Global Patterns Research Articles

Global patterns of gender disparities in STEM and explanations for their persistence

Global patterns of gender disparities in STEM and explanations for their persistence

A Study on Alzhemier Disease in Takum, Taraba State, Nigeria: An ARIMA Model Approach

Alzheimer’s disease, a progressive neurodegenerative disorder, represents a significant and growing public health burden, particularly as life expectancy increases worldwide. In sub-Saharan Africa, including Nigeria, the disease's prevalence is rising due to aging populations and urbanized lifestyles that elevate risk factors for cognitive decline. This study investigates the trends and projected incidence of Alzheimer’s disease in Takum, Taraba State, Nigeria. The research utilizes patient records from General Hospital Takum, spanning from 2012 to 2021. Following diagnostic tests and data transformations, the ARIMA(1,2,0) model was selected as the best fit for predicting future case counts. The findings reveal a steady increase in Alzheimer’s cases, consistent with global patterns, highlighting the need for proactive measures in healthcare planning. The study thus recommends the need for increased public awareness, investment in diagnostic infrastructure, and support systems for caregivers.

Summer High-Altitude Diurnal Rainfall Change in the Three Rivers Source Region and Associated Mechanism

Abstract The Tibetan Plateau (TP), a crucial area influencing global climatic patterns and water resources, is experiencing a unique climatic paradox, particularly evident in the Three Rivers Source Region (TRSR). A striking summer asymmetry in the increases of near-surface temperature and precipitation is observed from 1989 to 2018: the rate of daily minimum temperature (0.64°C decade−1) surpasses the daily maximum temperature (0.52°C decade−1), while the daytime precipitation intensity (0.40 mm day−1 decade−1) increases at a faster rate compared to nighttime (0.30 mm day−1 decade−1). Despite these trends, the summer mean nighttime precipitation intensity consistently remains higher than the daytime average. Notably, this pattern is accompanied by an increasing trend of moisture transport during both daytime and nighttime in TRSR. This paper deciphers the thermodynamic and dynamic processes behind this trend. The daytime warmth not only alters the stability of atmosphere but also modulates convective inhibition (CIN), thereby reshaping precipitation mechanics and potentially dampening or delaying daytime convection. Thermodynamically, a shift from unstable to stable anomalies in the summer troposphere suppresses precipitation development. The combination of increased CIN during those period leads to fewer but more intense rainy days. Dynamically, the shift from a consistent downward motion anomaly throughout troposphere to an upward motion anomaly becomes dominant during nighttime, exhibiting a similar transition but only below 500 hPa during daytime. These findings reveal the complex interplay between thermodynamics, dynamics, and precipitation, highlighting the need for refined climatic models that can accurately simulate these summer diurnal processes.

Time-dependent changes in genome-wide gene expression and post-transcriptional regulation across the post death process in silkworm.

Despite death marking the end of life, several gene expression and miRNA-mediated post-transcriptional regulation events may persist or be initiated. The silkworm (Bombyx mori) is a valuable model for exploring life processes, including death. In this study, we combined transcriptomics and miRNAomics analyses of young, old, and postmortem silkworms across the entire process after death to unravel the dynamics of gene expression and miRNA-mediated post-transcriptional regulation. In total, 171 genes exhibited sustained differential expression in postmortem silkworms compared to the pre-death state, which are primarily involved in nerve signaling, transport, and immune response. Postmortem time-specific genes were associated with cell cycle regulation, thermogenesis, immunity, and zinc ion homeostasis. We found that the down-regulated expression of 36 genes related to transcription, epigenetic modification, and homeostasis resulted in a significant shift in global gene expression patterns at 2h post-death. We also identified five mRNA-miRNA pairs (i.e., bmo-miR-2795-mhca, 2784-achi, 2762-oa1, 277-5p-creb, and 1000-tcb1) associated with stress hormone regulation, transcription activity, and signal transduction. The roles of these pairs were validated through in vivo experiments using miRNA mimics in silkworms. The findings provide valuable insights into the intricate mechanisms underlying the transcriptional and miRNA-mediated post-transcriptional regulation events in animals after death.

A scoping review of global patterns in reporting race, ethnicity, nationality, or religion in palliative care randomized controlled trials: Recommendations for transparency.

Though randomized controlled trials of non-pharmacological palliative care interventions have shown positive outcomes, findings are often generalized with limited consideration for the impact of the influence of race, ethnicity, nationality, or religion on said outcomes. To identify trends and gaps in global reporting of racial, ethnic, nationality, and religious demographics in non-pharmacological palliative care randomized controlled trials. We conducted a scoping review guided by the Joanna Briggs methodology. Global randomized controlled trials published in English, between 1999 and 2021 extracted from databases: Cochrane, PubMed, and Scopus. Inclusion criteria were non-pharmacological palliative care interventions for people with serious conditions reporting on one or more of the demographics of race, ethnicity, nationality, or religion. Our review included 131 (44%) articles published from 19 countries, predominantly the USA (n = 84, 64%). Most studies focused on cancer (n = 71, 54%) in inpatient settings (n = 85, 64%). Race was the most commonly reported demographic (n = 93, 70%), followed by ethnicity (n = 54, 41%), religion (n = 46, 35%), nationality (n = 20, 15%). Within racial reporting, 86 (92%) indicating a majority White/Caucasian sample. Only 14 (10%) articles provided context connecting participant demographics to intervention outcomes. Demographic data was discussed only as a limitation to generalizability in 18 cases (41%). Improving greater transparency in study reporting of social and historical context about population demographics, including specific demographic data collected, may better identify unmet palliative needs, facilitate cross-cultural interpretation, and improve adaptation and implementation of non-pharmacological palliative care interventions.

Identification of apelin/APJ signaling dysregulation in a human iPSC-derived granulosa cell model of Turner syndrome.

The interaction between germ cells and somatic cells in the ovaries plays a crucial role in establishing the follicle reserve in mammals. Turner syndrome (TS) predominantly affects females who have a partial or complete loss of one X chromosome. Our understanding of the role that granulosa cells (GCs) play in TS disease progression and pathogenesis remains limited. In this study, we achieved GC differentiation efficiency of up to 80% from iPSCs. When attempting to replicate the differentiation process of embryonic granulosa cells, we observed the downregulation of specific genes-GATA4, FOXL2, AMHR2, CYP19A1, and FSH-in Turner syndrome-derived granulosa cells (TS-GCs). Additionally, we identified dysregulation of the cell cycle in TS-GCs. To uncover the endogenous defects in TS-GCs, we compared global transcriptome patterns between iPSC-derived granulosa cells from healthy individuals and those with Turner syndrome. The apelin/APJ pathway exhibited differential signaling between the healthy and TS groups. Supplementation with apelin ligands and activation of apelin/APJ downstream signaling via Akt/PKB restored cell cycle progression and marker gene expression. We hypothesize that during early embryonic development, failures in apelin/APJ signaling in GCs of Turner syndrome patients lead to abnormalities in ovarian development, ultimately resulting in early oocyte loss and infertility.

Global prevalence and pattern of injuries in basketball players: a systematic review.

Basketball, as a widely popular sport, can be associated with a high risk of injury. This study aimed to provide a comprehensive overview of basketball-related injuries in various groups through the utilization of a systematic review methodology. An extensive search was conducted in the four valid databases using three groups of keywords up to November 2023 based on the PRISMA guidance. Two reviewers independently performed the steps of the study, including study selection, data extraction, and quality assessment. In case of any disagreements, the opinion of a third researcher was sought to achieve agreement in each step. The agreement between the reviewers was examined by computing Cohen's kappa coefficient. The quality assessment of the articles in this study was conducted using the Joanna Briggs Institute (JBI) critical appraisal tools. The prevalence range of injuries in these people was between 0.38% and 92.0%. Ankle injuries in 12 out of 28 studies (42.86%), knee injuries in 7 out of 28 studies (25.00%), head and tooth injuries in 3 out of 28 studies (10.71%), and shoulder and hand injuries in three out of 28 studies (10.71%) had the highest prevalence among various injuries. Five out of six studies (83.33%) showed a higher percentage of the injuries in males. Only a study compared the prevalence of injuries between people with various ages. These findings emphasize the need for injury prevention and management strategies, particularly targeting vulnerable areas like the ankle and knee.

Exploratory dietary patterns, the global diet quality score, and their associated socio-demographic factors among young adults in Rwanda: a cross-sectional study using a food list-validated, semi-quantitative food frequency questionnaire.

Economic growth in Rwanda is associated with significant changes in food systems, access to health and other services, lifestyle, and nutritional transitions. Nevertheless, our knowledge of dietary patterns in Rwanda remains limited. The present study aimed to identify the dietary habits of young adult population in Rwanda and to assess associated factors. A developed and validated semi-quantitative food frequency questionnaire covering a one-year period was used to collect data on food intake of 1,218 participants (18-35 years old) from end of January to April 2023 in a cross-sectional study. Dietary habits were assessed using two indicators: the Global Diet Quality Score (GDQS) and dietary patterns. The latest was developed using exploratory factor analysis. Rwandan adults had a mean GDQS of 24.1; 64.4% had high GDQS, especially urban, and educated respondents. The Southern province led at 77.4%. Three dietary patterns were identified: "Modern" (high in processed foods and drinks), "Traditional" (rich in cereals, roots, and plant-based proteins), and "low variety" (low in diverse foods but high in sugar and salt). Dietary patterns significantly varied by residency, province, sex, age, social category, asset, and education level. This study identified distinct dietary patterns among adult population of Rwanda, suggesting a nutritional transition associated with urbanization. The findings highlight the need for further research into the relationships between diet, obesity, and metabolic syndrome in Rwandan population.

Burden of Parkinson's disease in Central Asia from 1990 to 2021: findings from the Global Burden of Disease study.

Central Asia is known to face various ecological challenges that constitutes major risk factors for Parkinson's disease (PD). This study examines the burden of PD in Central Asia, a region where data on neurological disorders is notably sparse. Building on the latest Global Burden of Disease Study (GBD 2021), this study investigates the Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs) associated with PD in Central Asia and its countries from 1990 to 2021. The authors calculated average annual percent change (AAPC) to analyze trends, and compared individual country estimates to global figures. Additionally, incorporating data from the World Bank, both Bayesian hierarchical and non-hierarchical frequentist regression models were employed to assess their impact on DALYs. The DALYs varied across the study period, primarily driven by YLLs. While YLLs showed a uniform trend, YLDs were mostly incremental. Kazakhstan had the highest estimates across all metrics and was the only country aligned with global patterns. Age- and sex-specific estimates revealed substantial variations, with notably high figures found in male subjects from Tajikistan. The YLLs, YLDs, and DALYs for Kazakhstan, Uzbekistan, and Turkmenistan saw a significant increase in AAPCs. In contrast, Kyrgyzstan and Tajikistan saw declines, likely attributable to civic conflict and inter-country differences in population structure. Further comparison of DALY trends revealed significant deviations for all countries from the global pattern. This study showed an overall increase in PD burden from 1990 to 2021. These findings underscore the need for targeted strategies to reduce PD burden, with a particular focus on Kazakhstan. Integrating historical information is crucial for discussing the plausible mechanisms in studies sourced from the GBD.

ENSO predictive analytics based on layered rendering

AbstractEl Niño-Southern Oscillation (ENSO) is a periodic climate phenomenon in the equatorial Pacific that significantly influences global climate patterns. Accurate prediction and monitoring of ENSO events are essential for meteorological agencies and governmental institutions. This study introduces a content-guided attention module within a convolutional neural network to improve prediction accuracy. This module models inter-channel relationships and enhances information interaction by integrating channel and spatial attention weights. These advancements substantially improve prediction accuracy and help overcome the spring prediction barrier in ENSO forecasting. The research emphasizes global feature modeling and proposes a novel content-guided ENSO prediction model. It also includes an ocean data generation model utilizing global attention. Furthermore, a layered rendering technique is employed to invert ocean data, facilitating detailed analysis and contributing to the development of an ocean synthetic dataset.

Crowd-sourced trait data can be used to delimit global biomes

Abstract. Terrestrial biomes and their biogeographic patterns have been derived from a large variety of variables including species distributions and bioclimate or remote sensing products. However, classifying the biosphere into biomes from a functional perspective using biophysical traits has rarely been tested. Such a trait-based biome classification has been limited by data availability. Here, we aimed to exploit crowd-sourced plant observations and trait databases to systematically assess which traits are most suitable for biome classification. We derived global patterns of 33 biophysical traits covering around 50 % of the land surface by combining crowd-sourced species distribution data from the Global Biodiversity Information Facility (GBIF) and trait observations from the TRY database. Using these trait maps as predictors for supervised cluster analyses, we tested to what extent we can reconstruct 31 published biome maps. A sensitivity analysis with randomly sampled combinations of traits was performed to identify the traits that are most appropriate for biome classification. Performance was quantified by comparing modeled biome maps and the respective observation-based biome maps. Finally, spatial gaps in the resulting biome maps were filled using species distribution models to obtain continuous global biome maps. We showed that traits can be used for biome classification and that the most appropriate traits are conduit density; rooting depth; height; and different leaf traits, including specific leaf area and leaf nitrogen content. The best performance of the biome classification was obtained for biome maps based on biogeographic zonation and species distributions, in contrast to biome maps derived from optical reflectance. The availability of crowd-sourced plant observations is heterogeneous, and, despite its exponential growth, large data gaps are prevalent. Nonetheless, it was possible to derive biome classification schemes from these data to predict global biome patterns with good agreement. Therefore, our analysis is a valuable approach towards understanding biome patterns based on biophysical traits and associated ecological strategies.

An Improved Machine Learning Framework Considering Spatiotemporal Heterogeneity for Analyzing the Relationship Between Subway Station-Level Passenger Flow Resilience and Land Use-Related Built Environment

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

The Globalization of International Migration? A Conceptual and Data‐Driven Synthesis

AbstractAlthough the globalization of international migration is commonly accepted as a general tendency in contemporary migration patterns (de Haas, Castles, and Miller 2020, 9), the corresponding body of empirical evidence is mixed and fragmented. Our review of global migration patterns over the past half‐century highlights how the theories, expectations, and ultimately findings may vary depending on the specific definitions, vantage points, and measures being used. In this paper, we provide a simpler and integrated account of the globalization of international migration that includes a corresponding empirical template to quantify the relative importance of two processes at work: the intensity and connectivity of international migration. Using recent estimates of country‐to‐country migration flows every five years from 1990–1995 to 2015–2020, our analysis using demographic decomposition and group‐based multitrajectory modeling highlights the dynamic relationship between intensity and connectivity from both the global and country vantage points. Our work in this paper provides a starting point in the form of a much‐needed empirical template, one that is also highly flexible and customizable, for future research on the globalization of international migration to coalesce around and use going forward.

Geospatial Analysis of Climate Change Impacts on Vegetation Dynamics in Owerri West, Imo State

Climate change is a global phenomenon with profound effects on the environment, economies, and societies. It refers to significant changes in global temperatures and weather patterns over time. This study explores the impact of climate change on vegetation in Owerri West, Imo State, Nigeria, using geospatial techniques over a 20-year period (2003–2023). By analyzing satellite imagery and Normalized Difference Vegetation Index (NDVI) values alongside key climatic variables such as precipitation and land surface temperature (LST), the research seeks to quantify the extent of vegetation changes due to climate variability. The study uses Landsat 8 satellite data to evaluate spatiotemporal trends in vegetation health. NDVI values in 2003 ranged from a high of 0.45, indicating healthy and dense vegetation, to a low of -0.04, representing barren areas. By 2013, NDVI values had drastically declined, with a maximum of 0.12 and a minimum of -0.33, despite an increase in precipitation from 3300 mm (2003) to 3900 mm (2013). This decline suggests that other factors, such as extreme weather events or urbanization, contributed to vegetation stress. In 2023, NDVI values showed partial recovery, with a maximum of 0.28 and a minimum of 0.05, although precipitation levels dropped to a range of 600 mm to 540 mm. The broader temperature range in 2023, with a maximum of 32°C and a minimum of 17°C, likely reduced heat stress, allowing for vegetation recovery, though still below the 2003 levels, The findings highlight the complex interplay between climatic variables and vegetation health, where precipitation and temperature changes significantly influence vegetation dynamics. However, the NDVI decline in 2013, despite high precipitation, suggests that anthropogenic factors like urbanization and land use changes also play a critical role. This research emphasizes the importance of integrating remote sensing and GIS for monitoring vegetation responses to climate change. The study calls for sustainable land management practices and climate adaptation strategies to enhance vegetation resilience in the region.

Emergent Dynamic Formation through Optical Interactions in a Robot Swarm

Self‐organized formation is a key direction in swarm robotics. It is still challenging to design local interactions toward desired global formations and even more challenging for dynamic formations in a physical robot swarm system. Herein, a self‐organized method for emergent dynamic circling formation in a robot swarm through optical interactions is proposed. First, this method is quantitatively modeled based on the geometrical relations among robots. This model is further adjusted according to the characteristics of the robot swarm system. To demonstrate the effectiveness of this model, the effects of three key parameters of this model are tested on the size and disorder level of the emergent dynamic circling formation. The experimental results are consistent with the model predictions. Overall, a robot swarm system, in the physical environment, is quantitatively controlled to emerge a dynamic circling formation in this article. This work advances the swarm robotics for quantitatively designing local interactions among robots to reliably emerge dynamic global patterns.

Is It Time to Start Worrying? A Comprehensive Report on the Three-Year Prevalence of ESBL-Producing Bacteria and Their Trends in Antibiotic Resistance from the Largest University Hospital in Slovakia

Background/Objectives: Over the past few decades, extended-spectrum β-lactamase (ESBL)-producing bacteria have become a great concern in healthcare systems worldwide, imposing large burdens by increasing antimicrobial resistance and patient morbidity. Given the high mortality rates and emergence of multidrug-resistant (MDR) strains, monitoring ESBL prevalence and resistance patterns is crucial. This study aimed to evaluate ESBL-producing Escherichia coli, Proteus mirabilis, and Klebsiella pneumoniae over three years, focusing on phenotypic distribution and resistance profiles. Methods: A total of 1599 ESBL-producing bacterial samples were collected and analysed. A panel of 20 antibiotics was tested to determine resistance traits. Data were recorded on phenotypical distribution, isolation types, changes in antibiotic resistance, and the relation of such changes to antibiotic consumption (defined daily dose) from clinical isolates. Results: Phenotypical analysis revealed the minimal presence of the Cefotaximase from Munich (CTX-M) phenotype in E. coli and K. pneumoniae, creating a distinct epidemiological profile compared to global patterns. Shifts in isolation trends, particularly in P. mirabilis, suggest an expected increase in associated-mortality-rate in the coming years. While resistance trends were not statistically significant, MDR and extensively drug-resistant (XDR) strains were identified across all three bacteria. Only meropenem showed consistent 100% efficacy against E. coli, with other antibiotics displaying only partial effectiveness. Conclusions: These findings highlight the need for ongoing surveillance of ESBL-producing bacteria and underscore challenges in managing antibiotic resistance due to limited efficacy of last-resort treatments. The unique phenotypical distribution observed could impact local resistance management strategies in hospital settings in the coming years.

A preregistered meta-meta-analysis on the global distribution of Hepatotropic Viruses.

Hepatotropic viruses (HAV, HBV, HCV, HDV, and HEV) significantly impact global health, with varying prevalence across regions. This study aims to systematically consolidate data from diverse meta-analyses to provide a contemporary reference on virus distribution and prevalence. Adhering to PRISMA guidelines, the study utilized a mixed effects model for data integration. Quality evaluation was carried out with QUOROM and AMSTAR tools, with heterogeneity assessed via the Higgins I2 statistic, Q-statistic and Tau squared (τ2) values. The study analyzed 86 meta-analyses from 56 studies (2017-2022) with minimal overlap. Prevalence rates by region were as follows: MENA - 29.2%, Afghanistan - 9.14%, Africa - 8.10%. Prevalence rates by virus type: HAV - 82.5%, HBV - 8.6%, HCV - 15.1%, HDV - 8.9%, HEV - 13.9%, dual HBV-HCV coinfection - 2.2%. Prevalence rates by risk groups: general population - 8.3%, healthcare workers - 4.0%. Continent-specific HBV-HCV prevalence rates: Africa - 9.2%, China - 6.9%, others. HCVprevalence rates among at-risk groups: healthcare workers - 5.58%, hemodialysis patients - 34.8%. Regional HCV rates: Africa - 7.42%, Middle East - 25.30%. Diverse global hepatotropic virus prevalence patterns are influenced by multifaceted factors. MENA faces higher rates due to healthcare challenges, while Africa struggles with limited resources. Tailored public health strategies, including vaccination and awareness campaigns, are essential to alleviate burdens and enhance global health. This consolidated data serves as a valuable resource for informed decision-making.

Educational boundaries explain strength and variation in global fertility convergence

This paper shows that the level and timing of fertility are converging strongly over different measures of educational attainment using 65 years of data from 146 countries. Global convergence patterns are primarily driven by high-income societies, while sub-Saharan Africa is the world region that is converging most slowly, if not converging at all. Most importantly, levels of education matter heavily for explaining strength and variation in global fertility convergence, with two intersecting educational gradients suggesting: (i) stronger convergence over tertiary education followed, in turn, by secondary and primary; (ii) stronger convergence over education completed relative to education attended. Our findings provide important insights for addressing key challenges in global development and demography, and for informing policymakers as they evaluate the suitability of specific educational policies aimed at further narrowing inequalities between societies—such as supporting higher education as well as the successful completion of targeted educational cycles.

Charged critical behavior and nonperturbative continuum limit of three-dimensional lattice SU(Nc) gauge Higgs models

We consider three-dimensional (3D) lattice SU(Nc) gauge Higgs theories with multicomponent (Nf>1) degenerate scalar fields and U(Nf) global symmetry, focusing on systems with Nc=2, to identify critical behaviors that can be effectively described by the corresponding 3D SU(Nc) gauge Higgs field theory. The field-theoretical analysis of the RG flow allows one to identify a stable charged fixed point for large values of Nf, that would control transitions characterized by the global symmetry-breaking pattern U(Nf)→SU(2)⊗U(Nf−2). Continuous transitions with the same symmetry-breaking pattern are observed in the SU(2) lattice gauge model for Nf≥30. Here we present a detailed finite-size scaling analysis of the Monte Carlo data for several large values of Nf. The results are in substantial agreement with the field-theoretical predictions obtained in the large-Nf limit. This provides evidence that the SU(Nc) gauge Higgs field theories provide the correct effective description of the 3D large-Nf continuous transitions between the disordered and the Higgs phase, where the flavor symmetry breaks to SU(2)⊗U(Nf−2). Therefore, at least for large enough Nf, the 3D SU(Nc) gauge Higgs field theories with multicomponent scalar fields can be nonperturbatively defined by the continuum limit of lattice discretized models with the same local and global symmetries. Published by the American Physical Society 2024

COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses.

The global dynamics of seasonal influenza viruses inform the design of surveillance, intervention, and vaccination strategies. The COVID-19 pandemic provided a singular opportunity to evaluate how influenza circulation worldwide was perturbed by human behavioral changes. We combine molecular, epidemiological, and international travel data and find that the pandemic's onset led to a shift in the intensity and structure of international influenza lineage movement. During the pandemic, South Asia played an important role as a phylogenetic trunk location of influenza A viruses, whereas West Asia maintained the circulation of influenza B/Victoria. We explore drivers of influenza lineage dynamics across the pandemic period and reasons for the possible extinction of the B/Yamagata lineage. After a period of 3 years, the intensity of among-region influenza lineage movements returned to pre-pandemic levels, with the exception of B/Yamagata, after the recovery of global air traffic, highlighting the robustness of global lineage dispersal patterns to substantial perturbation.