Search Results (2,274)

Background: Influenza viruses are major pathogens responsible for respiratory infections and pose significant risks to densely populated urban areas. RT-qPCR has made substantial contributions in controlling virus transmission during previous COVID-19 epidemics, but it faces challenges in terms of detection time for large sample sizes and susceptibility to nucleic acid contamination. Methods: Our study designed loop-mediated isothermal amplification primers for three common influenza viruses: A/H3N2, A/H1N1, and B/Victoria, and utilized a 4-channel microfluidic chip to achieve simultaneous detection. The chip initiates amplification by centrifugation and allows testing of up to eight samples at a time. Results: By creating a closed amplification system in the microfluidic chip, aerosol-induced nucleic acid contamination can be prevented through physically isolating the reaction from the operating environment. The chip can specifically detect A/H1N1, A/H3N2, and B/Victoria and has no signal for other common respiratory viruses. The testing process can be completed within 1 h and can be sensitive to viral RNA at concentrations as low as 10⁻³ ng/μL for A/H1N1 and A/H3N2 and 10⁻¹ ng/μL for B/Victori. A total of 296 virus swab samples were further analyzed using the microfluidic chip method and compared with the classical qPCR method, which resulted in high consistency. Conclusions: Our chip enables faster detection of influenza virus and avoids nucleic acid contamination, which is beneficial for POCT establishment and has lower requirements for the operating environment. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article

Non-polio enteroviruses continue to cause numerous epidemics world-wide that range from mild to severe disease, including acute flaccid paralysis, meningitis, severe respiratory infections and encephalitis. Using publicly available data we present a comprehensive global and regional temporal distribution of non-polio enteroviruses, with a focus on highly prevalent genotypes. We found that regional distribution did vary compared to global prevalence where the top prevalent genotypes included CVA6 and EV-A71 in Asia, EV-D68 in North America and CVA13 in Africa, while E-30 was prevalent in Europe, South America and Oceania. In 2020, the COVID-19 pandemic did interrupt non-polio enterovirus detections globally, and cases rebounded in subsequent years, albeit at lower prevalence and with decreased genotype diversity. Environmental surveillance for non-polio enteroviruses does occur and has been used in some regions as an early-warning system; however, further development is needed to effectively supplement potential gaps in clinical surveillance data. Overall, monitoring for non-polio enteroviruses is critical to identify true incidence, improve understanding of genotype circulation, provide an early warning system for emerging/re-emerging genotypes and allow for better outbreak control. Full article

In late 2019, a new virus, SARS-CoV-2, emerged in Wuhan, China, causing COVID-19 and the subsequent global pandemic. As of 30 April 2023, more than 774 million cases of COVID-19 had been reported worldwide, including over 7.5 million in Mexico. Despite advances in vaccination, epidemic surges of COVID-19 continued to occur globally, highlighting the importance of sharing and disseminating the experiences gained during these first years to better understand the virus’s evolution and respond accordingly. For this reason, the National Council for Science and Technology (CONACYT) organized the meeting “Challenges and Opportunities for Genomic Surveillance of SARS-CoV-2 in Mexico” from 15 to 17 August 2022, to present the efforts and results accumulated over more than two years of the pandemic. In this meeting report, we summarize the key findings of each participant and provide their contact information. Full article

Background/Objectives: Syphilis, a re-emerging global public health issue, has shown increasing incidence over the past decade, particularly among key populations such as men who have sex with men (MSM), people living with HIV, and pregnant women. This narrative review aimed to synthesize global epidemiological trends of syphilis from 2015 to 2025, with a focus on surveillance gaps, regional disparities, and structural determinants. Methods: A broad narrative approach was used to collect and analyze epidemiological data from 2015 to 2025. The literature was retrieved from databases (PubMed, Scopus) and official reports from the WHO, CDC, and ECDC. Included materials span observational studies, surveillance reports, and modeling data relevant to global trends and public health responses. Results: Globally, syphilis incidence has increased, with notable surges in North America, Europe, and Asia. MSM remain disproportionately affected, while congenital syphilis is resurging even in high-income countries. Low- and middle-income countries report persistent burdens, especially among women of reproductive age, often exacerbated by limited screening and surveillance infrastructure. The COVID-19 pandemic disrupted syphilis-related services and further exacerbated underreporting, hindering timely detection and response efforts. Surveillance systems vary widely in their completeness and quality, which significantly hinders global data comparability and coordinated public health responses. Conclusions: Despite its curability, syphilis continues to spread due to fragmented prevention strategies, inequities in access to care, and insufficient surveillance. Strengthening diagnostic access, integrating prevention efforts into broader health systems, and addressing social determinants are essential. Improved surveillance, equitable access, and innovation—including diagnostics and potential vaccine research—are critical to controlling the global syphilis epidemic. Full article

COVID-19 vaccine hesitancy in Pakistan is a barrier to optimal vaccine uptake and has been situated within a context of hesitancy towards other vaccines. A mixed-methods study was conducted during the initial COVID-19 vaccine roll-out in 2021 in four union councils in Peshawar, consisting of a cross-sectional survey, eight focus group discussions (FGDs) with community members and eight in-depth interviews with healthcare workers (HCWs) to assess perceptions toward vaccines. Multivariable logistic regression was used to assess factors associated with COVID-19 vaccine hesitancy. Of 400 survey participants, 57.3% were vaccine acceptant and 42.8% vaccine hesitant. Just over half (56.8%) perceived COVID-19 vaccines to be safe. Most (88%) reported trust in HCWs to provide accurate vaccine information. FGDs revealed that women received less information about the vaccine compared to men and cultural restrictions were barriers even for those willing to be vaccinated. Correlates of vaccine acceptance included male sex (aOR 2.25; 95% CI 1.29–3.91), age 50 years or greater (aOR 1.74; 95% CI 1.19–6.31), social network support (e.g., vaccine acceptance among an individual’s social network) in receiving COVID-19 vaccines (aOR 2.38; 95% CI 1.45–3.89), community concern about COVID-19 spread (aOR 2.84; 95% CI 1.73–4.66), and trust in HCWs to provide vaccine information (aOR 3.47; 95% CI 1.62–7.42). Future vaccine promotion should prioritize engaging community leaders, sharing transparent information, combatting misinformation and rumors, and implementing household-based interventions especially targeting the importance of vaccination among women and young people to increase uptake. Full article

The 100-Day Mission, coordinated by the Coalition for Epidemic Preparedness Innovations (CEPI) and endorsed by significant international stakeholders, aims to shorten the timeframe for developing and implementing vaccines to 100 days after the report of a new pathogen. This ambitious goal is outlined as an essential first step in improving pandemic preparedness worldwide. This review highlights the mission’s implementation potential and challenges by examining it through the lens of low- and middle-income countries (LMICs), which often face barriers to equitable vaccine access. This article explores the scientific, economic, political, and social aspects that could influence the mission’s success, relying on lessons learned from previous pandemics, such as the Spanish flu, H1N1, and COVID-19. We also examined important cornerstones like prototype vaccine libraries, accelerated clinical trial preparedness, early biomarkers identification, scalable manufacturing capabilities, and rapid pathogen characterization. The review also explores the World Health Organization (WHO) Pandemic Agreement and the significance of Phase 4 surveillance in ensuring vaccine safety. We additionally evaluate societal issues that disproportionately impact LMICs, like vaccine reluctance, health literacy gaps, and digital access limitations. Without intentional attempts to incorporate under-resourced regions into global preparedness frameworks, we argue that the 100-Day Mission carries the risk of exacerbating already-existing disparities. Ultimately, our analysis emphasizes that success will not only rely on a scientific innovation but also on sustained international collaboration, transparent governance, and equitable funding that prioritizes inclusion from the beginning. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

In recent years, the open data initiative has led to the willingness of many governments, researchers, and organizations to share their data and make it publicly available. Healthcare, disease, and epidemiological data, such as privacy statistics on patients who have suffered from epidemic diseases such as the Coronavirus disease 2019 (COVID-19), are examples of open big data. Therefore, huge volumes of valuable data have been generated and collected at high speed from a wide variety of rich data sources. Analyzing these open big data can be of social benefit. For example, people gain a better understanding of disease by analyzing and mining disease statistics, which can inspire them to participate in disease prevention, detection, control, and combat. Visual representation further improves data understanding and corresponding results for analysis and mining, as a picture is worth a thousand words. In this paper, we present a visual data science solution for the visualization and visual analysis of large sequence data. These ideas are illustrated by the visualization and visual analysis of sequences of real epidemiological data of COVID-19. Through our solution, we enable users to visualize the epidemiological data of COVID-19 over time. It also allows people to visually analyze data and discover relationships between popular features associated with COVID-19 cases. The effectiveness of our visual data science solution in improving the user experience of visualization and visual analysis of large sequence data is demonstrated by the real-life evaluation of these sequenced epidemiological data of COVID-19. Full article

Canada’s national surveillance shows an 11% year-over-year decline in deaths from opioid and other unregulated drug poisonings, and a 10% drop in related hospitalisations in 2024. In stark contrast, Québec, home to more than nine million residents, and Montréal, the country’s second-largest city, experienced a continued rise in suspected drug-poisoning mortality through 2024, with fentanyl or analogues detected in almost two-thirds of opioid deaths. We conducted a narrative synthesis of provincial coroner and public-health surveillance tables, Health Canada dashboards, and the 2022–2025 Québec Strategy on Psychoactive-Substance Overdose Prevention. Results indicate a 40% increase in opioid-related mortality since 2018, a parallel uptick in stimulant toxicity, and a five-fold rise in overdose reversals at Montréal supervised-consumption services during the COVID-19 pandemic recovery. We aim to summarise the key problems underlying this epidemic and offer province-specific public-health strategies while also sending a call to action for first-line clinicians and psychiatrists to integrate overdose-risk screening, take-home naloxone, and stimulant-use-disorder treatments into routine care. We further urge Québec healthcare professionals to deepen their knowledge of provincial services such as supervised-injection sites and stay up to date with the rapidly evolving substance-use-prevention literature. Québec’s divergent trajectory underscores the need for region-tailored harm-reduction investments and stronger policy-to-clinic feedback loops to reduce preventable deaths. Full article

Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. We developed a semi-automated evaluation framework within a clinical decision support system (CDSS-EQCM) that integrates LIME and SHAP explanations with multi-criteria decision-making (TOPSIS and Borda count) to rank model interpretability. After two-phase preprocessing of 2934 COVID-19 patient records spanning four epidemic waves, we applied five classifiers (Random Forest, Decision Tree, Logistic Regression, k-NN, SVM). Five infectious disease physicians used a Streamlit interface to generate patient-specific explanations and rate models on accuracy, separability, stability, response time, understandability, and user experience. Random Forest combined with SHAP consistently achieved the highest rankings in Borda count. Clinicians reported reduced evaluation time, enhanced explanation clarity, and increased confidence in model outputs. These results demonstrate that CDSS-EQCM can effectively streamline interpretability assessment and support clinician decision-making in medical diagnostics. Future work will focus on deeper electronic medical record integration and interactive parameter tuning to further enhance real-time diagnostic support. Full article

Background/Objectives: Respiratory syncytial virus (RSV) is a leading cause of respiratory infections across all age groups, with the greatest burden observed in young children and older adults. The COVID-19 pandemic significantly disrupted RSV circulation, resulting in an immunity gap and altered transmission dynamics. This study aimed to assess the seroprevalence of anti-RSV IgG antibodies in the Polish population during the 2023/2024 epidemic season. To our knowledge, this is the first study to characterize RSV seroprevalence at the population level in Poland. Methods: A total of 700 serum samples from individuals across different age groups were analyzed using a commercial assay to detect anti-RSV IgG antibodies. Seroprevalence and antibody levels, expressed as the index of positivity (IP), were examined by age and sex. Results: The overall seroprevalence of anti-RSV IgG antibodies was 91.4%. Antibody positivity increased markedly from 35.5% in infants aged 0–1 years to over 90% in children aged 4–5 years, reaching nearly universal levels in older age groups, including 99.1% in adults aged ≥60 years. Median IP values also rose with age, peaking in individuals aged ≥60 years. No significant differences in seroprevalence were observed between sexes, though older men showed slightly higher median IP values, potentially reflecting greater cumulative RSV exposure. Conclusions: This study provides key insights into the post-pandemic landscape of RSV immunity in Poland. The high seroprevalence across most age groups underscores widespread prior exposure, while the lower rates in infants highlight a continued vulnerability. These findings support the development and implementation of targeted immunization strategies, particularly for infants and older adults. Full article

Background/Objectives: The COVID-19 pandemic highlighted the critical need for accurate predictive models to guide public health interventions and optimize healthcare resource allocation. This study evaluates how the complexity of compartmental infectious disease models influences their forecasting accuracy and utility for pandemic resource planning. Methods: We analyzed a range of compartmental models, including simple susceptible-infected-recovered (SIR) models and more complex frameworks incorporating asymptomatic carriers and deaths. These models were calibrated and tested using real-world COVID-19 data from the United States to assess their performance in predicting symptomatic and asymptomatic infection counts, peak infection timing, and resource demands. Both adaptive models (updating parameters with real-time data) and non-adaptive models were evaluated. Results: Numerical results show that while more complex models capture detailed disease dynamics, simpler models often yield better forecast accuracy, especially during early pandemic stages or when predicting peak infection periods. Adaptive models provided the most accurate short-term forecasts but required substantial computational resources, making them less practical for long-term planning. Non-adaptive models produced stable long-term forecasts useful for strategic resource allocation, such as hospital bed and ICU planning. Conclusions: Model selection should align with the pandemic stage and decision-making horizon. Simpler models are effective for rapid early-stage interventions, adaptive models excel in short-term operational forecasting, and non-adaptive models remain valuable for long-term resource planning. These findings can inform policymakers on selecting appropriate modeling approaches to improve pandemic response effectiveness. Full article

Background/Objectives: The COVID-19 pandemic significantly threatened cancer patients and oncologic care. The rollout of vaccines emerged as a critical milestone, despite the initial lack of evidence regarding their safety and efficacy in this population. This systematic review and meta-analysis evaluate the current evidence on COVID-19 vaccination in patients undergoing radiotherapy (RT). Methods: PubMed, Livivo, Scopus, and Cochrane Library were systematically reviewed for relevant publications on COVID-19 vaccination in the context of radiation oncology, published by 19 April 2024. The treatment effects were calculated as the proportion of seroconverted individuals. Results: A total of 22 studies published between 2021 and 2024 were included, covering various aspects of vaccination, including safety, tolerability, qualitative and quantitative humoral responses, cellular responses, vaccination efficacy, and booster vaccinations. Notably, patients undergoing RT exhibited a high willingness to receive vaccination. Vaccination was overall well tolerated and safe, with a low incidence of side effects, which were primarily mild. The primary meta-analysis showed a seroconversion proportion of 91% [95% CI: 84–96%] overall, with a somewhat higher proportion of 93% in patients receiving RT alone, compared to 90% in patients receiving either RT or RT combined with chemotherapy. Furthermore, immunization during RT led to a sustained increase in antibody titers, with a notable long-term persistence of IgG. Conclusions: COVID-19 vaccines demonstrate excellent safety, immunogenicity, and efficacy in patients receiving RT, who also exhibit a high willingness to be vaccinated. The outcomes observed are comparable to those in healthy controls and superior to those seen in patients receiving other cancer treatments, such as chemotherapy. The vaccination of radiation oncology patients in future pandemics or epidemics is strongly advocated even during active treatment. Full article

Aim. This study aimed to assess the SARS-CoV-2 herd immunity in the Republic of Armenia (RA) by late 2022. Materials and Methods. A randomized study was conducted from 28 November to 2 December (2022) by the Saint Petersburg Pasteur Institute (Russia) in collaboration with the National Center for Disease Control and Prevention (Armenia). This study was approved by the ethics committees at both organizations. A volunteer cohort (N = 2974) was formed and grouped by participant age, region, or activity. Antibodies (Abs) to viral nucleocapsid antigen (Nc) and receptor-binding domain (RBD) in plasma were determined by ELISA. The statistical significance of differences was calculated using a p < 0.05 threshold, unless noted. Results. At the end of 2022, estimated SARS-CoV-2 seroprevalence (Nc and/or RBD Abs) among the Armenian population was 99% (95%CI: 98.5–99.3). It was evenly distributed throughout the cohort without any significant differences by age, region, or activity. Volunteers with low (32–124 BAU/mL) or medium (125–332 BAU/mL) anti-Nc Ab levels prevailed: 32.4% (95%CI: 30.7–34.1) and 25.5% (95% CI: 24.0–27.1), respectively. Regarding anti-RBD Abs, maximum levels (>450 BAU/mL) were detected in 40% of children. The share of individuals with high anti-RBD Abs levels increased with age, reaching 65% among those aged 70⁺ years. The important contribution to the formation of herd immunity to coronavirus infection was made by vaccination in the preceding period (1 April 2021 to 1 May 2022). The contribution from individuals with post-vaccination immunity was estimated to be above 80%. Hybrid immunity, formed after vaccination of those who had earlier experienced COVID-19, was characterized by greater effectiveness than post-vaccination immunity alone. Conclusions. Within the context of mass prophylactic vaccination, effective herd immunity to SARS-CoV-2 was formed, which helped to stop epidemic spread in the Republic. Full article