Search Results (195)

In this paper we aimed to compare seasonality, clinical characteristics, and outcomes of Influenza A and COVID-19 in the context of influenza reemergence and ongoing Omicron circulation. We performed a retrospective comparative analysis at the Teaching Hospital of Infectious Diseases in Cluj-Napoca, Romania. We included adult patients hospitalized with Influenza A or COVID-19 between 1 November 2022 and 31 March 2024. Data were collected on demographics, clinical presentation, complications, and in-hospital mortality. We included 899 COVID-19 and 423 Influenza A patients. The median age was 74 years for COVID-19 and 65 for Influenza A (p < 0.001). The age-adjusted Charlson comorbidity index was higher in COVID-19 patients (5 vs. 3, p < 0.001). Despite this age gap, acute respiratory failure was more common in Influenza A (62.8% vs. 55.7%, p = 0.014), but ventilation rates did not differ significantly. Multivariate models showed Influenza A was associated with increased risk of intensive-care unit (ICU) admission or ventilation, whereas older COVID-19 patients had higher in-hospital mortality (5.67% vs. 3.3%, p = 0.064). Omicron COVID-19 disproportionately affected older patients with comorbidities, contributing to higher in-hospital mortality. However, Influenza A remained a significant driver of respiratory failure and ICU admission, underscoring the importance of preventive measures in high-risk groups. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

Severe Acute Respiratory Syndrome (SARS) represents a significant cause of morbidity and mortality in children under one year of age, a particularly vulnerable population due to immunological and respiratory immaturity. The diverse etiology includes multiple respiratory viruses such as Respiratory Syncytial Virus (RSV), influenza, rhinovirus, and SARS-CoV-2, each with distinct potential to cause severe illness and death. Understanding the specific incidence and lethality by etiological agents in the recent Brazilian context (2024), after the COVID-19 pandemic, is essential to guide surveillance and public health strategies. This study aimed to analyze the risk of incidence and lethality by specific etiology of SARS in children under one year of age hospitalized in Brazil during the year 2024. A descriptive cross-sectional study was performed using secondary data from the 2024 Influenza Epidemiological Surveillance Information System (SIVEP-Gripe), obtained via OpenDataSUS. Reported cases of SARS hospitalized in children <1 year of age in Brazil were included. Distribution by final classification and epidemiological week (EW) was analyzed; the incidence rate by Federative Unit (FU) (cases/100,000 < 1 year) with risk classification (Low/Moderate/High) was assessed; and, for cases with positive viral RT-PCR, the etiological frequency and virus-specific lethality rate (deaths/total cases of etiology ×100), also with risk classification, were extracted. A multivariate logistic regression model was performed for the risk factors of death. A total of 66,170 cases of SARS were reported in children under 1 year old (national incidence: 2663/100,000), with a seasonal peak between April and May. The majority of cases were classified as “SARS due to another respiratory virus” (49.06%) or “unspecified” (37.46%). Among 36,009 cases with positive RT-PCR, RSV (50.06%) and rhinovirus (26.97%) were the most frequent. The overall lethality in RT-PCR-positive cases was 1.28%. Viruses such as parainfluenza 4 (8.57%), influenza B (2.86%), parainfluenza 3 (2.49%), and SARS-CoV-2 (2.47%) had higher lethality. The multivariate model identified parainfluenza 4 (OR = 6.806), chronic kidney disease (OR = 3.820), immunodeficiency (OR = 3.680), Down Syndrome (OR = 3.590), heart disease (OR = 3.129), neurological disease (OR = 2.250), low O₂ saturation (OR = 1.758), SARS-CoV-2 (OR = 1.569) and respiratory distress (OR = 1.390) as risk factors for death. Cough (OR = 0.477) and RSV (OR = 0.736) were associated with a lower chance of death. The model had good calibration (Hosmer–Lemeshow p = 0.693) and overall significance (p < 0.001). SARS represented a substantial burden of hospitalizations, with marked seasonal and geographic patterns. RSV and rhinovirus were the main agents responsible for the volume of confirmed cases but had a relatively low to moderate risk of lethality. In contrast, less frequent viruses such as parainfluenza 4, influenza B, parainfluenza 3, and SARS-CoV-2 were associated with a significantly higher risk of death. These findings highlight the importance of dissociating frequency from lethality and reinforce the need to strengthen etiological surveillance, improve diagnosis, and direct preventive strategies (such as immunizations) considering the specific risk of each pathogen for this vulnerable population. Full article

Viral lung infections are a never-ending threat to public health due to the emergence of new variants and their seasonal nature. While vaccines offer some protection, the need for effective antiviral drugs remains high. The existing research methods using 2D cell culture and animal models have their limitations. Human cell-based tissue engineering approaches hold great promise for bridging this gap. Here, we describe a microextrusion bioprinting approach to generate three-dimensional (3D) lung models composed of four cell types: endothelial cells, primary fibroblasts, macrophage cells, and epithelial cells. A549 and Calu-3 cells were selected as epithelial cells to simulate the cells of the lower and upper respiratory tract, respectively. Cells were bioprinted in a hydrogel consisting of alginate, gelatin, hyaluronic acid, collagen, and laminin-521. The models were cultured under air–liquid interface (ALI) conditions to further enhance their physiological relevance as lung cells. Their viability, metabolic activity, and expression of specific cell markers were analyzed during long-term culture for 21 days. The constructs were successfully infected with both a seasonal influenza A virus (IAV) and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant, demonstrating their potential for studying diverse viral infections. Full article

Background/Objectives: Seasonal influenza vaccination is crucial for reducing morbidity, mortality, and healthcare burdens. The 2023/2024 Ligurian vaccination campaign (Italy) utilized an inclusive model involving local health authorities, general practitioners, pediatricians, and pharmacies to enhance accessibility. Our study aimed at focusing on factors influencing vaccine uptake, public attitudes and access to preventive healthcare services. Methods: A cross-sectional survey was conducted among adults (≥18 years) in Ligurian pharmacies visitors during the vaccination campaign. A self-administered structured questionnaire gathered data on demographics, vaccination history, healthcare access, and awareness. Results: The study included 30,499 participants, and the median age with P25–P75 (years) was 62.0 [47.0–74.0]; 54.6% were female. Considering determinants of accepting influenza vaccination, age was identified as a strong independent predictor. Each one-year increase in age was associated with a 3.8% increase in the odds of influenza vaccination (OR 1.03, 95% CI 1.03–1.04, p < 0.001). Compared to individuals who never visited their general practitioners, those who visited “sometimes”, “often”, or “very often” had significantly higher odds of influenza vaccination (OR 1.54, 1.97, and 1.98, respectively; p < 0.001 for all categories). The strongest predictor of influenza vaccination in the 2023/2024 season was having received the influenza vaccine in the previous season (2022/2023) (OR 71.73, 95% CI 65.38–78.78, p < 0.001). Consistent with increasing age predicting higher influenza vaccination uptake, older age was associated with lower odds of refusing the vaccine due to the belief that “getting or transmitting influenza does not matter” or due to “other or unspecified reasons”. In contrast, receipt of the COVID-19 vaccination significantly increased the odds of holding these opinions. Among individuals who cited reasons such as fear of side effects, concerns about vaccine safety, fear of injections, general opposition to vaccines, or doubts about vaccine effectiveness, having received the COVID-19 vaccine was associated with lower odds of citing these as barriers to influenza vaccination. Conclusions: Fear of side effects and perceived unnecessary vaccination are key barriers. Targeted education and the involvement of general practitioners could enhance vaccine acceptance, particularly among hesitant groups. Full article

Background/Objectives: COVID-19 pandemic had a significant impact on endemic respiratory illnesses. Through behavioral changes in populations and government policy, mainly through non-pharmaceutical interventions (NPIs), Canada saw historic lows in the number of influenza A cases from 2020 through 2022. In this study, we use historical influenza A data for Canada and three provincial jurisdictions within Canada—Ontario, Quebec, and Alberta—to quantify the effects of these NPIs on influenza A. Methods: We aim to see which base parameters and derived parameters of an SIR model are most affected by NPIs. We fit a simple SIR model to historical influenza data to get average paramters for seasonal influenza. We then compare these parameters to those predicted by fitting influenza cases during the COVID-19 pandemic. Results: We find substantial differences in the effective population size and basic reproduction number during the COVID-19 pandemic. We also see the effects of fatigue and relaxation of NPIs when comparing the years 2020, 2021, and 2022. Conclusions: We find that the effective population size is the main driver of change to disease spread and discuss how these retrospective estimates can be used for future forecasting. Full article

This study proposes and examines the dynamics of a susceptible–exposed–infectious–recovered (SEIR) model for the spread of seasonal influenza. The population is categorized into four distinct groups: susceptible (S), exposed (E), infectious (I), and recovered (R) individuals. The symmetric model integrates a bilinear incidence rate alongside a nonlinear recovery rate that depends on the quality of healthcare services. Additionally, it accounts for the impact of fear related to the disease and includes a constant vaccination rate as well as a nonlinear treatment function. The model advances current epidemiological frameworks by simultaneously accounting for these interrelated mechanisms, which are typically studied in isolation. We derive the expression for the basic reproduction number and analyze the essential stability properties of the model. Key analytical results demonstrate that the system exhibits rich dynamic behavior, including backward bifurcation (where stable endemic equilibria persist even when the basic reproduction number is less than one) and Hopf bifurcation. These phenomena emerge from the interplay between fear-induced suppression of transmission, treatment saturation, and healthcare quality. Numerical simulations using Saudi Arabian demographic and epidemiological data quantify how increased fear perception shrinks the bistability region, facilitating eradication. Healthcare capacity improvements, on the other hand, reduce the critical reproduction number threshold while treatment accessibility suppresses infection loads. The model’s practical significance lies in its ability to identify intervention points where small parameter changes yield disproportionate control benefits and evaluate trade-offs between pharmaceutical (vaccination/treatment) and non-pharmaceutical (fear-driven distancing) strategies. This work establishes a versatile framework for public health decision making and the integrated approach offers policymakers a tool to simulate combined intervention scenarios and anticipate nonlinear system responses that simpler models cannot capture. Full article

Influenza (flu) is a highly prevalent respiratory illness caused by influenza viruses, representing a significant global health burden due to its substantial morbidity and mortality rate. Vaccination remains the most effective strategy for influenza prevention, and well-characterized animal models of influenza infection serve as essential tools for evaluating vaccine protective efficacy. However, animal models utilizing live influenza virus strains pose significant biosafety concerns, and many such strains are not readily available for research. To address these challenges, we established a novel visual mouse infection model using an HIV-based vector system. This model employs influenza pseudoviruses carrying a luciferase reporter gene, enabling real-time monitoring of viral load and in vivo tracking of viral distribution during infection. Using this infection model, we assessed the in vivo protective efficacy of an influenza vaccine and cross-validated the pseudovirus-based evaluation results against a live virus-infected mouse model. Our study thus establishes a safer and more convenient platform for evaluating influenza vaccine efficacy, including the assessment of broad-spectrum neutralization capacity. Full article

Objective: Our population-based cohort study aims to compute the uptake of the influenza vaccine and the associated risk of hospitalization for respiratory diseases of infectious origin based on the residency setting and dementia status of people aged 65 or over. Methods: We conducted a retrospective cohort study on the whole population of residents aged ≥65 in Lombardy, the most populated Italian region. Using region-wide administrative data, we computed the seasonal prevalence of vaccination for influenza from 1 October 2022 to 30 April 2023. To estimate the risk of hospitalization, we applied Fine-Gray sub-distribution hazard models, accounting for the competing risk of death and adjusting for confounders. Results: Our study analyzed 2,420,279 individuals aged 65+ in Lombardy. Overall, 51.4% received an influenza vaccination in 2022–2023. Among residents living at home, 50.8% were vaccinated, while nursing home residents had an uptake of 74.0%. People living with dementia reported a vaccination coverage of 62.6%, and vaccination rates were higher among those residing in nursing homes than those who lived at home. The adjusted sub-hazard ratios (SHRs) showed higher hospitalization risks of 1.88 for unvaccinated individuals with dementia and 1.74 for unvaccinated individuals without dementia living at home. In nursing homes, the SHR for respiratory hospitalization was 2.20 for individuals without dementia and 2.40 for dementia patients. Vaccination reduced risks across all groups, but disparities persisted. Conclusions: People living with dementia were more likely to be hospitalized for respiratory diseases. However, they reported an influenza vaccination coverage that was below expectations and similar to the general population, both in nursing homes and home-living settings. Public health institutions should extend and mention dementia as a higher-risk condition. Full article

Seasonal outbreaks and occasional pandemics triggered by influenza viruses annually impose considerable burdens on public health and finances. The continual evolution of viral strains with drug resistance emphasizes the urgency of discovering novel agents for influenza viruses. This study investigated a set of innovative substances derived from Morinda officinalis with antiviral potential against influenza virus strains. The top candidate, anthraquinone-2-carboxylic acid (A2CA), presented antiviral activity against diverse influenza virus strains, including those resistant to oseltamivir. In an influenza mouse model, the pre-administration of A2CA dose-dependently ameliorated influenza A virus (IAV)-mediated weight loss as well as protected mice from a lethal IAV infection. In addition, lung injury and cytokine dysregulation were mitigated. Further investigation revealed that IAV-induced activation of the RIG-I/STAT1 signaling pathway did not occur after A2CA treatment. A time-of-addition assay revealed that A2CA targeted the final phase of intracellular replication, which was further determined by molecular docking between A2CA and the IAV RdRp protein. Finally, transcriptome analysis revealed that the TP53TG3C, CFAP57 and SNX30-DT genes may be involved in the antiviral effects of A2CA. These results play a part in achieving a thorough comprehension of the capacity of A2CA to inhibit influenza virus infection. Full article

Background/Objectives: During the COVID-19 pandemic, influenza vaccination compliance among nurses in Israel was significantly lower than in previous years. This study sought to evaluate factors associated with vaccination compliance. Methods: An online cross-sectional survey conducted in March-April 2022 among 386 Israeli nurses examined perceived disease threat, vaccination barriers, perceived vaccine benefits, attitudes, and subjective norms/social influences. Results: During the 2021/2022 winter season, the vaccination rate for COVID-19 was higher than for influenza (68.4% vs. 61.9%). For both, vaccination compliance was positively associated with perceived susceptibility and severity, perceived benefits, and supporting attitude and negatively associated with barriers. The odds for COVID-19 vaccination were higher among older (OR = 1.04, 95% CI = 1.02, 1.07, p < 0.001) and more experienced nurses (age and years of experience, r = 0.89, p < 0.001). For both, perceived susceptibility and severity were higher among female nurses (influenza M = 3.29 SD = 0.88; COVID-19 M = 3.65 SD = 0.83) than male nurses (influenza M = 3.03 SD = 0.90; COVID-19 M = 3.32 SD = 0.83). A model assessing the associations between COVID-19-related variables and influenza vaccination compliance found that higher perceived susceptibility and severity regarding COVID-19, lower perceived barriers to COVID-19 vaccination, and more supportive attitudes toward COVID-19 vaccination were related to a greater likelihood of influenza vaccination compliance. Conclusions: Perceived susceptibility, perceived severity, and attitudes made a significantly greater contribution to influenza vaccination than to COVID-19 vaccination, whereas perceived benefits made a significantly greater contribution to COVID-19 vaccination than to influenza vaccination. Full article

The objectives were to assess the economic burden of COVID-19 and impact of workplace COVID-19 vaccination in the United States (US). An economic model estimated COVID-19 workplace burden (infections, long COVID, inpatient/outpatient care, absent days) with and without vaccination, compared with seasonal influenza vaccination for context, using Optum’s de-identified Clinformatics^® Data Mart Database. Without workplace vaccination, an average US business (with 10,000 employees), had 18,175 absent days from COVID-19 and lost productivity costs of USD 5.08 million. Implementing COVID-19 workplace vaccination (at 70% coverage) prevented approximately 3132 absent days, saving employers USD 876,453 (lost productivity) and USD 240,633 (medical costs); and saving employees USD 182,196 (medical costs) and USD 198,250 (lost wages) versus no COVID-19 workplace vaccination. The burden and vaccination impact were greater for COVID-19 versus seasonal influenza. Workplace vaccination for COVID-19 and seasonal influenza can have a significant impact for both the employer and employees through averted disease. Full article

This study examined the genetic and evolutionary features of influenza A/H3N2 viruses in Hangzhou (2010–2022) by analyzing 28,651 influenza-like illness samples from two sentinel hospitals. Influenza A/H3N2 coexisted with other subtypes, dominating seasonal peaks (notably summer). Whole-genome sequencing of 367 strains was performed on GridION platforms. Phylogenetic analysis showed they fell into 16 genetic groups, with multiple clades circulating simultaneously. Shannon entropy indicated HA, NA, and NS gene segments exhibited significantly higher variability than other genomic segments, with HA glycoprotein mutations concentrated in antigenic epitopes A–E. Antiviral resistance showed no inhibitor resistance mutations in PA, PB1, or PB2, but NA mutations were detected in some strains, and most strains harbored M2 mutations. A Bayesian molecular clock showed the HA segment exhibited the highest nucleotide substitution rate (3.96 × 10⁻³ substitutions/site/year), followed by NA (3.77 × 10⁻³) and NS (3.65 × 10⁻³). Selective pressure showed A/H3N2 strains were predominantly under purifying selection, with only sporadic positive selection at specific sites. The Pepitope model demonstrated that antigenic epitope mismatches between circulating H3N2 variants and vaccine strains led to a significant decline in influenza vaccine effectiveness (VE), particularly in 2022. Overall, the study underscores the complex circulation patterns of influenza in Hangzhou and the global importance of timely vaccine strain updates. Full article

Monitoring coastal marine wildlife is crucial for biodiversity conservation, environmental management, and sustainable utilization of tourism-related natural assets. Conducting in situ censuses and population studies in extensive and remote marine habitats often faces logistical constraints, necessitating the adoption of advanced technologies to enhance the efficiency and accuracy of monitoring efforts. This study investigates the utilization of aerial imagery and deep learning methodologies for the automated detection, classification, and enumeration of marine-coastal species. A comprehensive dataset of high-resolution images, captured by drones and aircrafts over southern elephant seal (Mirounga leonina) and South American sea lion (Otaria flavescens) colonies in the Valdés Peninsula, Patagonia, Argentina, was curated and annotated. Using this annotated dataset, a deep learning framework was developed and trained to identify and classify individual animals. The resulting model may help produce automated, accurate population metrics that support the analysis of ecological dynamics. The resulting model achieved F1 scores of between 0.7 and 0.9, depending on the type of individual. Among its contributions, this methodology provided essential insights into the impacts of emergent threats, such as the outbreak of the highly pathogenic avian influenza virus H5N1 during the 2023 austral spring season, which caused significant mortality in these species. Full article

Background: Workers in healthcare facilities can encourage and serve as role models for the general population regarding vaccination. The information source preferences of employees in healthcare facilities can play an important role in their decisions to receive COVID-19 and seasonal influenza vaccinations (SIVs). A study of specific channels of information and their impact on vaccine acceptance could provide valuable insights. Methods: A cross-sectional questionnaire-based survey was conducted during the first semester of 2021 among 2592 staff members in healthcare facilities (primary, secondary and tertiary). Results: Higher odds of seasonal influenza vaccination (SIV) acceptance were found among staff who were informed by the National Public Health Organization (NPHO) (adjusted Odds Ratio (aOR): 1.47, 95% confidence intervals (CI): 1.13–1.90), the Hellenic Ministry of Health (HMH) (aOR: 1.50, 95% CI: 1.16–1.94) and the Healthcare Facilities Infection Control Committees (ICC) (aOR: 1.35, 95% CI: 1.06–1.73). Professionals who were more willing to accept a COVID-19 vaccine were more likely to obtain information from television (aOR: 1.43, 95% CI: 1.08–1.92), the ICC (aOR: 1.36, 95% CI: 1.03–1.81), the NPHO (aOR: 1.71, 95% CI: 1.28–2.28) and the HMH (aOR: 1.68, 95% CI: 1.26–2.26). Social media presented no statistically significant association with either COVID-19 vaccine acceptance or SIV. Conclusions: Workers in healthcare facilities who received information from highly credible organizations were more likely to accept vaccines. Television was effective in disseminating COVID-19 vaccine campaigns. Full article