Passive immunotherapies for the next influenza pandemic.

Influenzavirus is among the most relevant candidates for a next pandemic. We review here the phylogeny of former influenza pandemics, and discuss candidate lineages. After briefly reviewing the other existing antiviral options, we discuss in detail the evidences supporting the efficacy of passive immunotherapies against influenzavirus, with a focus on convalescent plasma.

Rapid Antigen Testing in School Health Offices for Surveillance of SARS-CoV-2 and Influenza A.

Context: Evaluation of acute respiratory infections (ARIs) has been disrupted by the SARS-CoV-2 (SC2) pandemic, limiting comprehensive surveillance for respiratory pathogens in clinical settings. Alternatives to using medically attended ARI for public health surveillance are needed. A potential approach involves assessing children and school personnel in school health offices (SHOs) as pandemic-related responses enhanced innovative approaches for SC2 testing in novel settings. Objective: Evaluate the comparability of SC2 and influenza A (FLuA) detection based on rapid antigen testing in SHOs to traditional surveillance systems. Study Design: Prospective evaluation of results from rapid antigen testing for SC2 and FluA in SHOs of a suburban/rural school district compared to large-scale, countywide detection of SC2 and clinic-based surveillance of FluA using cross correlation analyses. Setting: Oregon School District (OSD) in Dane County (DC: southcentral Wisconsin) over two academic years (September 2021-April 2023). Population Studied: School-aged children (4-18 years) and school staff/teachers reporting to SHOs with ARI symptoms. Comparator data included all PCR-based testing for SC2 reported to Public Health Madison & Dane County and PCR-based testing for influenza at five family medicine clinics in DC. Intervention/Instrument: Quidel Sofia SARS/Influenza Fluorescent Immunoassay with wireless transmission of anonymous results to a cloud-based server with availability to the study team. Outcome Measures: Cross correlation and lag estimates for weekly counts of SC2 and FLuA cases comparing school-based and community-based results. Results: The SHOs at the 7 OSD schools performed 1,508 tests with few invalid tests (n=7; 0.46%). Participants had a wide age range [1-71 years] with a median age of 13 years, and included 1,145 (76%) students. SC2: 118 and 119,630 cases were identified in OSD and DC, respectively. The maximum cross correlation (r=0.82) occurred with no time lag. FluA: 61 and 75 cases were identified in OSD and DC, respectively. The maximum cross correlation (r=0.69) occurred with DC lagging OSD by 1 week. Conclusions: Surveillance for significant respiratory pathogens can be based on rapid antigen testing within SHOs, is highly comparable with larger scale surveillance programs, and demonstrates either advanced (FluA) or concurrent detections (SC2). Performance of similar programs are needed in other geographical areas and for other pathogens.

Editorial: Global Health Concerns as Vaccine-Preventable Infections Including SARS-CoV-2 (JN.1), Influenza, Respiratory Syncytial Virus (RSV), and Measles Continue to Rise.

In December 2023, the US Centers for Disease Control and Prevention (CDC) published the updated 2024 Advisory Committee on Immunization Practices (ACIP) Adult Immunization Schedule, which is available online for access by the public and healthcare professionals. These new guidelines come at a time when the incidence of vaccine-preventable viral infections from SARS-CoV-2 (JN.1), respiratory syncytial virus (RSV), influenza, and measles are increasing in adults and children due to vaccine hesitancy, or non-compliance. This editorial aims to highlight the ongoing global health concerns for the consequences of increasing reports of vaccine-preventable infections, including SARS-CoV-2 (JN.1), influenza, RSV, and measles, to understand the causes of vaccine hesitancy, and introduce some public health measures that could improve vaccine uptake.

The effect of smaller classes on infection-related school absence: evidence from the Project STAR randomized controlled trial.

BACKGROUND: In an effort to reduce viral transmission, many schools reduced class sizes during the recent pandemic. Yet the effect of class size on transmission is unknown. METHODS: We used data from Project STAR, a randomized controlled trial in which 10,816 Tennessee elementary students were assigned at random to smaller classes (13 to 17 students) or larger classes (22 to 26 students) in 1985-89. We merged Project STAR schools with data on local deaths from pneumonia and influenza in the 122 Cities Mortality Report System. Using mixed effects linear, Poisson, and negative binomial regression, we estimated the main effect of smaller classes on absence. We used an interaction to test whether the effect of small classes on absence was larger when and where community pneumonia and influenza prevalence was high. RESULTS: Small classes reduced absence by 0.43 days/year (95% CI -0.06 to -0.80, p < 0.05), but small classes had no significant interaction with community pneumonia and influenza mortality (95% CI -0.27 to + 0.30, p > 0.90), indicating that the reduction in absence due to small classes was not larger when community disease prevalence was high. CONCLUSION: Small classes reduced absence, but the reduction was not larger when disease prevalence was high, so the reduction in absence was not necessarily achieved by reducing infection. Small classes, by themselves, may not suffice to reduce the spread of respiratory viruses.

Cost effectiveness of preemptive school closures to mitigate pandemic influenza outbreaks of differing severity in the United States.

BACKGROUND: Nonpharmaceutical interventions (NPIs) may be considered as part of national pandemic preparedness as a first line defense against influenza pandemics. Preemptive school closures (PSCs) are an NPI reserved for severe pandemics and are highly effective in slowing influenza spread but have unintended consequences. METHODS: We used results of simulated PSC impacts for a 1957-like pandemic (i.e., an influenza pandemic with a high case fatality rate) to estimate population health impacts and quantify PSC costs at the national level using three geographical scales, four closure durations, and three dismissal decision criteria (i.e., the number of cases detected to trigger closures). At the Chicago regional level, we also used results from simulated 1957-like, 1968-like, and 2009-like pandemics. Our net estimated economic impacts resulted from educational productivity costs plus loss of income associated with providing childcare during closures after netting out productivity gains from averted influenza illness based on the number of cases and deaths for each mitigation strategy. RESULTS: For the 1957-like, national-level model, estimated net PSC costs and averted cases ranged from $7.5 billion (2016 USD) averting 14.5 million cases for two-week, community-level closures to $97 billion averting 47 million cases for 12-week, county-level closures. We found that 2-week school-by-school PSCs had the lowest cost per discounted life-year gained compared to county-wide or school district-wide closures for both the national and Chicago regional-level analyses of all pandemics. The feasibility of spatiotemporally precise triggering is questionable for most locales. Theoretically, this would be an attractive early option to allow more time to assess transmissibility and severity of a novel influenza virus. However, we also found that county-wide PSCs of longer durations (8 to 12 weeks) could avert the most cases (31-47 million) and deaths (105,000-156,000); however, the net cost would be considerably greater ($88-$103 billion net of averted illness costs) for the national-level, 1957-like analysis. CONCLUSIONS: We found that the net costs per death averted ($180,000-$4.2 million) for the national-level, 1957-like scenarios were generally less than the range of values recommended for regulatory impact analyses ($4.6 to 15.0 million). This suggests that the economic benefits of national-level PSC strategies could exceed the costs of these interventions during future pandemics with highly transmissible strains with high case fatality rates. In contrast, the PSC outcomes for regional models of the 1968-like and 2009-like pandemics were less likely to be cost effective; more targeted and shorter duration closures would be recommended for these pandemics.

Application of the Time Derivative (TD) Method for Early Alert of Influenza Epidemics.

BACKGROUND: In order to minimize the spread of seasonal influenza epidemic to communities worldwide, the Korea Disease Control and Prevention Agency has issued an influenza epidemic alert using the influenza epidemic threshold formula based on the results of the influenza-like illness (ILI) rate. However, unusual changes have occurred in the pattern of respiratory infectious diseases, including seasonal influenza, after the coronavirus disease 2019 (COVID-19) pandemic. As a result, the importance of detecting the onset of an epidemic earlier than the existing epidemic alert system is increasing. Accordingly, in this study, the Time Derivative (TD) method was suggested as a supplementary approach to the existing influenza alert system for the early detection of seasonal influenza epidemics. METHODS: The usefulness of the TD method as an early epidemic alert system was evaluated by applying the ILI rate for each week during past seasons when seasonal influenza epidemics occurred, ranging from the 2013-2014 season to the 2022-2023 season to compare it with the issued time of the actual influenza epidemic alert. RESULTS: As a result of applying the TD method, except for the two seasons (2020-2021 season and 2021-2022 season) that had no influenza epidemic, an influenza early epidemic alert was suggested during the remaining seasons, excluding the 2017-2018 and 2022-2023 seasons. CONCLUSION: The TD method is a time series analysis that enables early epidemic alert in real-time without relying on past epidemic information. It can be considered as an alternative approach when it is challenging to set an epidemic threshold based on past period information. This situation may arise when there has been a change in the typical seasonal epidemic pattern of various respiratory viruses, including influenza, following the COVID-19 pandemic.

Assessment of Knowledge and Biosecurity Practices Related to Avian Influenza Among Poultry Workers in a District of South India.

CONTEXT: Avian influenza, commonly known as bird flu, is a contagious disease that affects both animals and humans, posing a significant threat to public health, animal welfare, and the economy. This study aims to evaluate the knowledge of avian influenza among poultry farmworkers and evaluate the biosecurity practices implemented on their farms. OBJECTIVE: The study's primary objective was to assess the knowledge regarding avian influenza among poultry farmworkers and the biosecurity practices they follow at the farm. DESIGN: Cross-sectional study. SETTING: The study was conducted in a district of South India. PARTICIPANTS: This study included 105 poultry farmworkers across 70 poultry farms in the district. MAIN OUTCOME MEASURES: Assessment of the participants' knowledge related to avian influenza, and the association between knowledge levels, demographic, and farm-related factors such as working experience in the poultry farm, type of poultry farm, type of poultry reared, and biosecurity practices. RESULTS: In the study, 90% of participants were aware of avian influenza, with 36% correctly identifying the virus as its cause, whereas 5% wrongly cited it to be a bacteria. Although 90% knew avian influenza was infectious, only 18% recognized its potential transmission to humans; however, 82% understood prevention methods. Participants with an education level beyond high school displayed significantly higher awareness ( P < .05), emphasizing importance of the education. CONCLUSIONS: The study showed diverse awareness levels among poultry farmworkers regarding avian influenza, emphasizing gaps in the knowledge, particularly about its transmission to humans. This underscores the need for targeted awareness campaigns focusing on zoonotic risks to improve the level of understanding and implement effective preventive measures against avian influenza.

[Epidemic trends and prevention and control of seasonal influenza in China after the COVID-19 pandemic].

During the COVID-19 pandemic, a series of non-pharmaceutical interventions, which were implemented to curb the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), significantly modified the seasonal pattern of influenza. The intensity of influenza activity markedly decreased and B/Yamagata lineage was no longer detected. As the national influenza sentinel surveillance data shown, clear seasonal patterns were observed for influenza between 2012-2019, annually with an average of 14.57% of specimens tested positive for influenza virus. However, the seasonal pattern of influenza was disrupted after the outbreak of COVID-19. In the 2020-2021 season, influenza demonstrated an extremely low activity (yearly positivity rate<1.0%), followed by a resurgence of winter peak in the 2021-2022 season. Following the downgrade of management of COVID-19 to Class B in China in December 26, 2022, social activities gradually resumed, leading to the rebound of influenza activity with an out-of-season ciculation. After COVID-19 pademic, other respiratory infectious diseases caused by SARS-CoV-2, respiratory syncytial virus, and mycoplasma pneumonia were alternatively or concurrently circulated with influenza. The prevention and control of influenza and other respiratory infectious diseases emphasizes a multi-disease prevention strategy, including long-term and continuous monitoring the epidemic trends in influenza virus and SARS-CoV-2, promoting influenza and COVID-19 vaccination among key populations, and strengthening the knowledge and public awareness of prevention and control for respiratory infectious diseases, etc.

[Research progress on the impact of public health and social measures on influenza during the COVID-19 pandemic].

Public health and social measures (PHSMs) are one of the most important measures in the prevention and control of COVID-19 and have also been effective in suppressing the spread of influenza viruses, but their effectiveness has not been fully investigated. This study aimed to review the progress of research on the impact of PHSMs on influenza during the COVID-19 pandemic based on the latest evidence of the effectiveness of various PHSMs in controlling transmission of influenza viruses, to provide scientific evidence for optimizing influenza prevention and control strategies.

Bird Flu Outbreak in Dairy Cows Is Widespread, Raising Public Health Concerns.

This Medical News article discusses the current H5N1 avian influenza outbreak in US dairy cattle and its implications for occupational and public health.

Effect of planned school breaks on student absenteeism due to influenza-like illness in school aged children-Oregon School District, Wisconsin September 2014-June 2019.

Background: School-aged children and school reopening dates have important roles in community influenza transmission. Although many studies evaluated the impact of reactive closures during seasonal and pandemic influenza outbreaks on medically attended influenza in surrounding communities, few assess the impact of planned breaks (i.e., school holidays) that coincide with influenza seasons, while accounting for differences in seasonal peak timing. Here, we analyze the effects of winter and spring breaks on influenza risk in school-aged children, measured by student absenteeism due to influenza-like illness (a-ILI). Methods: We compared a-ILI counts in the 2-week periods before and after each winter and spring break over five consecutive years in a single school district. We introduced a "pseudo-break" of 9 days' duration between winter and spring break each year when school was still in session to serve as a control. The same analysis was applied to each pseudo-break to support any findings of true impact. Results: We found strong associations between winter and spring breaks and a reduction in influenza risk, with a nearly 50% reduction in a-ILI counts post-break compared with the period before break, and the greatest impact when break coincided with increased local influenza activity while accounting for possible temporal and community risk confounders. Conclusions: These findings suggest that brief breaks of in-person schooling, such as planned breaks lasting 9-16 calendar days, can effectively reduce influenza in schools and community spread. Additional analyses investigating the impact of well-timed shorter breaks on a-ILI may determine an optimal duration for brief school closures to effectively suppress community transmission of influenza.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization.

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Does a Digital Health Application Could Be the Supplement to the Influenza Surveillance System?

Epidemics of seasonal influenza is a major public health concern in china. Historical percentage of influenza-like illness (ILI%) from CDC and health enquiry data from a health-related application were collected, when combining the real-time ILI-related search queries with one-week ago's ILI%, it was able to predict the trend of ILI correctly and timely. Digital health application is potentializing a supplement to the traditional influenza surveillance systems in China.

Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.

In this study, we propose a novel approach that integrates regime-shift detection with a mechanistic model to forecast the peak times of seasonal influenza. The key benefit of this approach is its ability to detect regime shifts from non-epidemic to epidemic states, which is particularly beneficial with the year-round presence of non-zero Influenza-Like Illness (ILI) data. This integration allows for the incorporation of external factors that trigger the onset of the influenza season-factors that mechanistic models alone might not adequately capture. Applied to ILI data collected in Korea from 2005 to 2020, our method demonstrated stable peak time predictions for seasonal influenza outbreaks, particularly in years characterized by unusual onset times or epidemic magnitudes.