Search Results (1,606)

Accurate influenza forecasting is essential for public health emergency preparedness and timely resource allocation. Although meteorological factors are established modulators of influenza transmission, existing deep-learning models rarely exploit this physical knowledge in a principled way. We introduce MeteoGST, a meteorology-driven spatiotemporal graph learning framework that combines (i) multi-scale feature extraction across seven operational meteorological variables (T_max, T_min, DTR, absolute humidity q, relative humidity RH, surface-pressure anomaly p_anom, and 10 m wind speed U₁₀) via parallel dilated convolutions, TCN, and Transformer branches; (ii) a meteorology-aware dynamic graph attention network (MeteoGAT) whose edges blend geographic adjacency with time-varying meteorological similarity through a learned gate α; (iii) residual-trend decomposition with a peak-aware composite loss; and (iv) anti-smoothing meta-learning adaptation. On a 34-city pre-COVID-19 benchmark (2018–2019), MeteoGST achieves an RMSE of 0.65/0.85/1.12, MAE of 0.48/0.63/0.82, R² of 0.84/0.76/0.70, and Peak F1 of 0.72/0.65/0.59 at 7-, 14-, and 30-day horizons, respectively—improvements of 4–6% over the strongest GNN baseline (MPNN-LSTM) and 31–44% over classical baselines (ARIMA/LSTM). Under a 2021–2022 distribution-shift stress test, the model retains its ranking at 1- and 4-week horizons (PCC 0.78/0.62) and degrades gracefully at 8 weeks, demonstrating robustness beyond the training distribution. MeteoGST offers an operationally deployable tool (MeteoGST-Lite: ≈1 ms per city-week on a laptop-class CPU) for integrated meteorology-aware influenza surveillance. Full article

The COVID-19 pandemic accelerated the use of telemedicine and revealed persistent barriers in rural primary healthcare, especially among older adults and caregivers. This study proposes an LLM-mediated smart tele-primary-care framework to support respiratory infection monitoring in underserved communities of Córdoba, Colombia. The framework is based on caregiver-centered teleconsultation, remote monitoring, preventive education, structured symptom reporting, risk-based teletriage, and clinician-supervised digital support. The LLM functions as a controlled conversational interface to collect symptoms, organize patient information, reinforce health education, generate follow-up reminders, and identify predefined warning signs, without replacing clinical judgment or making autonomous diagnostic or treatment decisions. A preliminary scenario-based assessment examined whether the proposed workflow supports coherent triage and appropriate escalation of high-risk cases. Importantly, the described architecture—a telematic system for intelligent-engine access in assisted medicine support (ATIMAMS)—has been implemented and is currently operational at the Systems Engineering Program, Universidad del Sinú (Montería, Colombia), providing intelligent decision support for telemedicine consultations and remote assistance appointments in the study region. Overall, the study presents a context-sensitive, low-barrier, and safety-aware model for strengthening rural primary care, improving continuity of care, and supporting caregiver-mediated respiratory infection monitoring. Full article

Migrant agricultural workers (MAWs), predominantly individuals from countries in the Global South, play a vital role in maintaining Canadian food security. Employed in low-income positions across the country, they often face workplace hazards and numerous occupational health and safety (OHS) challenges related to long working hours, limited access to OHS information, gaps in knowledge, structural power imbalances with employers, and language and cultural barriers. This pilot study explored the knowledge, attitudes, and perceptions of OHS issues among MAWs in Southern and Eastern Ontario during the COVID-19 pandemic. Participants completed survey questions on working conditions, OHS hazards, and living conditions. A total of 93 questionnaires were completed, 55 by Spanish-speaking and 38 by English-speaking individuals, with 91% of respondents identifying as male. Several participants reported awareness of positive COVID-19 cases in their workplaces, and some indicated having experienced OHS-related illnesses or injuries but felt uncomfortable reporting them to supervisors. Knowledge of OHS rights varied, with a notable minority uncertain about their entitlement to sick leave. The findings indicate a need for further research and targeted interventions to strengthen health and safety practices among MAWs and to ensure they are informed, protected, and supported in exercising their workplace rights. Full article

Background: Missed nursing care related to infection prevention and control compromises patient safety and reflects clinical practice gaps and organizational constraints. The COVID-19 pandemic intensified awareness of infection-control practices; however, whether this translated into sustained reductions remains unclear. Aim: To compare missed infection-control nursing care between the pre-/early-pandemic period (2019–2020) and the post-pandemic period (2026) and identify persistent omissions with implications for healthcare policy and management. Methods: A descriptive study was conducted among 1570 nurses, including 774 participants in Group A (2019–2020) and 796 in Group B (2026). Data were collected online using the Missed Infection Control Nursing Care Questionnaire, tested for reliability and validity in Greek. Data were analyzed using SPSS 25.0, with statistical significance set at α = 0.05. Results: Item-level analysis showed lower mean omission scores in 34/37 infection-control nursing care practices, with 26 statistically significant reductions. The largest decreases were observed for glove use during antibiotic preparation/administration (1.493 to 1.070), hand hygiene before medication administration (1.340 to 0.962), multidrug-resistant organism (MDRO) admission screening (1.849 to 1.481), and intravenous access hub disinfection (1.978 to 1.668). In 2026, key residual omissions involved urinary catheter care (31.2%), hub disinfection (33.2%), oral hygiene (30.9%), and environmental hygiene before meals (29.1%). Conclusions: Missed infection-control nursing care declined in the post-pandemic period, but system-dependent omissions persisted, highlighting the need for staffing adequacy, balanced workload allocation, environmental support, and routine integration of infection-prevention practices. Full article

Background: Influenza viruses can cause mild to severe illnesses. The burden of disease varies widely depending on multiple factors, including the type and subtype of circulating viruses, timing of the season, flu vaccine efficacy and vaccination rates. Influenza viruses are also highly prone to genetic change and rapid spread due to modern human movement patterns, making influenza surveillance vital for public health awareness, guidance, policy, disease mitigation, and annual recommendations on vaccine composition. Methods: A network of three National Influenza Reference Centers (NIRCs) was established in the United States more than 10 years ago to support the Centers for Disease Control and Prevention’s (CDC) Influenza Division with its national influenza surveillance efforts. Located in California, New York, and Wisconsin, they are funded by CDC via a collaborative agreement with the Association of Public Health Laboratories (APHL). The role of the NIRCs is critical to national and global influenza surveillance, providing rapid information on circulating influenza strains from three arms of laboratory testing: (1) the virus isolation project (VIP), (2) next-generation sequencing (NGS), and (3) anti-viral drug resistance testing. Results: Here, we review the data generated in the VIP lab of the New York State (NYS) NIRC before, during, and after the COVID-19 pandemic and discuss its utility in an understanding of disease dynamics and viral evolution, as well as public health policy and decision making during this historic period in health care. Conclusion: Continued preparedness and surveillance are critical to mitigating the impact of evolving influenza viruses. Full article

Medical image segmentation is critical for clinical diagnosis, yet existing methods face a persistent trade-off: CNN-based approaches are constrained by local receptive fields, while Transformer-based methods suffer from semantic dilution when modeling global context. To address these limitations, we propose SCAGC-UNet, a region-aware graph convolutional network that bridges local detail extraction and global dependency modeling through structured region-level reasoning. The architecture features a dual-layer residual encoder for hierarchical feature extraction and a Spatial-Channel Graph Convolution (SC-GCN) module at the bottleneck, which simultaneously captures inter-region spatial topology and intra-region channel semantics via dual-branch graph inference. Feature refinement in the decoder is further enhanced by Context-Corrected Modules and Backward-Aided Modules to reduce the semantic gap across skip connections. We validate SCAGC-UNet on three public benchmarks covering distinct imaging challenges. On Kvasir-SEG, the model achieves a Dice score of 92.28% and MIOU of 92.41%, surpassing the strongest CNN-based baseline CCBANet by 0.73% in DSC and outperforming TransUNet by 11.76% in DSC. On BUSI, it attains an IOU of 78.10% and MIOU of 87.68%, outperforming UNet by 2.82% in IOU and TransUNet by 6.91% in DSC. On COVID-19 CT, it achieves a DSC of 82.51%, surpassing UNet by 4.99% and TransUNet by 7.47%, demonstrating robust performance on irregular lesion morphologies. These results confirm that SCAGC-UNet achieves consistent and robust segmentation performance across three public benchmark datasets spanning distinct imaging modalities, suggesting its potential clinical relevance. Full article

We propose a family of spectral hypergraph algorithms for early detection of connectivity collapse in pharmaceutical supply chain networks. The Fiedler eigenvalue λ₂ of the normalised hypergraph Laplacian serves as the order parameter. Five geometry-aware early warning indicators (TSI, HSST, HOMFA, HOTV, ORC) monitor network topology rather than scalar residuals, with provable detection guarantees under geometric ergodicity. A Greedy Dejamming algorithm restores connectivity via rank-2 Laplacian updates, achieving a (1 − 1/e)-approximation within a procurement budget constraint. Monte Carlo validation on a calibrated pharmaceutical distribution hypergraph demonstrates substantially higher detection sensitivity and shorter lead times than classical statistical process control. Hyperedge representation yields detection gains exceeding 90% for simultaneous multi-party failures that pairwise graph projections miss entirely. A COVID-19 lockdown episode provides a held-out directional consistency check. Full article

Background: COVID-19 has been linked to musculoskeletal complications, including femoral head avascular necrosis (AVN). Both COVID-19–related hypercoagulability and corticosteroid therapy have been proposed as contributing factors. This systematic review synthesizes current evidence on the occurrence, clinical characteristics, timing, and risk factors for femoral head AVN following COVID-19. Methods: A PRISMA-compliant systematic search of PubMed, Embase, and Scopus identified observational studies and case series (≥10 patients) reporting femoral head AVN in adults or adolescents with confirmed COVID-19. Data on epidemiology, symptom onset, imaging findings, and corticosteroid exposure were narratively synthesized due to heterogeneity. Results: Fifteen eligible studies described patients with post-COVID femoral head AVN. Symptom onset ranged from days to >12 months after infection. Early MRI often revealed asymptomatic or low-grade disease. Corticosteroid exposure was common and strongly associated with AVN severity; however, several studies reported AVN in patients without steroid use, whether this reflects an independent contribution of COVID-19 or unrecognized confounding cannot be determined from the available uncontrolled data. Higher cumulative steroid doses, severe pulmonary involvement, and elevated inflammatory markers were consistently linked to more advanced AVN stages. Conclusions: Femoral head AVN is an emerging post-COVID complication with variable timing and presentation. Corticosteroid exposure remains the principal risk factor; whether COVID-19 contributes independently of corticosteroids is unproven, and current evidence supports an association rather than a causal relationship. Awareness of this potential complication is warranted, although the role of early MRI screening remains to be established in prospective studies. Full article

Background/Objectives: Prognostication in ICU patients with documented coma or unresponsiveness is a high-stakes task that informs escalation of care, goals-of-care discussions, and family counselling. Conventional scores are often based on static snapshots and may not reflect early physiological evolution in heterogeneous real-world ICU populations. Routine arterial blood gases (ABG) and SpO₂ are repeatedly measured during early ICU care and may capture clinically meaningful trajectories that can be leveraged by explainable machine learning. To develop and internally validate exploratory, time-updated explainable machine-learning models for ICU outcome in ICU patients with clinically documented coma or unresponsiveness using routine ABG/SpO₂ measurements and physiological trajectories available at admission, 24 h, and 72 h, and to evaluate whether trajectory information adds prognostic information within a staged internal-validation framework. Methods: We conducted a retrospective single-centre study of 108 adult ICU patients with clinically documented coma or unresponsiveness. Predictors included demographics, comorbidity burden, COVID-19 status, baseline ABG/SpO₂ at ICU admission, inflammatory and coagulation biomarkers, and derived ABG/SpO₂ trajectory variables at 24 h and 72 h. Trajectory variables were defined as changes from admission to 24 h and to 72 h and were retained as missing when follow-up measurements were unavailable. The primary ICU-course outcome was ICU death versus transfer to ward. Three staged models were evaluated: Model A using baseline variables, Model B adding 24 h trajectory features, and Model C adding 72 h trajectory features. For each stage, models were analyzed with and without the derived respiratory_support index; models excluding respiratory_support were treated as the main interpretive analyses. Logistic regression, random forest, and gradient boosting (XGBoost) classifiers were assessed using repeated stratified 5-fold cross-validation with 20 repeats and aligned out-of-fold predictions. Performance was reported using AUC-ROC, precision–recall AUC, Brier score, and operating-point metrics; clinical utility was examined with decision-curve analysis. Model interpretation used SHAP and partial dependence plots. Robustness analyses included feature-exclusion sensitivity analysis for respiratory_support and a label-permutation sanity check. Results: ICU mortality was 65.7% (71/108). Follow-up ABG completeness was 75.9% at 24 h and 61.1% at 72 h. Because respiratory_support summarized the highest support level during the first 72 h and strongly separated outcome groups, models excluding respiratory_support were treated as the primary interpretive analyses. In the primary NoRS logistic-regression models, discrimination was moderate-to-strong, with AUC-ROC 0.822 for Model A_noRS, 0.848 for Model B_noRS, and 0.895 for Model C_noRS; bootstrap 95% confidence intervals were 0.739–0.897, 0.766–0.919, and 0.830–0.951, respectively. Measurement-availability sensitivity analyses and simple benchmark models were added to contextualize trajectory-related performance. Respiratory_support-enriched models were retained only as secondary severity-aware analyses, not as admission-only prediction models. Label permutation reduced discrimination toward chance (AUC ≈ 0.55). SHAP and partial-dependence analyses identified oxygenation variables, inflammatory burden, acid–base status, and ΔPaO₂ at 72 h as clinically coherent contributors to predicted risk; when included, respiratory_support dominated feature attribution, consistent with its role as an organ-support intensity marker. Conclusions: In ICU patients with clinically documented coma or unresponsiveness, explainable machine-learning models using routine ABG/SpO₂ trajectories within the first 72 h are feasible and may provide time-updated prognostic information, but the incremental value of trajectory-enriched models over simpler admission-only benchmarks remains unproven. Trajectory-enriched NoRS models retained meaningful discrimination after removing organ-support severity, suggesting a possible physiologically meaningful signal beyond support intensity alone, although definitive incremental value over parsimonious admission-only benchmarks was not established. These findings should be interpreted as exploratory and internally validated only; they do not establish a deployable ICU mortality score, do not demonstrate superiority over established ICU severity scores, and require external validation in larger multicentre cohorts before clinical deployment. Full article

Background: Secondary bacterial infections are increasingly recognized after coronavirus disease 2019 (COVID-19); however, bacterial abscess formation remains uncommon, and the simultaneous occurrence of brain and lung abscesses has not been previously reported. We report a rare case of Streptococcus intermedius infection presenting with multiple brain microabscesses and a lung abscess following COVID-19. Case Presentation: A 75-year-old man with no significant medical history except cholelithiasis experienced persistent fever following a diagnosis of COVID-19 and subsequently developed impaired consciousness 17 days later. Because bacterial meningitis was suspected, he was admitted to a neurology-specialized hospital on the same day. Brain MRI revealed more than 80 small enhancing lesions scattered throughout the brain parenchyma, consistent with multiple microabscesses. Chest CT demonstrated a mass-like lesion in the left lower lobe. Although cerebrospinal fluid cultures were negative, blood cultures obtained on admission yielded S. intermedius. Further investigation of the source of infection revealed moderate periodontitis, suggesting the oral cavity as the probable portal of entry. The patient was treated with intravenous antibiotics for eight weeks based on antimicrobial susceptibility testing, resulting in near-complete resolution of the lesions. Conclusions: Although a causal relationship between COVID-19 and abscess formation cannot be established, COVID-19-associated immune and mucosal barrier dysfunction may have contributed to the progression and dissemination of infection in this patient. Clinicians should be aware of the possibility of severe bacterial superinfection when fever or respiratory symptoms related to COVID-19 persist, even in patients without overt immunocompromise, particularly in those with pre-existing oral infections. Full article

Introduction: Inequities in access to medicines persist for asylum seekers, refugees, and undocumented migrants in Europe. For vaccines, access gaps not only exist for these groups in childhood routine immunization, but also for life-course and catch-up vaccinations. As part of a broader project examining access to medicines and vaccines for migrants across all stages of the migration cycle, this scoping review synthesizes evidence on the determinants of access to vaccines. Methods: We conducted a scoping review across PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Database of Systematic Reviews, Scopus, and grey literature sources, covering the period 2000–2024. Sources were eligible if they addressed access to vaccines among migrants. We examined access to vaccines along the life course, and across phases of the migratory cycle, including departure, transit, reception and settlement, and return or deportation. Results: A total of 47 research studies and grey literature reports were included. Most studies focused on migrants in reception and settlement (destination) settings, with only twelve sources addressing other phases of the migratory cycle. Across European countries, migrants were frequently reported to have lower uptake of routine vaccines (e.g., measles–mumps–rubella (MMR), polio, diphtheria–tetanus–pertussis (DTP), and human papillomavirus (HPV)) and COVID-19 vaccines than host populations. The most frequently reported barriers were related to migrants’ legal status, administrative requirements, and lack of documentation, alongside poor affordability of vaccination, limited awareness of their rights, and mistrust in the health system. Conclusions: Health systems need to adopt innovative approaches to expand vaccine access for migrant populations. Further, protecting confidentiality is essential for building trust and reducing ethical and legal risks. Flexible and coordinated vaccination strategies are required to address migrants’ mobility across the different migration stages and settings. Our findings appeal for sustained improvements in access to vaccines among migrants in Europe, contingent on strong policy commitments to equity, data protection, and the adoption of life-course and catch-up vaccination strategies. Full article

Public health emergencies require spatial crowdsourcing platforms to finish urgent tasks while limiting unnecessary movement across regions. Most online task assignment studies focus on profit, travel distance, latency, task coverage, or service quality. However, isolation sensitive scenarios need a different assignment goal. In such scenarios, regional crossings should be directly controlled during worker–task matching. This paper studies an isolation sensitive online task assignment problem in spatial crowdsourcing. The service space is modeled as a regional adjacency graph. The matching objective combines cross-region movement cost, an urgency reward for delayed task completion, and a dummy no-assignment cost for carry-over decisions. To handle dynamic arrivals, a time-sliced online process is used. Unfinished tasks are carried over to later time slots, and the priority of each carried-over task increases with waiting time. Based on this framework, we design two algorithms. OnlineKM serves as the basic priority-aware online matching algorithm. OnlineKM builds a matching problem in each time slot and applies KM-based partial matching with the information currently available. OnlineARC further uses δ-balanced adaptive regional coarsening. OnlineARC merges adjacent regions according to recent supply–demand balance before matching. This step adjusts the regional granularity used for movement cost evaluation and helps keep assignments close to local regions when regional merging is suitable. Experiments are conducted using a real-world task locations dataset from a 2022 COVID-19-related scenario in Changchun, with simulated worker availability and online arrivals. The results show that the proposed methods usually reduce the combined assignment objective value under the tested settings. The service quality and movement control metrics show that OnlineARC reduces the cross-region assignment ratio and average hop distance while maintaining a high task completion rate under the representative setting. OnlineKM improves running efficiency through time-sliced matching, while OnlineARC provides a trade-off between adaptive coarsening cost and locality-aware movement cost evaluation. These results suggest that adaptive regional coarsening can serve as a practical heuristic for locality-aware online task assignment in isolation sensitive spatial crowdsourcing under suitable worker–task distributions. Full article

COVID-19, caused by the SARS-CoV-2 virus, has become a global public health crisis with diverse clinical manifestations affecting multiple organ systems, including the integumentary system. One notable cutaneous manifestation, referred to as “COVID toes,” involves the development of pernio-like chilblains, characterized by red-to-violet macules, plaques, or nodules, primarily on toes and fingers. This characteristic clinical feature gained significant attention due to its apparent association with COVID-19, especially during the early stages of the pandemic when individuals with mild or asymptomatic cases exhibited these symptoms. Concurrently, digital platforms such as Google Trends have emerged as tools for tracking public interest in health-related topics, offering insights into real-time patterns of disease awareness. Previous research has demonstrated that Google Trends data may correlate with the incidence of infectious diseases, suggesting that search interest can be a proxy for disease outbreaks. In this study, we sought to explore the potential relationship between public interest in COVID toes, as reflected in Google Trends, and the incidence and mortality rates of COVID-19. Specifically, we examined whether peaks in search interest for “COVID toes” corresponded with surges in COVID-19 cases and deaths. By analyzing trends in search data, we aimed to assess the utility of digital platforms as an epidemiological tool for monitoring disease progression and public awareness. Our findings provide insights into the potential role of digital search data in forecasting outbreaks and highlight the interplay between public perception and the clinical burden of COVID-19, emphasizing the importance of real-time data in public health surveillance and response. Full article

Objectives: This study aims to assess the acceptance of vaccinations among pregnant women, particularly against influenza, pertussis, COVID-19, and RSV, and to identify factors influencing their willingness to get vaccinated. It also seeks to evaluate the impact of the COVID-19 pandemic on maternal attitudes and behaviors regarding vaccination. Methods: The analysis involved a review of existing literature and studies to evaluate the level of vaccine acceptance among pregnant women before and after the COVID-19 pandemic. Factors contributing to vaccine hesitancy, including misinformation, lack of knowledge, and the influence of healthcare professionals, were examined. Results: The findings indicated that, despite scientific evidence supporting the safety and efficacy of vaccines during pregnancy, public concerns remain about their impact on the developing fetus. The outbreak of the COVID-19 pandemic has increased awareness of the risk of infectious diseases, but at the same time, its impact on vaccination rates among pregnant women is ambiguous and geographically diverse. Misinformation and decreased access to healthcare during the pandemic negatively affected vaccine uptake. Trustworthy information provided by healthcare professionals emerged as a key factor in promoting vaccine acceptance. Conclusions: To improve vaccination rates among pregnant women, it is essential to provide clear, evidence-based information through healthcare professionals, particularly those directly caring for pregnant women. Educational campaigns should address concerns calmly and without judgment, emphasizing the safety and benefits of vaccinations. Enhanced access to healthcare and vaccinations, along with strategic information dissemination, can significantly improve vaccine acceptance during pregnancy. Lessons learned from past pandemics should be incorporated into the development of healthcare strategies aimed at implementing recommended vaccinations for pregnant women in the future. Full article

Research on post-COVID tourism behaviour has expanded rapidly, yet inland UNESCO World Heritage cities remain underexamined, particularly in Mediterranean contexts. This study examines whether the pandemic produced durable changes in tourist behaviour and in willingness to pay for sustainable services in Cáceres, Spain. A structured face-to-face survey was administered to 421 visitors in March 2023, after public-health restrictions had been lifted. The analysis covered self-reported behavioural change, perceived impacts on different destination types, perceived effects on local sustainability objectives and changes in willingness to pay (WTP) for sustainable services. Descriptive statistics were complemented by an exploratory binary logistic regression predicting increased WTP. Because the model includes only sociodemographic predictors and shows modest fit, it is used to describe associations rather than to predict. Reported behavioural change was limited: mean scores for crowd avoidance, health–safety preferences, shorter stays and substitution towards rural and nature tourism ranged from 1.73 to 1.91 on a five-point scale. Respondents nevertheless perceived substantial spatial effects of the pandemic, particularly on natural parks (92.6%) and rural destinations (84.1%). Most believed that the pandemic had accelerated sustainability efforts mainly through greater institutional and business awareness (54.9%). WTP proved relatively stable, with 62.7% reporting no change and 26.1% an increase. Women and respondents with university education showed higher odds of reporting increased WTP. Because constructs such as institutional trust and pro-environmental values were not measured directly, these attitudes are interpreted—rather than demonstrated—as reflecting governance-related confidence and value orientations more than lingering health concerns. This governance-and-values reading is the study’s main interpretive contribution and requires confirmation with direct measures of the underlying constructs. Full article