caused by mutations in the ATP-binding site. To expand the chemical diversity of BCR-ABL1 kinase inhibitors with positive anti-tumor effect with asciminib, structure-based virtual screening and molecular dynamics simulations were employed to discover novel scaffolds. This approach led to the identification of a series of N-(2-acetamidobenzo[d]thiazol-6-yl)-2-phenoxyacetamide derivatives as new BCR-ABL1 inhibitors. The most potent compound, 10m, demonstrated inhibition of BCR-ABL-dependent signaling and showed an anti-tumor effect against K562 cells, with an IC50 value of 0.98 μM. Compound 10m displayed powerful synergistic anti-proliferation and pro-apoptotic effects when combined with asciminib, highlighting its potential as a promising lead for the development of potential BCR-ABL inhibitors.

Recent global influenza resurgences, escalating to pandemics, emphasize the urgency for effective vaccinations. Despite their efficacy, vaccines offer limited protection against A/H3N2 variants. Thus, elucidating the spatial patterns and underlying drivers of A/H3N2 seasonality is critical for its management. However, the mechanisms governing this seasonality are not fully understood. The study conducted a collaborative and interdisciplinary analysis of influenza A/H3N2 epidemiology in China from 2012 to 2018, utilizing national influenza surveillance data, viral gene sequence data, and meteorological information. We initially examined the spatiotemporal distribution of influenza A/H3N2 across different temperate zones in China. Subsequently, we employed Bayesian "SkyGrid" reconstruction analysis to gain insights into the population dynamics of the influenza A/H3N2 virus within China's temperature zones. Additionally, we utilized generalized additive models (GAM) to assess the influence of meteorological factors on the seasonal prevalence of influenza A/H3N2. Our analysis of China's national influenza data revealed distinct seasonal patterns for A/H3N2: winter epidemics prevailed in temperate zones, while summer and autumn outbreaks occurred in subtropical and tropical areas. The seasonality of influenza A/H3N2 across China's diverse climatic zones is shaped by the interplay of virus migration and meteorological factors. Virus migration introduced new variant populations during seasonal epidemics of influenza A/H3N2 to different temperature zones in China, thereby seeding subsequent seasonal outbreaks. Our findings also indicate that meteorological elements trigger influenza A/H3N2 activity following virus migration. Moreover, the spatial variations in influenza A/H3N2 seasonality in China can be attributed to specific temperature thresholds, approximately 1 °C and 24 °C. These thresholds could serve as potential indicators for A/H3N2 prevalence. This insight is invaluable for tailoring region-specific prevention and control strategies in China and other regions with similar environmental conditions.

The recent resurgence of highly pathogenic avian influenza H5N1 viruses in North America and Europe has heightened global concerns regarding potential influenza pandemics. Despite significant progress in the surveillance and prevention of emerging influenza viruses, effective tools for rapid and accurate risk assessment remain limited. Here, we present FluRisk, an innovative computational framework that integrates viral genomic data with artificial intelligence (AI) to enable rapid and comprehensive risk evaluation of emerging influenza strains. FluRisk incorporates a curated database of over 1,000 experimentally validated molecular markers linked to key viral phenotypes, including mammalian adaptation, mammalian virulence, mammalian transmission, human receptor-binding preference, and antiviral drug resistance. Leveraging these markers, we developed three state-of-the-art machine learning models to predict human adaptation, mammalian virulence, and human receptor-binding potential, all of which demonstrated superior performance compared to traditional approaches such as BLAST, prior models, and baseline classifiers. In addition, a reference-based method was implemented to provide preliminary estimates of human transmissibility and resistance to six commonly used antiviral drugs. To facilitate broad accessibility and practical application, we developed a user-friendly web server that integrates both the molecular marker atlas and predictive tools for influenza virus phenotyping (available at: #/). This computational platform offers a valuable resource for the timely risk assessment of emerging influenza viruses and supports global influenza surveillance efforts. ### Competing Interest Statement The authors have declared no competing interest. National Key Plan for Scientific Research and Development of China, 2022YFC2303802

The prevalence and transmission of avian influenza viruses (AIVs) in the live poultry market (LPM) is a serious public health concern. This study was to investigate the prevalence of different subtypes of avian influenza viruses in environment of LPM, and to analyze the differences and seasonality of the nucleic acid positive rate (NAPR) of A type, H5, H7, and H9 subtypes in feces, sewage, drinking water, breeding cages, and chopping boards. Feces, breeding cages swabs, drinking water, sewage and chopping boards swabs were collected from live poultry market during 2019–2023 from southern and northern China. Real-time PCR was used to screen for virus subtypes. Viruses were isolated, and deep sequencing was performed to obtain whole-genome sequences. Chi-square test was used for statistical analysis of categorical variable, GraphPad Prism software were used to construct graphs. A total of 64,599 environmental samples were collected from live poultry markets in the southern China and northern China between 2019 and 2023. The average NAPR of the A type was significantly higher in the samples collected from the southern China than in those collected from the northern China (P < 0.05). The NAPR of H5, H7, and H9 subtypes carried by the five types of environmental samples in the southern China were significantly different (P < 0.05), and a higher NAPR was detected in chopping boards (10.84

The COVID-19 pandemic caused an unprecedented disruption to the global circulation of influenza viruses. Among the most notable outcomes was the probable extinction of the B/Yamagata lineage of influenza B viruses, which has been rarely detected since March 2020. However, the underlying mechanism of the probable extinction is unknown. Here, we combine molecular, antigenic, and epidemiological data to explore the drivers of this phenomenon. Our analysis reveals that the probable extinction of B/Yamagata was driven by reduced transmission due to nonpharmaceutical interventions (NPIs) and a depleted susceptible population caused by conserved antigenicity and the 2017/2018 outbreak. Specifically, B/Yamagata exhibited slower antigenic evolution and alternating antigenic dominance compared to the co-circulating B/Victoria lineage, which was consistent with its weaker positive selection pressure. Simulation analysis suggests that B/Yamagata would maintain circulation if it underwent significant antigenic drift around the COVID-19 pandemic or if NPIs were not implemented. These findings provide a mechanistic explanation for the probable extinction of B/Yamagata and offer broader insights for controlling similar respiratory viruses.