Search Results (1,537)

This paper proposes that immune delay, beyond immune evasion, is key in the propagation competence of major viral and bacterial infections, and that the dynamics of infection and immune response suggest possibilities for mitigating the ensuing infectious diseases. Recent data show a critical role of neutrophils at various stages of viral and bacterial infections, revealing how early activation of neutrophils could help mitigate infectious diseases. It could prevent the gradual overactivation of subclasses of neutrophils and probably not induce it. In respiratory virus infections, an immune delay of several days allows the development of a high viral load supporting infectivity towards further hosts when a delayed and increased immune response takes place. Virus variants will optimize immune delay towards highest infectivity, supporting pandemic potential. The influenza virus, coronavirus, and several major bacterial infections exhibit such immune delay capability. Recurrent urinary tract infections (rUTI) are common, often associated with the causative uropathogenic E. coli (UPEC) that has this capability, suggesting that immune delay is crucial in the pathogenesis of rUTI and other widespread bacterial infections. Counteracting immune delay, therefore, is a promising approach for mitigating infectious diseases with epidemic and pandemic presence or potential. Previously proven low-frequency electromagnetic field (LF-EMF)-induced neutrophil activation is such an approach. Full article

Epidemic modeling plays a crucial role in understanding disease transmission and informing public health strategies. This study presents a fractional Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) model incorporating Atangana–Baleanu-Caputo (ABC) fractional derivatives to capture memory effects in disease dynamics. The model extends classical ordinary differential equation-based frameworks by integrating a fractional approach, enhancing its applicability to real-world epidemic scenarios. A key feature of our model is the inclusion of mortality rates across all disease compartments, providing a refined representation of influenza-like infections with pandemic potential. We conduct a detailed stability analysis to assess equilibrium states and derive conditions for disease control. Numerical simulations further validate the theoretical findings, offering insights into epidemic progression and intervention strategies. Our results highlight the significance of fractional calculus in epidemiological modeling and its potential to improve predictive accuracy for infectious disease outbreaks. Full article

One difficulty that arises in tracking and forecasting real-time epidemics is reporting delays, which are defined as the inherent delay between the time of symptom onset and the time a case is reported. Reporting delays can be caused by delays in case detection, symptom onset after infection, seeking medical care, or diagnostics, and they distort the accurate forecasting of diseases during epidemics and pandemics. To address this, we introduce a practical nowcasting approach grounded in survival analysis and actuarial science, explicitly allowing for non-stationarity in reporting delay patterns to better capture real-world variability. Despite its relevance, no flexible and accessible toolbox currently exists for non-stationary delay adjustment. Here, we present Nowcast-It, a tutorial-based toolbox that includes two components: (1) an R code base for delay adjustment and (2) a user-friendly R-Shiny application to enable interactive visualization and reporting delay correction without prior coding expertise. The toolbox supports daily, weekly, or monthly resolution data and enables model performance assessment using metrics such as mean absolute error, mean squared error, and 95% prediction interval coverage. We demonstrate the utility of Nowcast-It toolbox using publicly available weekly Ebola case data from the Democratic Republic of Congo. We and others have adjusted for reporting delays in real-time analyses (e.g., Singapore) and produced early COVID-19 forecasts; here, we package those delay adjustment routines into an accessible toolbox. It is designed for researchers, students, and policymakers alike, offering a scalable and accessible solution for addressing reporting delays during outbreaks. Full article

Poland, as an important transit hub in Europe, has experienced a dynamic increase in the significance of air transport in recent years. However, the outbreak of the COVID-19 pandemic in 2020 led to a severe collapse of passenger aviation worldwide. The aim of this study was to assess the condition of the passenger air transport market in Poland against the background of selected European countries in connection with the disruptions caused by the pandemic. The Holt–Winters models, based on pre-pandemic data, made it possible to forecast passenger transport volumes in the absence of the crisis and compare them with actual values to estimate losses and the extent of recovery. In the first three months after the outbreak, passenger losses ranged from 7.8 million in Sweden to 13.5 million in Portugal, while Poland recorded 10.9 million; after one year, cumulative losses in Poland reached 44.5 million. In addition, the pace of restoration of selected markets was evaluated. In 2022, Poland reached levels of up to 145% of its reference value, indicating one of the strongest restoration dynamics among the analyzed countries. The results show that all markets experienced sharp declines followed by a comparable rate of growth. The findings confirm Poland’s strengthening position in the European air transport system and highlight the need to build resilience to potential future crises. Full article

Background: The epidemiology of influenza was disrupted during the COVID-19 pandemic. Following the relaxation of non-pharmaceutical interventions, influenza viruses have re-emerged and caused epidemics with shifts in age distribution and seasonality. This study aimed to characterise the post-pandemic epidemiology of influenza infections among children in Xi’an, China. Methods: A retrospective analysis of laboratory-confirmed paediatric influenza cases spanning three periods [pre-pandemic (1 January 2010–22 January 2020), intra-pandemic (23 January 2020–8 January 2023), and post-pandemic (9 January 2023–31 August 2025)] was conducted. Age-specific incidences were determined by subtypes (lineage) and compared across periods. Seasonal parameters were estimated using a generalised linear model with harmonic terms. Associations between influenza infection and risk of co-detection with other respiratory pathogens were assessed using logistic regression models. Results: Influenza peak activity in the post-pandemic period was 10-fold higher than in the intra-pandemic period. The mean age of infected children increased by 1.4 years (95% CI: 1.2–1.7), shifting towards school-aged children (6–17 years). The seasonal pattern re-established with an earlier peak (13.9 weeks earlier than the pre-pandemic period, 95% CI: 10.4–15.2) and increased amplitude (10-fold and 4-fold higher than the intra- and pre-pandemic periods, respectively). It was observed that A(H1N1)pdm09 positivity was elevated in preschool and school-aged children, whereas B/Victoria infections showed renewed susceptibility among infants [0–5 months vs. 6–35 months vs. 3–5 years vs. 6–17 years: 11.0% (95% CI: 5.1–19.8) vs. 2.8% (1.9–4.0) vs. 4.0% (3.2–5.0) vs. 5.2% (4.5–6.0); p = 0.00014]. Influenza infection was associated with higher risk of bacterial co-detection with Streptococcus pneumoniae (aOR = 1.52, 95% CI: 1.22–1.91) and Haemophilus influenzae (aOR = 1.46, 95% CI: 1.19–1.80), but lower risk of co-detection with SARS-CoV-2 (aOR = 0.52, 95% CI: 0.27–0.99), RSV (aOR = 0.29, 95% CI: 0.11–0.79), and parainfluenza viruses (aOR = 0.16, 95% CI: 0.04–0.65). Conclusions: The post-pandemic landscape of paediatric influenza in Xi’an has undergone substantial reconfiguration, characterised by intensified activity, altered seasonality, and a marked shift in age distribution. The increased bacterial co-detection points out the potential for more severe respiratory co-infections. These findings highlight the importance of optimising vaccination timing and prompting school-aged-children-targeted immunisation programmes in the post-pandemic era. Full article

The coronavirus disease 2019 (COVID-19) pandemic in Tocantins, Brazil, exhibited distinct phases between 2020 and 2025, with high mortality concentrated in 2020–2021 and subsequent stabilization at residual levels. Using epidemiological data, statistical modeling, and genomic surveillance, we show that the crisis peaked in 2021, coinciding with the circulation of Gamma and Delta, when health system capacity was severely strained. From 2022 onwards, the spread of Omicron led to record incidence but proportionally low mortality, reflecting accumulated immunity, vaccination, and improved clinical management. Vaccination represented the turning point, reducing hospitalizations and deaths by over 90% and driving a clear decoupling between incidence and severity. Interrupted time-series and generalized additive model (GAM) analyses confirmed sustained reductions in transmission and severity associated with mass immunization. Genomic sequencing of 3941 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes identified 166 lineages and successive variant replacements, culminating in the predominance of LP.8.1.4 in 2025. To our knowledge, this is one of the few integrated, long-term analyses (2020–2025) combining epidemiological and genomic data, capturing the full succession of variants up to LP.8.1.4 and highlighting Tocantins as a strategic “variant corridor” linking Brazil’s North and Central-West regions. These findings underscore the dual role of vaccination and genomic surveillance in shaping the epidemic trajectory and the importance of sustaining both strategies to mitigate future health crises. Full article

Background: The COVID-19 pandemic profoundly disrupted healthcare systems worldwide, compelling rapid adaptation of clinical workflows and accelerating the integration of telemedicine. Objective: This study evaluates the implementation of telemedicine in neurosurgical outpatient care at a tertiary referral hospital in Madrid during the first epidemic wave (March–May 2020) and explores its long-term significance five years later. Methods: A retrospective observational analysis including 5175 neurosurgical outpatient consultations was conducted, comparing the first epidemic wave of COVID-19 (2070 teleconsultations) with the equivalent period in 2019 (3105 in-person visits). Demographic, clinical, and procedural data were analyzed, including six-month follow-up outcomes. Univariate and multivariate analyses were performed to identify factors associated with teleconsultation use and follow-up delay. Results: The total number of consultations decreased by 33% compared to the pre-pandemic year. In May 2020, teleconsultations represented more than 70% of all visits. Continuity of care was preserved (follow-up adherence >80%), and missed appointments declined to zero. Cranial and oncological pathologies were prioritized, while degenerative and benign cases were largely deferred. Teleconsultation independently predicted delayed six-month follow-up (aOR 1.9, 95% CI 1.3–2.8, p = 0.002) and a lower likelihood of surgical indication (aOR 0.4, 95% CI 0.2–0.7, p = 0.004). Despite these differences, remote care ensured accessibility, safety, and clinical continuity under extreme healthcare system strain. Five years perspective: In addition to these early outcomes, the study describes the sustained integration of telemedicine during the subsequent five years, illustrating how this model became permanently embedded in routine neurosurgical practice in this center. Conclusions: This study represents one of the earliest structured telemedicine experiences in Spanish neurosurgery. The rapid adaptation of the Hospital General Universitario Gregorio Marañón ensured care continuity during the pandemic and catalyzed the lasting adoption of hybrid models that enhance accessibility, safety, efficiency, and healthcare system resilience. Full article

Background/Objectives: Local production is a global priority for increasing access to routine, outbreak, and pandemic vaccines and leads to a variety of direct and indirect benefits for countries. This study aimed to characterize the enabling environment for the sustainable production of influenza vaccines, including for epidemic and pandemic preparedness. Methods: National/local vaccine manufacturers were surveyed to capture data on influenza vaccine market contributions, government support for local production, and involvement in national pandemic preparedness activities. Using a conceptual framework for sustainable local production of influenza vaccines for epidemic and pandemic preparedness, manufacturers described 41 global/regional, national, and institutional sustainability factors across policy, health system, research and development (R&D), and regulatory thematic domains. In addition to the survey, key findings from country-level sustainability assessments of vaccine production in Bangladesh, Brazil, Indonesia, Serbia, and Viet Nam were analyzed to complement survey results. Results: This study included 12 participants representing 11 manufacturers from 10 countries. Of the 11 manufacturers, six reported that their countries have policies that support local production, but most manufacturers reported benefiting from some level of direct or indirect support by the government. Manufacturers considered 40/41 factors as important for sustainable production of influenza vaccines, and among the four domains, influenza prevention and control policies, influenza burden data, quality management, and regulatory filing capacity ranked highly. Additionally, manufacturers ranked factors related to cohesive policies for local production promotion and business/strategic planning at the manufacturer level as the top sustainability factors. Conclusions: Manufacturers broadly agreed on the importance of cohesive policies, evidence-based public health priorities, robust R&D and manufacturing investments, and regulatory readiness, though perceptions varied across contexts and company characteristics. Sustainable local production of influenza vaccines should be driven by the alignment of policies, investments, and demand. Full article

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

Background and Objectives: Computer Vision Syndrome (CVS) has become a major health problem among university students as a result of extensive electronic device use, but there is limited in-depth risk factor analysis by academic disciplines. The purpose of this study was to determine CVS prevalence, identify risk-associated factors, and investigate discipline-specific differences among university students. Methods: A cross-sectional study was conducted at Jazan University among 427 students of six academic disciplines between 2023 and 2024. Questionnaires validated by collecting demographics, electronic device usage patterns, eye care practices, and CVS symptoms were used to assess the data. Statistical analyses involved chi-square tests and multivariable logistic regression with significance at p < 0.05. Results: Prevalence of CVS was at epidemic proportions at 89.7% (95% CI: 86.8–92.6%), which was much higher than global averages. Considerable inter-disciplinary heterogeneity occurred, from 95.3% in Computer Science to 75.4% in Arts and Humanities students. A strong dose–response gradient was found for duration of device use: 3–4 h (OR = 4.13, 95% CI: 1.13–5.57), 5–6 h (OR = 5.31, 95% CI: 1.46–9.86), and ≥7 h per day (OR = 6.25, 95% CI: 1.74–8.01) versus 1–2 h use. Students >24 years old demonstrated a very high risk (OR = 9.73, 95% CI: 1.53–19.65). Headaches were the most common symptom (68.0%), and adoption of protective measures was low. Conclusions: This work demonstrates epidemic-level prevalence of CVS with unequivocal dose–response relationships and discipline-specific risk patterns, offering evidence-based targets for immediate campus-wide interventions and identifying a vital post-pandemic public health challenge meriting immediate attention. Full article

Rapid and accurate diagnostics for influenza viruses are essential for preventing future epidemics. Surface-enhanced Raman spectroscopy (SERS) presents a promising alternative to conventional techniques, offering a rapid, cost-effective, and highly sensitive platform for influenza virus detection. It is a highly sensitive analytical technique that enables the detection of minute chemical substances through significant signal enhancement. It operates by illuminating a sample with a laser and analyzing the scattered light to generate a unique molecular Raman spectrum. The sensitivity of SERS is derived from its use of metal nanoparticles, which amplify the weak Raman signals, making it particularly effective for detecting low-concentration targets such as viruses. Avian influenza (AI) is a major threat to domestic poultry, leading to large-scale culling during outbreaks. It leads to economic losses globally and can also infect pigs and humans, potentially causing a pandemic. Migratory birds spread various strains, leading to the development of highly pathogenic viruses. Viral monitoring is crucial for prevention strategies and understanding the virus evolution. This review outlines the challenges in detecting AI virus in chickens and critically assesses the established and emerging diagnostic technologies, with a specific focus on the factors influencing detection and recent advances in SERS-based AI detection. Ultimately, this review aims to provide insights that will assist the influenza research community in developing novel strategies for monitoring and preventing AI outbreaks in chickens and mitigating zoonotic transmission. Full article

The COVID-19 pandemic has had a diverse impact worldwide, affecting all strata of society. This article examines the relationship between health system adaptation and socioeconomic inequality in countries and the WHO Eastern Mediterranean Region (WHO-EMR), and we suggest that the dynamics among government response, health system preparedness, and epidemic spread are calibrated by the present socioeconomic inequality. With the use of a panel dataset spanning February 2020 to March 2021 and both linear (PARDL) and nonlinear (PNARDL) estimation techniques, we find that more socioeconomically vulnerable regions were disproportionately hit by the efforts of the pandemic, even in the presence of containment measures. From our findings, we find that health system capacity measures, such as hospital bed density and primary healthcare expenditure, are positively related to long-term economic resilience, while antimicrobial drug resistance is strongly negatively related to it. The study emphasizes the need for selective policy interventions to protect the most disadvantaged groups, a finding of relevance for other high-inequality low- and middle-income countries. Full article

Background: The early months of the COVID-19 pandemic were characterized by high transmission rates and mortality, compounded by the emergence of multiple SARS-CoV-2 lineages, including Variants of Concern (VOCs). This study investigates the phylodynamic and spatio-temporal trends of VOCs during the peak of the pandemic in Nigeria. Methods: Whole-genome sequencing (WGS) data from three major VOCs circulating in Nigeria, B.1.1.7 (Alpha), B.1.617.2 (Delta), and B.1.1.529 (Omicron), were analyzed using tools such as Nextclade, R Studio v 4.2.3, and BEAST X v 10.5.0. The spatial distribution, evolutionary history, viral ancestral introductions, and geographic dispersal patterns were characterized. Results: Three major lineages following WHO nomenclature were identified: Alpha, Delta, and Omicron. The Delta variant exhibited the widest geographic spread, detected in 14 states, while the Alpha variant was the least distributed, identified in only eight states but present across most epidemiological weeks studied. Evolutionary rates varied slightly, with Alpha exhibiting the slowest rate (2.66 × 10⁻⁴ substitutions/site/year). Viral population analyses showed distinct patterns: Omicron sustained elevated population growth over time, while Delta declined after initial expansion. The earliest Times to Most Recent Common Ancestor (TMRCA) were consistent with the earliest outbreaks of SARS-CoV-2 globally. Geographic transmission analysis indicated a predominant coastal-to-inland spread for all variants, with Omicron showing the most diffuse dispersal, highlighting commercial routes as significant drivers of viral diffusion. Conclusion: The SARS-CoV-2 epidemic in Nigeria was characterized by multiple variant introductions and a dominant coastal-to-inland spread, emphasizing that despite lockdown measures, commercial trade routes played a critical role in viral dissemination. These findings provide insights into pandemic control strategies and future outbreak preparedness. Full article

Influenza viruses, featured by high variability, pose a persistent public health threat because of an annual seasonal epidemic in the world and irregular global pandemic, requiring animal models to elucidate their pathogenic mechanisms and advance preventive strategies. Mice have been selected as the primary animal model, although several experimental animals have been used in studies of the influenza virus. However, the limited susceptibility of wild-type influenza viruses to mice poses significant challenges for studying pathogenesis and intervention strategies. Here, to help understand the construction of mouse-adapted influenza viruses, we reviewed the recent research progress in constructing mouse-adapted influenza virus strains to overcome species-specific barriers. Full article

Population mobility links cities, propelling the spatiotemporal spread of urban pandemics and adding complexity to disease dynamics. It also closely shapes, and is shaped by, the selection and intensity of intervention measures. Revealing the multistage spatial-temporal dynamics of cross-regional epidemic continuity under this interaction is often overlooked but critically important. This study innovatively applies a self-organizing map (SOM) neural network to classify cities into six distinct types based on population mobility characteristics: high-inflow core (HIC), low-inflow core (LIC), low-inflow sub-core (LISC), high-outflow semi-peripheral (HOSP), equilibrious semi-peripheral (ESP), and low-outflow peripheral (LOP). Building on this, we propose a novel SEIR-AHQ theoretical framework and construct an epidemiological model using network-coupled ordinary differential equations (ODEs). This model captures the dynamic interplay between inter-city population mobility and intervention measures, and quantifies how heterogeneous city types shape the evolution of epidemic transmission across the coupled mobility network. The results show that: (1) Cities with stronger population mobility face significantly higher infection risks and longer epidemic durations, characterized by “higher peaks and longer tails” in infection curves. HIC cities experience the greatest challenges, and LOP cities experience the least. (2) Both higher transmission rates and delayed intervention timings lead to exponential growth in infections, with nonlinear effects amplifying small changes disproportionately. (3) Intervention efficacy follows a “diminishing marginal returns” pattern, where the incremental benefits of increasing intervention intensity gradually decrease. This study offers a novel perspective on managing interregional epidemics, providing actionable insights for crafting tailored and effective epidemic response strategies. Full article