Influenza and Other Respiratory Viruses

While there are numerous studies about influenza pandemic-associated school closures, literature is scant regarding closures associated with seasonal influenza. To address this knowledge gap, we conducted systematic daily online searches from August 1, 2011- June 30, 2022, to identify public announcements of unplanned school closures in the US lasting [≥]1 day, selecting those that mentioned influenza and influenza-like illness (ILI) as reason for school closure (ILI-SCs). We studied ILI-SC temporal patterns and compared them with reported outpatient ILI-related healthcare visits and laboratory confirmed influenza hospitalizations with attention to the difference between the pre-COVID-19 pandemic and the COVID-19-affected years. We documented that ILI-SCs occurred annually and concurrently with, and likely as a consequence of, widespread illness. The strongest correlations were primarily observed during influenza A (H3N2)-dominant seasons. ILI-SCs were heavily centered in HHS Region 4 and disproportionately impacted rural and lower-income communities. Article summary lineInfluenza-related school closures occurred annually in the US and their temporal patterns mirror the general patterns of influenza activity on both national and regional levels as observed through routine surveillance of medically attended ILI.

Background & AimsSeasonal respiratory viruses such as influenza cause substantial illness in the United States, over-whelming healthcare facilities and reducing economic productivity. Effective surveillance of these viruses is therefore critical for timely risk communication, strategic resource allocation, and coor-dinated public health responses that mitigate viral spread. Syndromic surveillance, which tracks patient symptoms rather than confirmed diagnostic results, plays an essential role in disease monitoring. While this form of surveillance aids in early trend detection, widespread adoption, particularly for unobserved disease burden estimation, has been hindered by insufficient validation against laboratory-confirmed cases and the lack of accurate syndromic profiles. In this study, we leverage a high-volume medical claims database to develop data-driven syndromic profiles for influenza based on symptom patterns from lab-confirmed cases. We then apply these syndromic profiles to estimate total symptomatic case dynamics and burden (both tested and untested) by geography and demography. Methods & ResultsWe analyzed a large medical claims database covering healthcare visits for over 40% of the United States population annually from 2016 to 2020. We used a regression modeling approach to develop syndromic profiles based on lab-confirmed cases of influenza. With these models, we estimated spatiotemporal dynamics at the county-week scale and season prevalence from time series data on symptom occurrence in healthcare settings. We validated our estimates by comparing them with traditional surveillance data. Symptom-inferred disease estimates aggregated to state and national-levels showed strong agreement with existing surveillance systems in both spatiotemporal trends and magnitude of disease activity. Across all seasons examined, influenza prevalence was spatially heterogeneous, with the southern United States experiencing the highest burden. ImplicationsOptimized syndromic surveillance has promise to serve as a representative, fine-scale, and admin-istratively efficient system for tracking infectious diseases. Public health priorities such as disease forecasting, transmission parameter estimation, and hospital bed allocation can benefit from high-resolution data and disease-specific syndromic profiles. Overall, disease-specific syndromic surveillance will provide more precise monitoring, strengthening public health preparedness and response capabilities.

BackgroundMultiple prophylactic products are now available to protect against respiratory syncytial virus (RSV) in different age groups. Assessing the pre-intervention burden of RSV infections across various severity levels and risk groups is crucial, as it provides a baseline for evaluating the impact of these products. MethodsWe obtained monthly time series data on hospitalizations, intensive care unit (ICU) admissions, and deaths by age group, ZIP code, and cause for New York state from 2005 to 2019. Socioeconomic status (SES) of the ZIP codes was classified using supervised principal component analysis (PCA). We estimated the incidence of hospitalizations, ICU admissions, and deaths attributable to RSV and to influenza using hierarchical Bayesian regression models. Additionally, we assessed severity, defined by ICU admission and mortality risks, as well as recording fraction (i.e., percent of estimated virus-associated hospitalizations recorded as being due to the specific virus), stratified by age, SES, and over time. ResultsThe estimated annual incidence of RSV-associated hospitalizations and ICU admissions were highest in infants under 1 in the low SES group (2,240 [95% credible interval (CrI): 2,200-2,290] hospitalizations and 330 [95% CrI: 320-350] ICU admissions per 100,000 person-years). The incidence of RSV-associated deaths was highest among adults [≥]85 years old (61 [95% CrI: 49-74] per 100,000 person-years). In contrast to RSV, the burden of influenza was greatest in age groups [≥]65 years. The risk of ICU admission varied by patients age and SES, and the mortality risk increased dramatically with age for both pathogens (RSV: 11.9% [95% CrI: 9.6-14.3%], influenza: 14.4% [95% CrI: 13.1-15.6%] among [≥]85 year age group). Incidence varied by epidemic year and season, and we observed an increasing recording fraction of RSV among all age groups over the study period. ConclusionsRSV and influenza contribute significantly to the burden of hospitalizations, ICU admissions, and deaths, particularly among infants and older adults. Although the recording fraction of RSV increased over the study period, it remains lower, particularly for adults. Our findings reveal a disparity in hospitalization burden by SES, particularly among younger age groups.

ObjectiveTo compare the population rate of COVID-19 and influenza hospitalizations by age, COVID-19 vaccine status and pandemic phase. DesignObservational retrospective study SettingResidents of British Columbia (population 5.3 million), Canada ParticipantsHospitalized patients due to COVID-19 or historical influenza Main outcome measuresThis population based study in a setting with universal healthcare coverage, used COVID-19 case and hospital data for COVID-19 and influenza. Admissions were selected from March 2020 to February 2021 for the annual cohort and the first 8 weeks of 2022 for the peak cohort of COVID-19 (Omicron era). Influenza annual and peak cohorts were from three years with varying severity: 2009/10, 2015/16, and 2016/17. We estimated hospitalization rates per 100,000 population by age group. ResultsSimilar to COVID-19 with median age 66 (Q1-Q3 44-80), influenza 2016/17 mostly affected older adults, with median age 78 (64-87). COVID-19 and influenza 2016/17 hospitalization rate by age group were "J" shaped. The rates for mostly unvaccinated COVID-19 patients in 2020/21 in the context of public health restrictions were significantly higher than influenza among individuals 30 to 69 years of age, and comparable to a severe influenza year (2016/17) among 70+. In early 2022 (Omicron peak), rates primarily due to COVID-19 among vaccinated adults were comparable with influenza 2016/17 in all age groups while rates among unvaccinated COVID-19 patients were still higher than influenza among 18+. In the pediatric population, COVID-19 hospitalization rates were similar to or lower than influenza. ConclusionsOur paper highlighted the greater population-level impact of COVID-19 compared with influenza in terms of adult hospitalizations, especially among those unvaccinated. However, influenza had greater impact than COVID-19 among <18 regardless of vaccine status or the circulating variant.

BackgroundThe primary objective of influenza immunization programs is to reduce the risk and burden of severe outcomes. To inform optimal program strategies, we monitored influenza hospitalizations over several seasons of varying subtype predominance, stratified by age, comorbidity and vaccination status. MethodsWe assembled data from an active hospital-based surveillance network involving systematic swabbing and PCR-confirmation of influenza virus infection by type/subtype during peak-weeks of seven influenza seasons (2012/13 to 2018/19) in Quebec, Canada. We estimated seasonal, population-based incidence of influenza-associated hospitalizations (interpreted as risk) by subtype, age, comorbidity and vaccine status, and derived the number-needed-to-vaccinate to prevent one hospitalization per stratum. ResultsThe average seasonal incidence of influenza-associated hospitalization was 89/100,000 (95%CI: 86, 93), lower during A(H1N1) (49-82/100,000) than A(H3N2) seasons (73-143/100,000). Overall risk followed a J-shaped age pattern, highest among infants 0-5 months and adults [&ge;]75 years. Hospitalization risks were highest for children <5 years during A(H1N1) but for adults [&ge;]75 years during A(H3N2) subtype- predominant seasons. Age-adjusted hospitalization risks were 7-fold higher among individuals with versus without comorbidities (214 versus 30/100,000). The number-needed-to-vaccinate to prevent hospitalization was 82-fold lower for [&ge;]75-years-olds with comorbidity (n=1,995), who comprised 39% of all hospitalizations, than for healthy 18-64-year-olds (n=163,488), who comprised just 6% of all hospitalizations. ConclusionsIn the context of broad-based influenza immunization programs (targeted or universal), severe outcome risks should be simultaneously examined by subtype, age, comorbidity, and vaccine status. Policymakers require such detail to prioritize further promotional efforts and expenditures toward the greatest and most efficient program impact. 40-word summaryThis hospital-based study involving systematic PCR testing over seven seasons revealed important differences in influenza hospitalization risk by subtype, age, comorbidity, and vaccination status. The findings highlight the need for data-driven decision-making to optimize vaccination strategies and minimize healthcare burden.

BackgroundInfluenza vaccination prevents influenza and influenza-like illness (ILI) in children, but its potential influence on susceptibility to other respiratory viruses remains unclear. We aimd to evaluate the relationships between influenza/ILI, non-influenza respiratory virus (NIRV) infections, and influenza vaccination in children. MethodsWe conducted a retrospective cohort study using real-world data from the Italian Pedianet pediatric network, including children aged 6 months to 14 years followed during two influenza seasons (September 2022-April 2023 and September 2023-April 2024). Multistate models were used to describe transitions over time between vaccination and infection states (influenza/ILI and NIRV), allowing events to occur in any order and enabling estimation of transition probabilities and timing. A Self-Controlled Case Series (SCCS) analysis using Poisson regression estimated the incidence rate ratio (IRR) of NIRV infection associated with vaccination, accounting for influenza/ILI and seasonality. ResultsAmong 91,695 children (median age 8 years; IQR 4-11; 51% male), vaccinated children showed delayed progression to NIRV infection and fewer early infections. Multistate models showed a substantially lower probability of transitioning to NIRV infection, by approximately 9- to >60-fold, in vaccinated vs unvaccinated children. In the SCCS analysis, influenza vaccination was associated with a reduced risk of NIRV infection (2022-2023:IRR 0.54; 95%CI,0.51-0.58; 2023-2024:IRR 0.60; 95%CI,0.56-0.64). ConclusionsInfluenza vaccination was associated with reduced NIRV infection risk in children, beyond its expected protection against influenza/ILI. These findings support virus interactions as a plausible mechanism shaping infection dynamics and reinforce the value of influenza vaccination in pediatric respiratory disease.

IntroductionFew studies have examined the incidence of virologic-confirmed influenza virus and respiratory syncytial virus (RSV) infections in community-dwelling older adults. MethodsWe enrolled adults aged 60-89 years in Jiangsu Province, China and followed them weekly from December 2015-September 2017 to identify acute respiratory illnesses (ARI), collect illness information and respiratory specimens for laboratory testing. Results1,527 adults were enrolled, 0{middle dot}4% reported ever receiving influenza vaccination. 95 PCR-confirmed influenza ARIs and 22 RSV ARIs were identified, among whom 4-5% required hospitalization. One death associated with RSV ARI while none for influenza ARIs was observed. From December 2015-August 2016, the cumulative incidences of influenza and RSV ARIs were 0{middle dot}8% (95% CI:0{middle dot}3-1{middle dot}4) and 0{middle dot}5% (95% CI:0{middle dot}1-1{middle dot}0), respectively. From September 2016-August 2017, the cumulative incidences were 6{middle dot}1% (95% CI:4{middle dot}7-7{middle dot}7) and 1{middle dot}0% (95% CI:0{middle dot}5-1{middle dot}6); the influenza and RSV ARI-associated hospitalization incidences were 0{middle dot}3% (95% CI:0-0{middle dot}8) and 0{middle dot}1% (95% CI:0-0{middle dot}2). Feverishness was more common in influenza (55%) than RSV ARIs (30%, p=0{middle dot}03). Influenza (12{middle dot}5 days, p=0{middle dot}02) and RSV ARI symptoms (14{middle dot}1 days, p=0{middle dot}15) lasted longer compared to PCR-negative/other ARIs (11{middle dot}0 days). Antibiotic use was more common for influenza (65%, p=0{middle dot}02) and RSV (70%, p=0{middle dot}04) ARIs than other ARIs (51%). ConclusionsWe observed a higher incidence of influenza relative to RSV infections among community-dwelling older adults compared to prior studies. Our findings suggest older adults may benefit from receiving influenza and RSV vaccines to reduce the occurrence of illnesses.

Mathematical and computational models are often used to forecast respiratory infectious disease burden, including to inform healthcare capacity requirements. We aimed to characterize pathways of clinical progression associated with SARS-CoV-2, influenza, and respiratory syncytial virus (RSV) infections using data from patients in an integrated healthcare system, whose encounters were monitored across all levels of acuity spanning virtual, ambulatory, and inpatient care settings. Using parametric survival models, we estimated probabilities of progression and distributions of time to progression from each care setting to all higher-acuity settings on a cascade encompassing the following classes of events or healthcare encounters: symptoms onset; diagnostic testing; telehealth or other virtual care appointment; outpatient physician office visit; urgent care presentation; emergency department presentation; hospital admission; mechanical ventilation; and death. Our analyses included data from 59,668, 22,705, and 1,668 episodes associated with positive SARS-CoV-2, influenza, and RSV tests, respectively, between 1 April 2023 and 31 March 2024. First clinical encounters occurred in inpatient settings for only 4.7%, 3.4%, and 18.7% of SARS-CoV-2, influenza, and RSV episodes, respectively, with median times (interquartile range) of 6.8 (3.6-13.2), 6.6 (3.5-12.1), and 6.4 (3.8-10.6) days from symptoms onset to admission. Overall, 7.9% of SARS-CoV-2 episodes, 5.8% of influenza episodes, and 33.8% of RSV episodes resulted in inpatient admission, ventilation, or death. Between 40.4-62.1%, 71.6-87.3%, and 47.9-58.7% of SARS-CoV-2, influenza, and RSV infections, respectively, had encounters in lower-acuity virtual care, outpatient, or urgent care settings. For all three viruses, the proportions of cases receiving care at each level of acuity increased with older age and greater numbers of comorbid conditions. Median durations of hospital stay were 4.2 (2.6-7.3), 4.0 (2.3-6.8), and 4.3 (2.5-7.4) days for SARS-CoV-2, influenza, and RSV episodes resulting in admission. These estimates provide a basis for modeling real-world clinical care requirements and the progression of respiratory viral infections. AUTHOR SUMMARYModels of respiratory infections such as SARS-CoV-2, influenza, and RSV are used to forecast disease burden and plan the allocation of healthcare resources. Early healthcare system encounters--for instance, for receipt of care in virtual or outpatient settings--may provide a basis for anticipating higher-acuity care requirements as patients progress to more severe disease. However, limited data are available addressing patterns of healthcare utilization among patients with these infections. Using electronic healthcare records from an integrated healthcare system, we estimated probabilities and rates of progression from lower-acuity states, such as virtual or outpatient visits, to increasingly higher-acuity states including inpatient admission, ventilation, and death. We quantified associations of demographic and clinical risk factors with progression probabilities for each infection. We provide a databank containing fitted distributions for progression to inform infectious disease modeling.

Our understanding of influenza transmission remains imperfect due to the high prevalence of asymptomatic infections that often go undetected. To address this challenge, we leveraged uniquely resolved data from a household cohort study spanning three consecutive years in rural and urban South Africa. The study incorporated pre-season serum collection and twice-weekly virological testing during the influenza season, regardless of symptom presence. We developed a subtype/lineage-specific influenza household transmission model that accounts for time-resolved viral shedding across the full clinical spectrum of infections, allowing us to disentangle the role of household and community exposures, pre-season immunity, and age on transmission. Our analysis revealed that viral shedding intensity, as measured by the cycle threshold (Ct) values of infected household members, significantly correlated with the risk of transmission for all four influenza subtypes/lineages. After adjusting for viral shedding, pre-season hemagglutination inhibition (HAI) titers greater than 1:40 were associated with a significantly lower risk of infection acquisition for A(H1N1)pdm09, A(H3N2), and B/Victoria, but not for B/Yamagata. Notably, children exhibited higher susceptibility and longer viral shedding durations compared to adults across all subtypes/lineages, even after adjusting for pre-season HAI titers. While our findings support that HAI titers correlate with protection, the strong residual effects of age on susceptibility and viral shedding may reflect the accumulation of additional immune responses shaped by repeated exposures over time. Our study underscores the need to explore immune mechanisms beyond HAI titers that modulate influenza susceptibility and transmission.

We built a framework for categorizing week-to-week increases or decreases in seasonal influenza hospitalizations aiding data interpretation in the context of past seasons. Using Influenza Hospitalization Surveillance Network (FluSurv-NET) data, we established thresholds for weekly hospitalization rate differences and applied them to the 2022/23 and 2023/24 influenza seasons from the National Healthcare Safety Network. While the number of weeks categorized as stable was consistent across seasons, more large increases and decreases were observed in 2022/23. This metric captures hospitalization rate changes and contextualizes the current season relative to past influenza trends, facilitating trend assessment.

Electronic health records (EHRs) are a rich source of data which can be used to analyse health outcomes using computable phenotypes. With the approval of NHS England we used the OpenSAFELY secure analytics platform to design and assess phenotypes to classify three key respiratory viruses - respiratory syncytial virus (RSV), influenza, and COVID-19 - in English coded health data between September 2016 and August 2024. We compared specific and sensitive phenotypes to one another and to publicly available surveillance data. Cases from both phenotypes showed similar seasonal patterns to surveillance data. Sensitive phenotypes led to increased risk of misclassification than specific phenotypes for mild cases. For severe cases the risk of misclassification was higher in infants than for older adults, irrespective of the phenotype used. The phenotypes presented here offer a solution to classifying respiratory viruses from coded health records in the absence of testing information.

BackgroundInfluenza A(H1N1)pdm09 and A(H3N2) viruses predominated during the 2024-25 U.S. influenza season. We estimated influenza vaccine effectiveness (VE) in the United States against mild-to-moderate outpatient influenza illness by influenza type and subtype in the 2024-25 season. MethodsWe enrolled outpatients aged [&ge;]8 months with acute respiratory illness symptoms including cough in 7 states. Upper respiratory specimens were tested for influenza type/subtype by reverse-transcriptase polymerase chain reaction (RT-PCR). Influenza VE was estimated with a test-negative design comparing odds of testing positive for influenza among vaccinated versus unvaccinated participants controlling for age, study site, underlying health status, and month of illness onset. We also estimated VE of current season vaccination among adults stratified by prior season vaccination status. ResultsAmong 6,793 enrolled patients, 2,016 (30%) tested positive for influenza including 961 A(H3N2), 770 A(H1N1)pdm09, and 183 B/Victoria. Overall vaccine effectiveness against any influenza illness was 33% (95% Confidence Interval [CI]: 24 to 41): 27% (95% CI: 14 to 39) against influenza A(H3N2), 37% (95% CI: 24 to 48) against A(H1N1)pdm09, and 40% (95% CI: 12 to 59) against B/Victoria. VE did not differ based on whether or not participants had received influenza vaccine the previous season. ConclusionsInfluenza vaccination during the 2024-25 season protected against circulating influenza viruses, reducing the risk of outpatient medically attended influenza overall by approximately one-third among people who were vaccinated. Key PointsInfluenza vaccine reduced the risk of outpatient acute respiratory illness due to laboratory-confirmed influenza during the 2024-25 season by a third.

Novel infectious diseases, predominately originating from non-human animals, pose a significant threat to global public health and economic stability. Avian influenza virus presents an especially significant challenge due to its high mortality rates and spillover capability into new host species. Recent H5N1 spillover events into poultry and cattle resulted in massive economic burden and increased human health risk. Traditional methods of disease surveillance rely on reactive case detection and pathogen characterization, providing insufficient lead time for effective intervention. Computational tools that allow efficient and proactive prediction of zoonotic potential are critical in mitigation of influenza outbreaks and identification of strains with human spillover risk. Existing models predicting influenza virus subtypes or host have been developed; however, the complexity of spillover events, including the non-binary nature of zoonotic potential, limits the capabilities of these models. In the approach reported here, rich protein language model embeddings were generated from ESM-2 for each protein in influenza virus strains and used to predict the protein host tropism probabilities across nine animal families. The protein host tropism model achieved weighted precision and recall scores of 0.95 and 0.95, respectively. We then constructed a zoonotic risk prediction model using the outputs from the protein host tropism prediction model to classify the strains into six classifications: avian, mammal, human, avian-to-human zoonotic, avian-to-mammal zoonotic, or mammal-to-human zoonotic. The average weighted precision and recall scores for this model were 0.90 and 0.90, respectively. This framework advances the prediction of influenza zoonotic risk by being agnostic to influenza subtype, incorporating non-human mammals and mammal zoonotic spillover classifications, and using the full influenza proteome to capture the complexity of spillover dynamics.

Background/ObjectivesThe trajectory of influenza hospitalization burden from pre-pandemic baseline through post-pandemic recovery remains poorly characterized at the national level. This study characterized phase-stratified burden and seasonal structure, quantified racial and ethnic disparities, and assessed whether post-pandemic seasons represent anomalous departures from pre-pandemic expectations. MethodsSixteen seasons of FluSurv-NET surveillance data (2009-2010 through 2024-2025; 509 observation weeks) were analyzed across pre-pandemic, disruption, and recovery phases using OLS regression with effect-size estimation, bootstrapped age-adjusted rate ratios, seasonal-trend decomposition (STL), Prophet time-series forecasting, and Isolation Forest anomaly detection. ResultsMean peak weekly hospitalization rate nearly doubled from pre-pandemic to recovery (5.1 to 11.1 per 100,000), cumulative seasonal burden increased from 46.3 to 87.0 per 100,000, and median peak timing advanced from MMWR week 9 to week 50. STL decomposition revealed a marked shift from weak pre-pandemic seasonality (Fs = 0.14) to substantially stronger annual regularity (Fs = 0.98) across three recovery seasons, with threefold amplitude increase. Non-Hispanic Black persons had rate ratios of 1.72, 2.16, and 1.99 relative to White persons across phases; American Indian and Alaska Native persons showed the highest disruption-phase ratio (2.24, 95% CI 1.90-3.53), based on two contributing seasons. A flat-growth Prophet model detected first exceedance in February 2020, outperforming a linear-growth specification on held-out validation. Isolation Forest identified 2017-2018, 2023-2024, and 2024-2025 as robust anomalies across all contamination thresholds. ConclusionsPost-pandemic influenza recovery is characterized by intensified and restructured seasonality, persistent racial and ethnic disparities, and anomalous burden exceeding pre-pandemic projections, identified independently by time-series forecasting and unsupervised anomaly detection.

BackgroundReduced influenza transmission during the COVID-19 pandemic prompted concern about waning of population immunity that could lead to subsequent surges in circulation. We evaluated this by comparing longitudinal influenza antibody titers in Michigan and Hong Kong, two regions with reduced influenza transmission during the COVID-19 pandemic. MethodsIn two prospective cohort studies (HIVE, Michigan; EPI-HK, Hong Kong), we analyzed longitudinal serum samples collected from 2020 through 2023 from participants without documented influenza virus infection or vaccination. Sera were tested using hemagglutination inhibition assays (HAI) against relevant vaccine strains. Geometric mean titers (GMTs) and fold changes were estimated by region and time. Linear mixed-effects models were used to assess temporal trends. ResultsWe analyzed 173 sera from 57 HIVE participants and 259 sera from 60 EPI-HK participants. Initial GMTs in 2020-21 ranged from 12.3-123.4 in HIVE and 6.3-40.9 in EPI-HK (B/Yamagata-H1N1). Fold changes in GMTs ranged from 1.2-2.6 in HIVE and 0.7-1.0 in EPI-HK. In HIVE models, no significant change in HAI titers over time was detected. In EPI-HK, small but statistically significant monthly declines were observed for select H1N1 (A/Michigan) and H3N2 (A/Hong Kong) strains (e.g., A/Hong Kong: -0.98%, 95% CI: -1.82% to -0.11%). ConclusionMinimal HAI titer waning was observed in both regions. In some cases, antibody levels increased in Michigan, possibly indicating cryptic circulation of strains prior to the 2022/23 influenza season. These findings do not support an "immunity debt" during pandemic restrictions and could help explain the lack of a substantial surge in influenza impact after the COVID-19 pandemic.

FluSurvey is a participatory surveillance system used to monitor trends in influenza and other respiratory viruses through weekly symptom surveys among the UK population. We aimed to characterise the wider impact of "influenza-like illnesses" (ILI) among FluSurvey participants and assess correlations of ILI with other established influenza surveillance systems. We included data reported by FluSurvey participants over the 2023-24 and 2024-25 winter seasons. Using weekly symptoms surveys, we derived ILI episodes and estimated the proportion reporting healthcare service use, medication use, impact on daily life, absenteeism and use of tests. We applied existing methodologies (omitting first report and weighting to the age-sex structure of England) and assessed cross-correlations of weekly FluSurvey ILI rates with the national surveillance of GP ILI consultations, influenza hospital admissions, and influenza PCR test positivity at time lags of up to +/- 2 weeks. There were 3057 participants over two winter seasons (N2023-24=2540, 63% female, mean age 60 years; N2024-25=2273, 64% female, mean age 61 years). Of 1868 ILI episodes, only a minority contacted healthcare services (14%, most frequently visiting the GP). A large proportion of episodes reported medication use (89%), impact on daily life (75%) and missing school or work (47%). Notable differences in testing behaviour were apparent by season, with fewer reporting use of tests in 2024-25. FluSurvey ILI rates were strongly correlated with other influenza surveillance, predominantly leading GP ILI consultations (max r=0.73), coinciding with influenza hospital admissions (max r=0.88) and lagging influenza test positivity (max r=0.88). The majority of ILI reported to FluSurvey do not contact healthcare due to symptoms but experienced wider impacts on daily life. FluSurvey ILI corresponds well with other national influenza surveillance and provides broader context on community illness, supplementing the monitoring of influenza activity for public health response.

Respiratory syncytial virus (RSV) infections and hospitalization have surged sharply among young children. Here we test how the seasonal patterns of RSV infections in 2022 compared with those from other COVID-19 pandemic and pre-pandemic years. For this purpose, we analyzed a nation-wide and real-time database of electronic health records of 56 million patients across 50 states in the US. The monthly incidence rate of first-time RSV infection in young children (<5 years of age) and very young children (<1 year of age) followed a seasonal pattern from 2010 to 2019 with increases during the autumn, peaking in winter, subsiding in spring and summer. This seasonal pattern was significantly disrupted during the COVID-19 pandemic. In 2020, the incidence rate of RSV infections was remarkably low throughout the year. In 2021, the RSV season expanded to 9 months starting in the early summer and peaking in October. In 2022, RSV infections started to rise in May and were significantly higher than in previous years reaching a historically highest incidence rate in November 2022. There were significant racial and ethnic disparities in the peak RSV infection rate during 2010-2021 and the disparities further exacerbated in 2022 with peak incidence rate in black and Hispanic children 2-3 times that in white children. Among RSV-infected children in 2022, 19.2% had prior documented COVID-19 infection, significantly higher than the 9.7% among uninfected children, suggesting that prior COVID-19 could be a risk factor for RSV infection or that there are common risk factors for both viral infections. Our study calls for continuous monitoring of RSV infection in young children alongside its clinical outcomes and for future work to assess potential COVID-19 related risk factors.

BackgroundRespiratory virus transmission in healthcare settings is not well understood. To investigate the transmission dynamics of common healthcare-associated respiratory virus infections, we performed retrospective whole genome sequencing (WGS) surveillance at one pediatric and two adult teaching hospitals in Pittsburgh, PA. MethodsFrom January 2, 2018, to January 4, 2020, nasal swab specimens positive for rhinovirus, influenza, human metapneumovirus (HMPV), or respiratory syncytial virus (RSV) from patients hospitalized for [&ge;]3 days were sequenced on Illumina platform. High-quality genomes were assessed for genetic relatedness using [&le;]3 single nucleotide polymorphisms (SNPs) cut-off, except for rhinovirus (10 SNPs). Patient health records were reviewed for genetically related clusters to identify epidemiological connections. ResultsWe collected 436 viral specimens from 359 patients: rhinovirus (n=291), influenza (n=50), HMPV (n=47), and RSV (n=48). Of these, 55% (197/359 patients) were from pediatric hospital and 45% from adult hospitals. Patients ranged in age from 14 days to 93 years, 61% were male, and 74% were white. WGS was performed on 61.2% (178/291) rhinovirus, 78% (39/50) influenza, 92% (44/48) RSV, and all HMPV specimens. Among high-quality genomes, we identified 14 genetically related clusters involving 36 patients, ranging in size from 2-5 patients. We identified common epidemiological links for 53% (19/36) of clustered patients; 63% (12/19) patients had same-unit stay, 26% (5/19) had overlapping hospital stays, and 11% (2/19) shared common provider. On average, genetically related clusters spanned 16 days (range:0-55 days). ConclusionWGS offered insights into respiratory virus transmission dynamics. These advancements could potentially improve infection prevention and control strategies, leading to enhanced patient safety and healthcare outcomes. SummaryWe performed retrospective whole genome sequencing surveillance of common healthcare-associated respiratory virus infections across three hospitals. Our investigation elucidated complex respiratory virus transmission dynamics, which could potentially improve infection prevention and control strategies, leading to enhanced patient safety and healthcare outcomes.

Background The COVID-19 pandemic substantially altered respiratory pathogen circulation worldwide. However, longitudinal analyses of changes in respiratory pathogen ecology across the pandemic and post-pandemic periods remain limited. Methods We analyzed 19,968 respiratory samples tested with the BioFire(R) FilmArray(R) Respiratory Panel at a hospital in Tokyo, Japan, between January 2020 and March 2026. We evaluated temporal changes in pathogen circulation, age-specific epidemiology, co-detection patterns, pairwise pathogen associations, and clinical parameters. Results At least one respiratory pathogen was detected in 27.8% of tests. Respiratory pathogens resurged asynchronously following the relaxation of COVID-19-related public health measures. Influenza virus circulation remained markedly suppressed until late 2022 before re-emerging in successive large seasonal epidemics, whereas other pathogens, including RSV, human metapneumovirus, and Mycoplasma pneumoniae, exhibited distinct resurgence patterns. Pathogen distributions also varied by age. Human rhinovirus/enterovirus remained predominant among young children, whereas SARS-CoV-2 predominated among older adults. Co-detection occurred in 14.0% of positive specimens and was significantly more frequent in younger patients. Pairwise analysis identified both positive and negative pathogen associations; however, the patterns varied across age groups and study periods. Conclusions Respiratory pathogen circulation changed substantially during the transition from the COVID-19 pandemic to the post-pandemic period, with pathogen-specific, age- and period-dependent patterns. Continued surveillance is warranted to determine how respiratory pathogen circulation will evolve and to inform infection control strategies in the post-pandemic era.

Abstract textO_ST_ABSBackgroundC_ST_ABSCOVID-19 pandemic mitigation measures, including travel restrictions, effectively limited global circulation of influenza viruses. In Australia, travel bans for non-residents and quarantine requirements for returned travellers were eased in November 2021, providing pathways for influenza viruses to be re-introduced. MethodsFrom 1 November 2021 to 30 April 2022 we conducted an epidemiological study to investigate the re-establishment of influenza in Victoria, Australia. We analyzed case notification data from the Victorian Department of Health to describe case demographics, interviewed the first 200 cases to establish probable routes of virus reintroduction, and examined phylogenetic and antigenic data to understand virus diversity and susceptibility to current vaccines. ResultsOverall, 1598 notifications and 1064 positive specimens were analyzed. The majority of cases occurred in the 15-34 year age group. Case interviews revealed a higher incidence of international travel exposure during the first month of case detections and high levels of transmission in university residential colleges associated with the return to campus. Influenza A(H3N2) was the dominant subtype, with a single lineage predominating despite multiple importations. ConclusionsEnhanced testing for respiratory viruses during the COVID-19 pandemic provided a more complete picture of influenza virus transmission compared to previous seasons. Returned international travellers were important drivers of the re-emergence of influenza, as were young adults, a group whose role has previously been under-recognised in the establishment of seasonal influenza epidemics. Targeting interventions, including vaccination, to these groups could reduce influenza transmission in the future.