Search Results (831)

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

Background/Objectives: Mucosal vaccines, delivered intranasally or via inhalation, are being studied for respiratory infectious diseases like COVID-19 and influenza. These vaccines aim to provide non-invasive administration and strong immune responses at infection sites, making them a promising area of research. This systematic review and meta-analysis assessed their immunogenicity, safety, and protective efficacy. Methods: The study design was a systematic review and meta-analysis, searching PubMed and Cochrane databases up to 30 May 2025. Inclusion criteria followed the PICOS framework, focusing on mucosal vaccines for COVID-19, influenza, RSV, pertussis, and tuberculosis. Results: A total of 65 studies with 229,614 participants were included in the final analysis. Mucosal COVID-19 vaccines elicited higher neutralizing antibodies compared to intramuscular vaccines (SMD = 2.48, 95% CI: 2.17–2.78 for wild-type; SMD = 1.95, 95% CI: 1.32–2.58 for Omicron), with varying efficacy by route (inhaled VE = 47%, 95% CI: 22–74%; intranasal vaccine VE = 17%, 95% CI: 0–31%). Mucosal influenza vaccines protected children well (VE = 62%, 95% CI: 30–46%, I² = 17.1%), but seroconversion rates were lower than those of intramuscular vaccines. RSV and pertussis vaccines had high seroconversion rates (73% and 52%, respectively). Tuberculosis vaccines were reviewed systemically, exhibiting robust cellular immunogenicity. Safety was comparable to intramuscular vaccines or placebo, with no publication bias detected. Conclusions: Current evidence suggests mucosal vaccines are immunogenic, safe, and protective, particularly for respiratory diseases. This review provides insights for future research and vaccination strategies, though limitations include varying efficacy by route and study heterogeneity. Full article

This study examines the impact of free trade zone (FTZ) establishment on regional economic resilience (RER) in China, using provincial-level panel data spanning from 2010 to 2022 and a multi-period difference-in-differences (DID) approach. The empirical results indicate that FTZ implementation significantly enhances regional economic resilience by 3.46%, with the development of green finance acting as a key moderating mechanism that amplifies this positive effect. Heterogeneity analysis uncovers notable disparities across policy cohorts and geographical regions: the first wave of FTZs demonstrates the most pronounced resilience-enhancing impact, whereas later cohorts exhibit weaker or even adverse effects. Coastal regions experience substantial benefits from FTZ policies, in contrast to statistically insignificant outcomes observed in inland areas. These findings suggest that strategically expanding the FTZ network, when paired with tailored implementation mechanisms and the integration of green finance, could serve as a powerful policy tool for post-COVID economic recovery. Importantly, by strengthening economic resilience through institutional openness and green investment, this study offers valuable insights into balancing economic growth with environmental sustainability. It provides empirical evidence to support the optimization of FTZ spatial governance and institutional innovation pathways, thereby contributing to the pursuit of sustainable regional development. 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

This study assesses the effects of the COVID-19 pandemic on catastrophic health expenditures and income inequality in Benin. A microsimulation was calibrated to estimate the impact of the pandemic under three different shock scenarios: low, moderate, and severe. The analysis relies on secondary data from household living condition surveys. The results indicate that the COVID-19 crisis would lead to a significant average income loss of up to 20% and income inequality, while the number of households with catastrophic health expenditures would increase by 4%. More importantly, the findings reveal heterogeneous impacts across households, with urban residents, younger individuals, more educated households, and male-headed households experiencing the greatest income decline. These findings underscore the need for targeted health coverage and employment policies to better protect vulnerable populations in Benin in the face of future shocks. Full article

In recent years, the persistent emergence of novel infectious pathogens (epitomized by the global coronavirus disease-2019 (COVID-2019) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) has propelled nucleic acid testing (NAT) into an unprecedented phase of rapid development. As a key technology in modern molecular diagnostics, NAT achieves precise pathogen identification through specific nucleic acid sequence recognition, establishing itself as an indispensable diagnostic tool across diverse scenarios, including public health surveillance, clinical decision-making, and food safety control. The reliability of NAT systems fundamentally depends on reference materials (RMs) that authentically mimic the biological characteristics of natural viruses. This critical requirement reveals significant limitations of current RMs in the NAT area: naked nucleic acids lack the structural authenticity of viral particles and exhibit restricted applicability due to stability deficiencies, while inactivated viruses have biosafety risks and inter-batch heterogeneity. Notably, pseudovirus has emerged as a novel RM that integrates non-replicative viral vectors with target nucleic acid sequences. Demonstrating superior performance in mimicking authentic viral structure, biosafety, and stability compared to conventional RMs, the pseudovirus has garnered substantial attention. In this comprehensive review, we critically summarize the engineering strategies of pseudovirus platforms and their emerging role in ensuring the reliability of NAT systems. We also discuss future prospects for standardized pseudovirus RMs, addressing key challenges in scalability, stability, and clinical validation, aiming to provide guidance for optimizing pseudovirus design and practical implementation, thereby facilitating the continuous improvement and innovation of NAT technologies. Full article

People living with HIV (PLWH) faced diverse challenges during the COVID-19 pandemic, including disruptions to care, housing instability, emotional distress, and economic hardship. This study used graph-based clustering methods to analyze pandemic-era experiences of PLWH in a national sample from the NIH’s All of Us dataset (n = 242). Across three graph configurations we identified consistent subgroups shaped by social connectedness, housing stability, emotional well-being, and engagement with preventive behaviors. Comparison with an earlier local study of PLWH in Illinois confirmed recurring patterns of vulnerability and resilience while also revealing additional national-level subgroups not observed in the smaller sample. Subgroups with strong social or institutional ties were associated with greater emotional stability and proactive engagement with COVID-19 preventive behaviors, while those facing isolation and structural hardship exhibited elevated distress and limited engagement with COVID-19 preventive measures. These findings underscore the importance of precision public health strategies that reflect the heterogeneity of PLWH and suggest that strengthening social support networks, promoting housing stability, and leveraging institutional connections may enhance pandemic preparedness and HIV care in future public health crises. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article

Acute respiratory distress syndrome (ARDS) is an inflammatory pulmonary condition that remains at alarming rates of fatality, with neutrophils playing a vital role in its pathogenesis. Beyond their classical antimicrobial functions, neutrophils contribute to pulmonary injury via the release of reactive oxygen species, proteolytic enzymes, and neutrophil extracellular traps (NETs). To identify targets for treatment, it was found that epigenetic mechanisms, including histone modifications, hypomethylation, hypermethylation, and non-coding RNAs, regulate neutrophil phenotypic plasticity, survival, and inflammatory potential. It has been identified that neutrophils in ARDS patients exhibit abnormal methylation patterns and are associated with altered gene expression and prolonged neutrophil activation, thereby contributing to sustained inflammation. Histone citrullination, particularly via PAD4, facilitates NETosis, while histone acetylation status modulates chromatin accessibility and inflammatory gene expression. MicroRNAs have also been shown to regulate neutrophil activity, with miR-223 and miR-146a potentially being biomarkers and therapeutic targets. Neutrophil heterogeneity, as evidenced by distinct subsets such as low-density neutrophils (LDNs), varies across ARDS etiologies, including COVID-19. Single-cell RNA sequencing analyses, including the use of trajectory analysis, have revealed transcriptionally distinct neutrophil clusters with differential activation states. These studies support the use of epigenetic inhibitors, including PAD4, HDAC, and DNMT modulators, in therapeutic intervention. While the field has been enlightened with new findings, challenges in translational application remain an issue due to species differences, lack of stratification tools, and heterogeneity in ARDS presentation. This review describes how targeting neutrophil epigenetic regulators could help regulate hyperinflammation, making epigenetic modulation a promising area for precision therapeutics in ARDS. Full article

Background: Refractory ventricular fibrillation (RVF) is a life-threatening condition characterized by the persistence of ventricular fibrillation despite multiple defibrillation attempts. It represents a critical challenge in out-of-hospital cardiac arrest management, with poor survival outcomes and limited guidance from current resuscitation guidelines. In recent years, alternative defibrillation strategies (ADSs), including dual sequential external defibrillation (DSED) and vector change defibrillation (VCD), have emerged as potential interventions to improve defibrillation success and patient outcomes. However, their clinical utility remains debated due to heterogeneous evidence and limited high-quality data. Methods: This narrative review explores the current landscape of ADSs in patients with RVF. MEDLINE, Google Scholar, the World Health Organization, LitCovid NLM, EMBASE, CINAHL Plus, and the Cochrane Library were examined from their inception to April 2025. Results: The available literature is dominated by retrospective studies and case series, with only one randomized controlled trial (DOSE-VF). This trial demonstrated improved survival to hospital discharge with ADSs compared to standard defibrillation. DSED was associated with higher rates of return of spontaneous circulation and favorable neurological outcomes. However, subsequent meta-analyses have produced inconsistent results, largely due to the heterogeneity of the included studies. The absence of sex-, gender-, and ethnicity-specific analyses further limits the generalizability of the findings. In addition, practical barriers, such as equipment availability, pose significant challenges to implementation. Conclusions: ADSs represent a promising yet still-evolving approach to the management of RVF, with DSED showing the most consistent signal of benefit. Further high-quality research is required to enhance generalizability and generate more definitive, high-level evidence. Full article

Background/Objectives: In Germany, the provision of healthcare for post-COVID patients primarily lies with general practitioners (GPs), who often lack the necessary knowledge and skills. As part of the PostCovidCare pilot study (PCC), case management software incorporating a symptom diary was introduced and piloted in n = 10 GP practices with n = 33 included patients involved (September 2022–March 2023). This study aimed to explore patients’ experiences. Methods: Semi-structured telephone interviews were transcribed and analyzed using qualitative content analysis. A total of n = 10 patient interviews were conducted (July–September 2023). Results: Patients’ experiences were heterogeneous. The service was largely structured, involving an extensive initial assessment, follow-up appointments, questionnaires, and support from medical assistants, but technical problems with the symptom diary occurred. The GP consultation played a prominent role. Positive aspects included being actively asked about their symptoms, being given a lot of time, initiating diagnostic and therapeutic measures, and having a closer relationship with their GP. Negative aspects included the time taken, resulting exhaustion, duplication of efforts, and insufficient involvement in the consultation process. Conclusions: The pilot study conducted at an early stage of the post-COVID era demonstrated the basic feasibility of case management in primary care from patients’ perspectives. In addition, for future projects, it is important to integrate patients into the design from the outset, adapt the software to users’ needs, and consider care providers’ perspectives. Full article

Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version of our previous SARS-CoV-2 model formulated as input–output non-linear dynamical systems (IO-NLDS). Methods: This updated framework incorporates age-dependent contact patterns, immune waning, and new data sources, including seropositivity studies, hospital dynamics, variant trends, the effects of non-pharmaceutical interventions, and the dynamics of vaccination campaigns. Results: We analyze the dynamics of various datasets spanning the entire pandemic in Germany and its 16 federal states using this model. This analysis enables us to explore the regional heterogeneity of model parameters across Germany for the first time. We enhance our estimation methodology by introducing constraints on parameter variation among federal states to achieve this. This enables us to reliably estimate thousands of parameters based on hundreds of thousands of data points. Conclusions: Our approach is adaptable to other epidemic scenarios and even different domains, contributing to broader pandemic preparedness efforts. Full article

Background: The COVID-19 pandemic progressed unevenly across the 417 municipalities of Bahia, Brazil. Pinpointing where and when risk peaked is vital for preparing for future emergencies. Methods: We performed an ecological, spatiotemporal study using COVID-19-confirmed cases in Bahia, Brazil, from January 2020 to December 2022. A discrete Poisson space–time scan in SaTScan-identified clusters. For each cluster, we calculated relative risk (RR) and Log Likelihood Ratio, considering p < 0.05 as significant. Results: A total of 33 clusters were detected; 25 statistically significant. The largest cluster (164 municipalities; May 2020–June 2021) comprised 702,720 observed versus 338,822 expected cases (RR = 2.8). Two overlapping large clusters (185 and 136 municipalities) during January–February 2022—coinciding with Omicron circulation—showed RR > 2.0. Localized clusters reached RR > 3.0. Spatially, risk concentrated in the south, southwest, and east of the state, with isolated countryside outbreaks. Conclusions: The heterogeneous spatiotemporal dynamics of COVID-19 in Bahia underscore the value of cluster detection for targeted surveillance and resource allocation. We recommend employing statistical techniques for early detection and control, as well as conducting further studies on socioeconomic and behavioral factors. Full article

Background/Objectives: The long-term sequelae of COVID-19 pneumonia, particularly the persistence of imaging abnormalities and their relationship to clinical symptoms, remain unclear. While the acute radiologic patterns are well-documented, the transition to chronic pulmonary changes—and their implications for long COVID symptoms—require systematic investigation. Methods: Our study included 93 patients with moderate to severe COVID-19 pneumonia who were admitted to Istanbul Medical Faculty Hospital, each having one follow-up CT scan over a ten-month period. Two thoracic radiologists independently calculated semi-quantitative initial chest CT scores to evaluate lung involvement in pneumonia (0–5 per lobe, total score 0–25). Two radiologists and one pulmonologist retrospectively examined the persistence of follow-up imaging findings, interpreting them alongside the relevant clinical and laboratory data. Additionally, in a subcohort (n = 46), mid-term (5–7 months) and long-term (≥10 months) scans were compared to assess temporal trajectories. Results: Among the 93 patients with long-term follow-up imaging, non-fibrotic changes persisted in 34 scans (36.6%), while fibrotic-like changes were observed in 70 scans (75.3%). The most common persistent non-fibrotic changes were heterogeneous attenuation (29%, n = 27) and ground-glass opacities (17.2%, n = 16), and the persistent fibrotic-like changes were pleuroparenchymal bands or linear atelectasis (58%, n = 54), fine reticulation (52.6%, n = 49), and subpleural curvilinear lines (34.4%, n = 32). Both persistent non-fibrotic and fibrotic-like changes were statistically correlated with the initial CT score (p < 0.001), LDH (p < 0.001), and ferritin levels (p = 0.008 and p = 0.003, respectively). Fatigue (p = 0.025) and chest pain (p < 0.001) were reported more frequently in patients with persistent non-fibrotic changes, while chest pain (p = 0.033) was reported more frequently among those with persistent fibrotic-like changes. Among the 46 patients who underwent both mid- and long-term follow-up imaging, 47.2% of those with non-fibrotic changes (17 out of 36) and 10% of those with fibrotic-like changes (4 out of 40) exhibited regression over the long term. Conclusions: Initial imaging and laboratory findings may indicate persistent imaging findings related to long-term sequelae of COVID-19 pneumonia. Many of these persistent imaging abnormalities, particularly non-fibrotic changes seen in the mid-term, tend to lessen over the long term. A correlation exists between persistent imaging findings and clinical outcomes of long COVID-19, underscoring the need for further research. Full article

Background: COVID-19 has led to significant global morbidity and mortality, with clinical outcomes varying widely among individuals. Understanding the impact of comorbidities on COVID-19 outcomes is essential for improving patient management. To date, analyses of comorbidities affecting COVID-19 severity in a heterogeneous Swiss cohort across multiple outbreak waves are unavailable. The objective of this study was to explore the role of comorbidities on COVID-19 severity in hospitalized patients from a diverse Swiss cohort and to evaluate the association between comorbidities and specific in-hospital complications. Methods: This retrospective, observational, single-center study included adult patients who were hospitalized for COVID-19 for at least one night at the Cantonal Hospital Baselland, Switzerland (KSBL), between March 2020 and December 2021. Logistic regression analyses adjusted for age and gender were performed to analyze the association between comorbidities and critical condition (defined as severe disease or in-hospital death) and complications. Results: A total of 1124 patients were included in the study (median age 66, range 19–100 years, 60% male). A total of 76% of patients had at least one comorbidity. The most common comorbidities were arterial hypertension (47%), obesity (27%), and diabetes mellitus (24%). Overall, 16% of patients experienced a critical condition, and 25.5% had any type of complication. Patients without comorbidities had the lowest rates of critical condition (5.3%) and complications (10.2%). Obesity (OR 2.01, p < 0.001), diabetes mellitus (OR 1.67, p = 0.004), arterial hypertension (OR 1.65, p = 0.006), arrhythmia (OR1.87, p = 0.003), and chronic obstructive pulmonary disease (OR 2.72, p < 0.001) were found to be associated with critical condition. The most frequently observed complication was acute kidney failure, affecting 17.1% of the study population, while patients with arrhythmia showed the highest overall complication rate (42%). Conclusions: Our findings are consistent with previous research, confirming the relevance of specific comorbidities as key risk factors for critical COVID-19 outcomes. Among all comorbid conditions evaluated, asthma appeared to have the least impact on disease severity. Future research should focus on the impact of the combination of comorbidities on the disease severity of COVID-19, as well as the long-term effects of COVID-19 for patients with certain comorbidities. Full article