Influenza and Other Respiratory Viruses

Introduction: Respiratory infections are a leading cause of morbidity. Newly available vaccines to prevent respiratory syncytial virus (RSV) disease and encouraging clinical progress on vaccines for human metapneumovirus (hMPV) and parainfluenza (PIV) could reduce the disease burden beyond existing influenza and SARS-CoV-2 immunisation programs. However, evidence on the contribution of these viruses to respiratory disease burden across the lifespan remains limited. Methods: We reviewed studies from 01/2002-11/2025 reporting age-stratified, medically attended cases of influenza, and at least one of RSV, hMPV, or PIV, in high-income countries, excluding periods substantially overlapping with the COVID-19 pandemic. Using only studies that tested for all four viruses, we estimated the age-specific proportion of cases that were non-influenza (total across RSV, hMPV and PIV) compared to influenza using a mixed-effects logistic regression model. Results: Following exclusions and screening, 61 studies were included in the primary analysis comprising >500,000 detections of the four viruses. We found that a substantial proportion of medically attended respiratory illness in infants and young children was due to PIV, hMPV and RSV, rather than influenza, with a non-influenza virus proportion of 90.2% (95% CI 85.9-93.2%) in young infants aged 0-6 months. The converse was true for school-aged children, with a non-influenza virus proportion of 34.8% (95% CI 26.5-44.2%) in children aged 5-18 years. In adults aged 65+ years, non-influenza causes of medically attended disease were common at 60.2% (95% CI 50.0-69.5%). Restricting to studies reporting hospitalised cases (n=19) produced broadly similar age-specific trends in relative virus burden contributions. Discussion: We highlight the significant burden of medically attended illness due to PIV, hMPV and RSV across ages, particularly in infant and preschool-aged children and older adults, supporting the need for effective vaccines targeting this burden.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

Global efforts to prevent and mitigate the impacts of high pathogenicity avian influenza (HPAI) H5 on domestic animals, humans, and wildlife rely on timely and transparent information that is both accurate and interpretable across countries and sectors. International epidemiological and genomic databases, such as the World Animal Health Information System (WAHIS), the Global Animal Disease Information System (EMPRES-i+), the Global Initiative on Sharing All Influenza Data (GISAID), and the National Center for Technological Bioinformation Virus Portal (NCBI) provide essential information for surveillance, research, and decision-making. To evaluate how well these resources capture recent wildlife impacts, we consolidated information from these databases and complementary public sources including government reports, scientific literature, and news articles, on wildlife mortality associated with HPAI H5 in the Americas from November 2021 to July 2024. The consolidated dataset comprised 615,883 wild birds (287 spp.) and 63,409 wild mammals (39 spp.). In comparison, WAHIS represented 16,902 wild birds (261 spp.) and 6,323 wild mammals (31 spp.) while EMPRES-i+ captured a substantially smaller portion of affected host diversity for both wild birds (105 spp.) and wild mammals (27 spp.). Genomic databases (GISAID and NCBI) represented 7,027 whole genome equivalents of H5 viruses from wild birds (175 spp.) and 371 from wild mammals (26 spp.). These discrepancies indicate that international databases, while essential, provide an incomplete picture of HPAI impacts on wildlife, with significant geographic and taxonomic asymmetries attributable to differences in surveillance capacity, reporting practices, sequencing effort, and data-sharing pathways. Studies and management strategies relying on these resources without complementary validation may therefore mistake data gaps for real-world epidemiological patterns. Strengthening data reporting standards, improving validation procedures, and integrating international databases with national reports, scientific publications, and other sources will enhance the reliability of epidemiological analyses and support more effective One Health surveillance, risk assessment, and conservation action. Author summaryHigh pathogenicity avian influenza (HPAI) H5 viruses, often called bird flu viruses, can cause severe disease in birds and mammals, including humans. Because of their relevance for human health, livestock production, and wildlife conservation, international databases were established to share information on when and where these viruses are detected, which species are affected, and what virus strains are found. These databases are essential tools for governments, scientists, and conservation practitioners working to track outbreaks, understand how these viruses spread and evolve, and guide surveillance and response. In this study, we compiled and compared information on recent HPAI H5 events in wildlife in the Americas available in international databases with information from other public sources, including reports from governments, scientific literature, and news articles. We found important discrepancies in how countries and species affected were represented across sources. As a result, international databases might not fully capture the actual distribution or conservation impact of HPAI H5 on wildlife. Our findings also show why decision-makers and scientists should interpret database-derived patterns carefully. We provide recommendations to improve international databases to address these gaps and better inform One Health risk assessment and wildlife conservation actions.

Background Human challenge trials provide a unique opportunity to quantify pathogen infectivity in terms of the probability of infection given an inoculated dose. However, between-pathogen comparisons are often distorted by individual heterogeneity in host susceptibility and by differences in background immunity across trial populations. We examined how dose-dependent infection risks differ across common respiratory viruses when such heterogeneity is explicitly incorporated. Methods We conducted a systematic review of human challenge trials for four respiratory viruses: respiratory syncytial virus (RSV), influenza virus, rhinovirus, and adenovirus. Using the extracted data, we fitted dose-response models under different distributional assumptions, allowing both continuous susceptibility variation and discrete immune fractions. We compared alternative heterogeneity models and evaluated pathogen-specific dose-response patterns using original and scaled dose metrics. Results All four viruses showed substantial heterogeneity in host susceptibility, and models assuming homogeneous susceptibility were unsupported. RSV and influenza were best described by models with a distinct immune or effectively non-susceptible subgroup, and the estimated immune proportions were approximately 40% and 25%, respectively. In contrast, rhinovirus and adenovirus were better explained by continuously distributed susceptibility, with little evidence of a fully immune subgroup. On a scaled dose axis, rhinovirus and adenovirus showed steeper increases in infection risk with dose than RSV and influenza. Conclusions The structure of susceptibility heterogeneity differs across common respiratory viruses, which in turn shapes dose-dependent infection risks. Incorporating this heterogeneity is essential for valid cross-pathogen comparison and for interpreting human challenge data in epidemiologic and public health contexts.

Background: The 2026 FIFA World Cup may bring over one million visitors to North America from around the globe to participate in mass gathering events. The nature of the event and recent news have raised concerns for some that the tournament could lead to infectious disease outbreaks or fuel existing epidemics. Objective: To systematically assess the infectious disease threat posed to the United States by the tournament. Design: A multi-institutional team evaluated pathogen-specific risk across three dimensions: importation, outbreak potential, and impact to identify a priority pathogen list. A systematic screening protocol ensured common criteria and that pathogen information was collected when necessary to inform inclusion. Results: Increased risk from the World Cup is near zero for 63 of 77 evaluated pathogens. Pathogens were predominantly excluded as threats due to low excess importation risk and low outbreak potential if introduced. The remaining priority pathogens fall into five categories: (a) mosquito borne pathogens with the potential for sustained transmission in some host cities, (b) seasonal respiratory viruses, (c) chronic infections with high prevalence outside the United States, (d) pathogens present in the United States with likely increased transmission at World Cup activities, and (e) high-consequence infectious threats. Limitations: Data availability is variable across diseases. Impact calculations may not reflect actual costs to host cities. Disease incidence in World Cup travelers may differ from national incidence rates. Conclusion: While infectious disease outbreaks at the 2026 FIFA World Cup are possible, in an already highly connected world where large gatherings are frequent, the elevated risk from the tournament is not as extreme as it first may seem.

Currently circulating swine influenza viruses (SIVs) mainly include H1N1, H1N2, and H3N2 subtypes. In this study, two G4 genotype Eurasian avian-like (EA) H1N1 SIVs were isolated from 556 samples collected between 2023 and 2026. A systematic analysis was conducted on the two EA H1N1 isolates (FYD30 and YZF69) to assess their pandemic potential. The hemagglutinin (HA) proteins of both H1N1 viruses possessed residues 225E and 228S, indicating enhanced affinity for human-like -2,6-linked sialic acid receptors, which was confirmed by receptor-binding assays. Polymerase activity tests demonstrated that the two SIVs exhibited significantly higher activity in mammalian cells, relative to avian cells, which is consistent with the efficient replication in mammalian cells. Challenge experiments revealed that both H1N1 caused significant pathogenicity in mice and pigs, with YZF69 exhibited higher virulence than FYD30. The higher virulence of YZF69 may be attributed to its molecular features, including the NP Q357K mutation, and an additional glycosylation site in HA. In conclusion, currently circulating EA H1N1 SIVs have acquired key molecular signatures of mammalian adaptation, exhibit enhanced virulence in mammals, and continue to undergo extensive reassortment driven by international swine trade. These findings highlight the potential pandemic risk of SIVs and underscore the urgent need for strengthened surveillance.

Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.

Since the U.S. 2013/14 influenza season, the CDC's FluSight Challenge has provided a platform for evaluating influenza forecasting models and fostering collaboration across institutions. The Challenge aims to improve the science and enhance the utility of infectious disease forecasts for public health decision making. We analyzed ten years of submitted forecasts (2014/15-2019/20 (influenza-like illness seasons) and 2021/22-2024/25 (hospital admissions seasons)) across a range of model types, including statistical, mechanistic, machine learning, and hybrid models. Influenza-like illness (ILI) forecasts were evaluated using the exponentiated logarithmic score (skill metric) while hospital admissions forecasts were evaluated using the log transformed relative Weighted Interval Score. Corresponding potential performance differences were assessed using Wilcoxon rank-sum tests, and associations with team participation history were evaluated using Spearman's rank correlation. Model performance varied by season, and no single model type consistently outperformed others. In ILI seasons, statistical models generally performed better than mechanistic and machine learning models, though consistent differences were not observed in more recent hospital admissions seasons. Ensemble forecasts showed better overall performance across seasons, and the CDC's FluSight ensemble ranked among the top-performing forecasts every year. We also found a positive correlation between forecast accuracy and the number of years a team participated in the Challenge, with statistically significant associations in four seasons. These findings highlight the benefits of ensemble approaches and sustained engagement in improving forecasting performance, while also underscoring the continued value of forecast evaluation before and following the COVID-19 pandemic. Insights from the FluSight Challenge can guide future infectious disease forecasting efforts and support more effective public health preparedness.

Background Post-acute sequelae are well described following COVID-19 but may also occur after other respiratory infections and Aedes-borne infections. Evidence remains fragmented due to heterogeneity in study design, populations, and exposure, outcome, and follow-up definitions. Methods We synthesized and compared post-acute sequelae across influenza, RSV-ARI, dengue fever, chikungunya, Zika, and yellow fever. We searched five databases from inception to 25-08-2025 for articles quantifying risk, incidence, or rates of post-acute sequelae following these diseases. Eligible non-randomized observational studies assessed post-acute neurological, psychiatric, gastrointestinal, cardiovascular, respiratory, renal, musculoskeletal, autoimmune, or endocrine outcomes after confirmed infection. Risk of bias was assessed using ROBINS-E. Random-effects meta-analyses with restricted maximum likelihood estimation were conducted when comparable effect estimates were available (PROSPERO #CRD420251124994). Findings 51 studies were included, predominantly from high-income regions. Most were retrospective cohorts using ICD-coded diagnoses; prospective studies used laboratory-confirmed infections. Data sources, comparator groups, exposure definitions, outcome ascertainment, and follow-up periods varied substantially. Meta-analyses were feasible for RSV, influenza, and dengue fever. All RSV-ARI studies were pediatric and assessed infections during infancy, which were associated with higher pooled odds of physician-diagnosed asthma (OR:2.93 [95%CI: 2.12-4.06]). Influenza studies used COVID-19-positive comparators; pooled estimates showed lower risk for neurological (HR:0.82 [0.76-0.89]) and composite outcomes (RR:0.88 [0.82-0.95]), with other organ systems non-significant. Dengue fever studies spanned all ages and showed increased risks of anxiety (HR:1.34 [1.01-1.78]), dementia (HR:1.61 [1.10-2.35]), autoimmune (RR:1.39 [1.17-1.67]), cardiovascular (HR:1.51 [1.27-1.80]), psychiatric (HR:1.17 [1.07-1.28]), and any sequelae (HR:1.19 [1.13-1.25]) versus those without prior infection. Interpretations Post-acute sequelae contribute to overall disease burden following RSV-ARI and dengue fever. The evidence remains limited by heterogeneity in study design, exposure and outcome definitions, comparator selection, and follow-up duration. Greater standardization in study design and reporting is needed to improve comparability and strengthen causal inference.

Background/Objectives: Influenza infection is a major trigger of pneumonia and acute exacerbations among patients with chronic obstructive pulmonary disease (COPD). However, national laboratory-confirmed evidence on influenza vaccine effectiveness (VE) in this high-risk population remains limited. This study aimed to estimate the effectiveness of seasonal influenza vaccination against influenza-associated pneumonia and COPD exacerbations among patients with COPD in Thailand.Methods: We conducted a nationwide retrospective test-negative design study using administrative healthcare data from the National Health Security Office linked with laboratory-confirmed influenza surveillance data between June 1, 2013, and May 31, 2025, covering twelve influenza seasons (2013-2024). COPD-related clinical episodes among patients aged [≥]40 years who presented with pneumonia or acute exacerbation of COPD and underwent RT-PCR testing for influenza were included. Multilevel Poisson regression models were used to estimate adjusted risk ratios (RRs), and VE was calculated as (1 - adjusted RR) x 100.Results: A total of 606,072 COPD-related clinical episodes were included, of which 192,224 (31.7%) were influenza-positive. The overall adjusted VE against influenza-associated pneumonia was 63.2% (95% CI: 62.5-64.0), while VE against influenza-associated COPD exacerbations was 67.0% (95% CI: 48.8-78.8). VE estimates were broadly similar across age groups and remained substantial across COPD severity strata. Although point estimates were numerically higher in severe and very severe COPD, subgroup differences should be interpreted cautiously.Conclusions: Seasonal influenza vaccination was associated with substantial protection against influenza-associated pneumonia and COPD exacerbations among patients with COPD in Thailand.

Background Appendicitis is a readily treatable surgical emergency, yet it remains a cause of preventable death among children in resource-limited settings. While recent studies have documented the global burden of pediatric appendicitis, none have systematically examined its geographic clustering or spatial spillover effects. Understanding whether high-mortality countries cluster geographically, and whether neighboring countries influence each other's outcomes, is essential for designing regional surgical capacity strategies. Methods We conducted a spatial analysis of pediatric appendicitis case fatality rates in children aged 0-14 years across 169 countries from 2000 to 2019. Data were obtained from the Global Burden of Disease Study 2023 and World Bank databases. We calculated global Moran's I to assess spatial autocorrelation, used Getis-Ord Gi* to identify local hotspots, and fitted spatial lag and spatial error regression models to quantify spatial spillovers while adjusting for GDP per capita, physician density, and basic sanitation access. Results Global Moran's I was 0.621 in 2000 (p < 0.001), 0.621 in 2010 (p < 0.001), and 0.592 in 2019 (p < 0.001), indicating strong and persistent spatial clustering. Hotspots at 99% confidence were consistently concentrated in sub-Saharan Africa and parts of South Asia, with little change in geographic distribution over two decades. The spatial error model provided the best fit (AIC = 212.6), with a spatial error coefficient ({lambda}) of 0.663 (p < 0.001), suggesting that approximately 66% of residual variation was explained by unobserved regional factors. In the final model, higher GDP per capita ({beta} = -0.497, p < 0.001) and higher physician density ({beta} = -0.568, p < 0.001) were independently associated with lower case fatality, while basic sanitation access showed no significant association (p = 0.284). Conclusions Pediatric appendicitis case fatality exhibits strong and persistent geographic clustering. The substantial spatial spillover effect suggests that regional coordination of surgical capacity building may be more effective than country-by-country investments. Priority should be given to hotspot countries in sub-Saharan Africa and South Asia, with emphasis on surgical workforce expansion rather than broad economic development alone.

Background: Diarrheal illness in children leads to 3.5 million care visits and 200,000 hospitalizations annually in the US. Viruses are responsible for most pediatric diarrheal cases, yet limited guidance on distinguishing viral from bacterial etiologies complicates clinical decision-making, especially regarding empiric antibiotic use. Methods: We used clinical and qualitative molecular etiologic data from the Implementation of Molecular Diagnostics for Pediatric Acute Gastroenteritis (IMPACT) study to develop prediction models for viral etiology of diarrhea. We used conditional random forests to identify informative clinical and environmental predictors and evaluated model performance using logistic regression and random forests within a 5-fold cross-validation framework. We conducted external validation using the Alberta Provincial Pediatric Enteric Infection Team (APPETITE) dataset. Results: Variables predictive of viral etiology included younger age, non-bloody diarrhea, winter season, and presence of vomiting. External validation showed that an AUC of 0.82 can be achieved with a parsimonious 5-variable model, yielding a sensitivity of 0.92 and specificity of 0.55 Conclusion: Our results suggest that in North American healthcare settings, clinical prediction models can inform decision-making by identifying children with a high probability of viral diarrhea, improving diagnostic clarity, and reducing unnecessary testing and treatment.

Objectives To quantify the frequency of baseline control-group use in published long COVID prevalence studies and assess their key methodological features. Design Cross-sectional meta-epidemiological evaluation of published post-acute COVID-19 prevalence studies, supplemented by a corresponding-author survey. Setting Published studies identified through a systematic review by Hou et al. (2025) and supplementary data obtained through direct email contact with corresponding authors. Participants A total of 440 published long COVID prevalence studies. Main Outcome measures Presence and type of comparator group, reliance on solely self-reported outcomes, acknowledgment of lack of a control group among uncontrolled studies, and availability of additional comparator data through author survey. Results Among 440 studies, 372 (84.5%) reported no control group on their publication. Healthy or uninfected comparators were reported in 55 studies (12.5%) and other comparator types in 14 (3.2%); 1 study included both categories. Solely self-reported outcomes were used in 279 studies (63.4%). Among 372 uncontrolled studies, 244 (65.6%) did not explicitly acknowledge the absence of a baseline comparator as a limitation anywhere in text. Corresponding authors of 140 studies (31.8%) responded to the survey; among them, 126 (90.0%) reported no additional comparative data, while 14 (10.0%) mentioned some available comparative datasets (19 additional datasets). Almost all of that information (10/14, 17/19) had been already published in other articles not captured by the Hou et al. systematic review. Conclusions Most published long COVID prevalence studies lacked comparator groups and relied exclusively on self-reported outcomes without acknowledging this limitation. Direct author contact identified little additional comparator information. Much of the long COVID prevalence literature may therefore be poorly suited to estimating burden attributable specifically to SARS-CoV-2, underscoring the need for appropriately matched comparators and more objective outcome assessment. Registration The protocol was prospectively registered on the Open Science Framework (https://osf.io/f4hra).

Nasopharyngeal (NP) swabs remain the dominant gold standard for respiratory infection diagnostics. While there has been increased use of alternative sample types since the COVID-19 pandemic, guidance on their use for detecting respiratory viruses is not yet definitive, especially for children. In this systematic review and meta-analysis, we aimed to compare the diagnostic accuracy and tolerability of multiple respiratory specimen types for detecting respiratory viruses in pediatric populations. Searches were conducted on July 17, 2025 in MEDLINE, Embase, Web of Science, and Scopus, with screening and data extraction performed in Covidence. English-language primary research articles published since 2000 comparing respiratory virus detection rates in children, using nucleic acid amplification tests between paired respiratory specimens, were included. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies criteria. We calculated pooled sensitivities and specificities of index specimens: nasopharyngeal aspirates (NPA), mid-turbinate swabs (MT), anterior nasal swabs (ANS), oropharyngeal swabs (OP), and bronchoalveolar lavage fluid (BAL), as compared to the reference, NP swabs, using random-effects modeling, firstly without discrimination by virus. Index specimens were then grouped by sample collection site as nasal, oral, and lower respiratory tract (LRT) specimens for virus-specific analyses. Overall performance and statistical validity were evaluated by hierarchical summary receiver operating characteristic (HSROC) analysis. Data regarding sampling tolerability was also assessed. We screened 2,448 studies and identified 36 publications (total N participants = 10,687) that reported diagnostic test accuracy using paired index-reference data in children. Of these, 18 (total N participants = 4,310) used NP specimens as the reference and were included in the diagnostic test accuracy analysis. Virus-agnostic pooled sensitivity estimates indicated that MT (0.92%) performed most similarly to NP, though sensitivities of ANS (0.79%) and OP (0.70%) were also moderately high for detection of any respiratory virus. BAL sensitivity was the lowest (0.37%). All sample types demonstrated high specificity (0.98%-0.99%). Group estimates and HSROC statistics found that nasal specimens, when grouped, had the highest sensitivity and accuracy for all examined viruses, including for influenza (92%) and RSV (90%). By comparison, oral and LRT specimens performed less well, with more variability, though both showed moderately high sensitivities for RSV (78%, 76%, respectively) and influenza (82%, 80%, respectively), and LRT samples showed high sensitivity for HMPV (82%). Analysis of sample tolerability found that NP swabs consistently ranked as the least comfortable and least preferred, while nasal swabs and saliva both performed well. Datasets for LRT and oral specimens were sparser than for nasal, and this contributed to greater variability, underscoring the need for further diagnostic accuracy studies on alternatives to NP sampling. These data support the viability of nasal and oral alternatives to NP swabs and affirm their application in pediatric care, particularly in outpatient settings. Such alternatives could greatly improve sampling tolerability and increase global access, including in resource-limited settings, to accurate diagnostic methods for respiratory infections.

Of the two influenza A virus (IAV) subtypes circulating endemically in humans, A/H3N2 and A/H1N1pdm09, A/H3N2 has historically been the dominant driver of disease burden in older adults. Based on an analysis of publicly available global surveillance data from 2015 to 2025 (>300,000 subtyped, age-stratified infections), we report a substantially increased contribution of A/H1N1pdm09 to influenza morbidity in older adults since approximately 2022. Birth cohort-stratified analyses suggest elevated A/H1N1pdm09 burden among individuals born before 1955-1959, consistent with erosion of pre-existing immunity originally generated by exposure to historical A/H1N1 strains. Pooled estimates across datasets and analytical approaches indicate the increase in A/H1N1pdm09 burden rises with earlier birth year, ranging from 1.22-fold (95% CI 1.08-1.37) for the 1955-1959 birth cohort to 3.10-fold (95% CI 2.58-3.72) for the 1930-1934 cohort. These findings point to a substantial rise in the overall influenza burden among the most vulnerable age groups, with implications for vaccine policy, clinical management, and public health planning.

Human metapneumovirus (HMPV) causes acute lower respiratory infections, primarily affecting young children and older adults, with seasonal outbreaks peaking annually in March or April in the United States and other temperate regions in the Northern hemisphere. However, the factors driving HMPV seasonality in the United States remain poorly understood. We analyzed laboratory-confirmed HMPV cases and age-specific emergency department visits across 10 US regions, fitting an age-stratified dynamic transmission model to assess spatiotemporal patterns and investigate the influence of environmental variables and viral interference from RSV on HMPV transmission rates. We found that models incorporating climate variables into the transmission rate, including vapor pressure, precipitation, potential evapotranspiration, and minimum temperature, could not capture the timing of HMPV activity across all regions. Instead, HMPV timing was associated with RSV activity, with the HMPV transmission rate reduced in the presence of RSV. We showed that, unlike RSV, only models incorporating viral interference could reproduce the biennial pattern of HMPV observed in some regions, characterized by alternating late-small and early-large epidemics. Furthermore, our model successfully reproduced post-COVID-19 HMPV and RSV epidemics and predicted that RSV interventions are not likely to lead to a substantial increase in HMPV activity despite decreasing competition from RSV. Our work unravels the spatiotemporal dynamics of HMPV and its interaction with RSV, informing future seasonal forecasting and intervention strategies for HMPV.

Despite concerns about the spread and pandemic potential of H5N1, there is no commercial H5N1 vaccine. Seasonal influenza vaccines offer some cross-protection against H5N1, but to date there has been no consideration of whether protection differs between the sexes. We investigated immune responses and protection in adult male and female C57BL/6 mice following vaccination with either inactivated H1N1 or H5N1 (LAIV backbone) virus vaccines. Vaccination induced strong homologous antibody responses, with females generating greater total IgG than males against both H1N1 and H5N1 vaccine, which was primarily mediated by greater IgG responses to neuraminidase (NA) than hemagglutinin (HA) protein. IgG cross-recognition of H1N1 also was greater among H5N1 vaccinated females and was primarily caused by greater IgG responses to N1. IgG2b and IgG2c were the primary isotypes generated in response to these vaccines, with females having greater IgG2b responses and greater binding to Fc{gamma}RIV for avian and human NA than males in response to both homologous and heterologous vaccination. Antibody-dependent complement deposition was measured as an FcR-mediated non-neutralizing response against HA and NA and was robust in both sexes. Vaccinated females had greater neutralizing antibody titers than males against the homologous vaccine virus, with limited cross-neutralizing antibodies detected in either sexes. Neuraminidase inhibition titers were greater in vaccinated females than males against the heterologous virus following H1N1 vaccination and against both the vaccine and heterologous viruses following H5N1 vaccination. When H1N1 and H5N1 vaccinated mice were challenged with a lethal dose of A/Texas/37/2024 H5N1, all H5N1 vaccinated mice were protected, regardless of sex. Among H1N1 vaccinated mice, while both sexes were protected against disease, H1N1 vaccinated females cleared virus faster than their male counterparts. These findings highlight that female-biased NA-specific antibodies result in greater cross-protection and should be considered in studies of influenza vaccines. HighlightsO_LIFemales mount stronger IgG responses than males to both H1N1 and H5N1 vaccines C_LIO_LISex differences in vaccine responses are driven by immunity to neuraminidase (NA) C_LIO_LINA inhibition titers are greater in females, supporting broader cross-protection C_LIO_LIH5N1 vaccination confers full protection in both sexes against lethal H5N1 challenge C_LIO_LIH1N1-vaccinated females clear H5N1virus faster than males after lethal challenge C_LI

Using a commercially available H5 serology assay, we identified a 7.4% (n=5/68) anti-H5 seroreactivity rate among hunters in Northern Canada. All participants reported close contact with wild birds.

Background: Wearing facemasks and practising social distancing slow the spread of respiratory pathogens. However, in the event of a new pandemic emerging, the willingness of populations to voluntarily adopt these behaviours is unclear. Methods: A discrete choice experiment was conducted among 2,006 UK-based adults. Participants were presented with hypothetical scenarios describing the emergence of a respiratory virus pandemic and were asked to choose when they would wear facemasks and practise social distancing. A mixed multinomial logit model was used to jointly estimate how disease severity and prevalence, uncertainty in these quantities, and individual-level characteristics influence behavioural choices. Findings: Participants were averse to facemasks and social distancing in the absence of pandemic risk. For each ten-unit increase in severity (10 additional hospitalisations/1,000 infections), the odds of always wearing a facemask outside the home increased by 15.9% (95%CI: 14.3%, 17.5%), relative to rarely/never, and the odds of avoiding all people as much as possible increased by 16.4% (14.6%, 18.2%), relative to not avoiding anyone. Greater disease prevalence, uncertainty in disease severity or disease prevalence, a university education, prior COVID-19 vaccination and non-white ethnicity were also associated with choosing to always wear facemasks and avoid all people as much as possible. The probability of participants choosing to rarely/never wear facemasks varied from 13.4% (11.9%, 14.9%) in the lowest-risk scenario to 1.4% (1.2%, 1.7%) in the highest-risk scenario. Interpretation: Perceived risks of disease and associated uncertainty drive intention of UK adults to adapt their behaviour in a future pandemic.

Background Home Test-to-Treat (HTTT) programs deliver timely antiviral treatment for acute respiratory infections, including COVID-19 and influenza, through at-home testing and telehealth. Because access is often measured by visit occurrence, variation in how and when care is delivered may be overlooked. We hypothesized that telehealth access follows distinct process-based patterns. Methods We analyzed de-identified encounters from the national HTTT program (September 2023-July 2024); 6,213 of 8,160 eligible individuals remained after exclusions for missing data. Phenotypes were derived by k-means clustering of standardized variables capturing encounter timing, modality preference, process duration, and sociodemographic and digital access attributes. Ten-day surveys assessed symptom duration and healthcare utilization. Results Three phenotypes emerged: Delayed/Disrupted Access (n = 1,537; 24.7%), Digitally Engaged but Socioeconomically Vulnerable (n = 1,460; 23.5%), and Mainstream Access and Efficient Utilization (n = 3,216; 51.8%). Mean process duration differed (15.93 [SD 3.84] vs 3.69 [3.31] vs 2.87 [2.41] hours; p < 0.001). Synchronous preference was lowest in the Digitally Engaged group (22.9%); antiviral prescribing was high (88.6%-91.9%). Among 10-day respondents (n = 1,023), symptom duration did not differ. Emergency department visits were most frequent in the Digitally Engaged group (2.3% vs 0.0% and 0.5%; p = 0.02) and urgent care in the Delayed/Disrupted group (5.8% vs 4.1% vs 2.0%; p = 0.02). Conclusions Telehealth use in a national HTTT program formed distinct phenotypes defined by timing, modality, and care-process efficiency. Evaluating equity requires attention to how and when care is delivered, not simply whether it occurred.