Search Results (258)

Coronavirus disease 2019 (COVID-19) resulted in significant morbidity and mortality globally. Despite the efficacy of COVID-19 vaccines in reducing morbidity and mortality, uptake in South Africa was sub-optimal due to a number of factors which remain not fully understood, particularly in mandatory vaccination workplace settings. This quantitative, cross-sectional study aimed to understand determinants of COVID-19 vaccination uptake among clinical and non-clinical workers, aged 18 years and older, employed at a large organisation with a mandatory workplace COVID-19 vaccination policy in South Africa. Workers completed a one-off, self-administered, online questionnaire that explored determinants of COVID-19 vaccination, barriers and enablers to accessing vaccines, and perspectives regarding the mandatory workplace vaccine policy. Among the 88 workers enrolled in the study, the frequent reasons for COVID-19 vaccination included preventing the spread of COVID-19 (71%, n = 62), fear of contracting COVID-19 (64%, n = 56), protecting colleagues and patients (63%, n = 55), and the mandatory workplace policy (65%, n = 57). Just under two-thirds of workers (63%, n = 55) were supportive/very supportive of the mandatory COVID-19 vaccine policy. Reasons for support included the fact that vaccination would create a safer work environment, protecting oneself/others from acquiring COVID-19, and receiving support from their employer. Only 15% (n = 13) of workers were not supportive/against the policy. The findings of this study could inform occupational health policy and counselling and support in workplaces in future pandemics. Full article

The Coronavirus Disease 2019 (COVID-19) pandemic highlighted the urgent need for effective vaccination strategies to control the virus’s spread and reduce mortality. Machine learning (ML) algorithms offer promising tools for predicting vaccine effectiveness and aiding public health decisions. This study explores the application of various ML techniques, including artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) to model and forecast the impact of vaccination on COVID-19 mortality. The algorithms were evaluated using accuracy, precision, recall, specificity, F-measure, and area under the curve (AUC) metrics. The findings revealed that DT outperformed other ML algorithms, achieving the highest metrics across multiple evaluation criteria. It recorded an accuracy of 92.27%, precision of 92.54%, recall of 91.95%, specificity of 87.92%, F-measure of 92.24%, and an AUC of 94.50%, highlighting its exceptional predictive performance. Moreover, DT demonstrated this high level of accuracy while maintaining minimal computational time. These findings suggest that ML models, particularly DTs, can be valuable in assessing vaccine effectiveness and informing health strategies against COVID-19. Full article

Background/Objectives: Vaccine hesitancy has increased during the coronavirus pandemic, a period marked by the spread of disinformation and conspiracy theories about COVID-19 vaccines. This qualitative study aimed to explore the beliefs and attitudes of Spaniards towards COVID-19 vaccines and analyze the role of information sources in this process. Methods: Semi-structured interviews were conducted with 35 residents of Spain who exhibited varying levels of vaccine hesitancy. Through thematic content analysis, the narratives supporting vaccination-related decisions, as well as the influence and trust in information sources, were examined. Results: Reasons for getting vaccinated included perceptions of it being “almost an obligation” and “fear of illness and death”. Conversely, reasons for not getting vaccinated included “uncertainty about vaccines”, the belief that “the risk is not real”, and the perception that “vaccination is a personal decision”. Regarding vaccine-related information sources, interviewees expressed distrust of the media, particularly television, as they considered news about vaccine effectiveness and characteristics to be contradictory and constantly changing, which created uncertainty about its reliability. Most interviewees were unsure if social media influenced their decision not to get vaccinated. However, those who acknowledged its impact mentioned trusting sources such as people with a university education. Additionally, close contacts—particularly healthcare professionals—had a significant influence on the decision not to get vaccinated. Conclusions: This study shows that the decision not to vaccinate is shaped by personal beliefs and sources of information—particularly social media and close acquaintances, including healthcare professionals. Full article

Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic has significantly affected global health, economies, and societies, necessitating a deeper understanding of the factors influencing its spread and severity. Methods: This study employed text network analysis to examine relationships among various risk factors associated with severe COVID-19. Analyzing a dataset of published studies from January 2020 to December 2021, this study identifies key determinants, including age, hypertension, and pre-existing health conditions, while uncovering their interconnections. Results: The analysis reveals five thematic clusters: biomedical, occupational, demographic, behavioral, and complication-related factors. Temporal trend analysis reveals distinct shifts in research focus over time. In early 2020, studies primarily addressed immediate clinical characteristics and acute complications of COVID-19. By mid-2021, research increasingly emphasized long COVID, highlighting its prolonged symptoms and impact on quality of life. Concurrently, vaccine efficacy became a dominant topic, with studies assessing protection rates against emerging viral variants, such as Alpha, Delta, and Omicron. This evolving landscape underscores the dynamic nature of COVID-19 research and the adaptation of public health strategies accordingly. Conclusions: These findings offer valuable insights for targeted public health interventions, emphasizing the need for tailored strategies to mitigate severe outcomes in high-risk groups. This study demonstrates the potential of text network analysis as a robust tool for synthesizing complex datasets and informing evidence-based decision-making in pandemic preparedness and response. Full article

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its global spread have left an indelible mark, disrupting multiple aspects of human life. It is therefore crucial to retrospectively analyze the factors that have contributed more to the initial inefficiency of the global response, thus enhancing preparedness and proactively addressing the risk of similar events occurring in the future. Critical areas were identified based on our expertise. Relevant bibliographic references were subsequently gathered through an open search of scientific databases to substantiate the concepts discussed in this article. The key issues that hindered an effective response to coronavirus disease 2019 (COVID-19) are numerous and multifaceted, and some of these will be critically examined in this article, including delayed identification of the pathogen, inadequate public health preparedness, inadequate therapeutic management, and deficiencies in laboratory diagnostics. From this analysis, key areas for improvement emerge to ensure more efficient responses to future health crises, including (i) enhancing and strengthening health information systems, (ii) improving pandemic preparedness and response planning, (iii) developing a resilient healthcare workforce, (iv) increasing investment in research and development, (v) expanding the use of telemedicine and digital health, (vi) ensuring universal access to healthcare, and (vii) improving public health communication and trust. Full article

After first appearing in December 2019, coronavirus disease 2019 (COVID-19) spread rapidly, leading to global effects and significant risks to health systems. The virus’s high replication competence in the human lung accelerated the severity of lung pneumonia cases, resulting in a catastrophic death rate. Variable observations in the clinical testing of virus-related and patient-related cases across different populations led to ambiguous results. Medical and epidemiological studies on the virus effectively use imaging and scanning devices to help explain the virus’s behavior and its impact on the lungs. Varying equipment resources and a lack of uniformity in medical imaging acquisition led to disorganized and widely dispersed data collection worldwide, while high heterogeneity in datasets caused a poor understanding of the virus and related strains, consequently leading to unstable results that could not be generalized. Hospitals and medical institutions, therefore, urgently need to collaborate to share and extract useful knowledge from these COVID-19 datasets while preserving the privacy of medical records. Researchers are turning to an emerging technology that enhances the reliability and accessibility of information without sharing actual patient data. Federated learning (FL) is a technique that learns distributed data locally, sharing only the weights of each local model to compute a global model, and has the potential to improve the generalization of diagnosis and treatment decisions. This study investigates the applicability of FL for COVID-19 under the impact of data heterogeneity, defining the lung imaging characteristics and identifying the practical constraints of FL in medical fields. It describes the challenges of implementation from a technical perspective, with reference to valuable research directions, and highlights the research challenges that present opportunities for further efforts to overcome the pitfalls of distributed learning performance. The primary objective of this literature review is to provide valuable insights that will aid in the formulation of effective technical strategies to mitigate the impact of data heterogeneity on the generalization of FL results, particularly in light of the ongoing and evolving COVID-19 pandemic. Full article

The main proteinase (Mpro), or 3CLpro, is a critical enzyme in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lifecycle and is responsible for breaking down and releasing vital functional viral proteins crucial for virus development and transmission. As a catalytically active dimer, its dimerization interface has become an attractive target for antiviral drug development. Recent research has extensively investigated the enzymatic activity of Mpro, focusing on its role in regulating the coronavirus replication complex and its significance in virus maturation and infectivity. Computational investigations have identified four druggable pockets, suggesting potential allosteric sites beyond the substrate-binding region. Empirical validation through site-directed alanine mutagenesis has targeted residues in both the active and allosteric regions and corroborated these predictions. Structural studies of drug target proteins can inform therapeutic approaches, with metadynamics simulations shedding light on the role of H163 in regulating Mpro function and providing insights into its dynamic equilibrium to the wild-type enzyme. Despite the efficacy of vaccines and drugs in mitigating SARS-CoV-2 spread, its ongoing viral evolution, selective pressures, and continued transmission pose challenges, potentially leading to resistant mutations. Phylogenetic analyses have indicated the existence of several resistant variations predating drug introduction to the human population, emphasizing the likelihood of drug spread. Hydrogen/deuterium-exchange mass spectrometry reveals the structural influence of the mutation. At the same time, clinical trials on 3CLPro inhibitors underscore the clinical significance of reduced enzymatic activity and offer avenues for future therapeutic exploration. Understanding the implications of 3CLPro mutations holds promise for shaping forthcoming therapeutic strategies against COVID-19. This review delves into factors influencing mutation rates and identifies areas warranting further investigation, providing a comprehensive overview of Mpro mutations, categorization, and terminology. Moreover, we examine their associations with clinical outcomes, illness severity, unresolved issues, and future research prospects, including their impact on vaccine efficacy and potential therapeutic targeting. Full article

The most discussed infectious disease is coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Many research endeavors have focused on the effects of the virus on reproductive organs, as these have also been shown to carry the receptors to which the virus attaches. The results of assisted reproductive technology (ART) have been significantly affected by the pandemic, with some in vitro fertilization (IVF) centers being closed due to the risk of further spread of the disease. According to World Health Organization statistics, 17.5% of adults worldwide suffered from fertility problems in 2023; in other words, one in six people in the world have reproductive health problems. As infertility is a growing problem in the modern world and new developments in assisted reproduction are always a topic of profound interest, it is important to understand the impact of SARS-CoV-2 on reproductive health. This systematic review aimed to examine studies describing patients undergoing ART procedures with a COVID-19-positive history and to shed light on the recent evidence on the safety of COVID-19 vaccination in the ART context. A meta-analysis was conducted to confirm the results of the systematic review. The results showed a significant difference in clinical pregnancy rates between the vaccinated and unvaccinated groups and an increased miscarriage rate in those with a COVID-19-positive history. However, no significant difference in clinical pregnancy and birth rates was found in participants with a previous COVID-19 infection. The results show that further studies and research are needed, even though the spread and impact of the virus have decreased. Evidence-based information for individuals and couples undergoing infertility treatment is vital to enable informed decision-making. Full article

The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense, single-stranded RNA genome-containing virus which has infected millions of people all over the world. The virus has been mutating rapidly enough, resulting in the emergence of new variants and sub-variants which have reportedly been spread from Wuhan city in China, the epicenter of the virus, to the rest of China and all over the world. The occurrence of mutations in the viral genome, especially in the viral spike protein region, has resulted in the evolution of multiple variants and sub-variants which gives the virus the benefit of host immune evasion and thus renders modern-day vaccines and therapeutics ineffective. Therefore, there is a continuous need to study the genetic characteristics and evolutionary dynamics of the SARS-CoV-2 variants. Hence, in this study, a total of 832 complete genomes of SARS-CoV-2 variants from the cities of Taiyuan and Wuhan in China was genetically characterized and their phylogenetic and evolutionary dynamics studied using phylogenetics, genetic similarity, and phylogenetic network analyses. This study shows that the four most prevalent lineages in Taiyuan and Wuhan are as follows: the Omicron lineages EG.5.1.1, followed by HK.3, FY.3, and XBB.1.16 (Pangolin classification), and clades 23F (EG.5.1), followed by 23H (HK.3), 22F (XBB), and 23D (XBB.1.9) (Nextclade classification), and lineage B followed by the Omicron FY.3, lineage A, and Omicron FL.2.3 (Pangolin classification), and the clades 19A, followed by 22F (XBB), 23F (EG.5.1), and 23H (HK.3) (Nextclade classification), respectively. Furthermore, our genetic similarity analysis show that the SARS-CoV-2 clade 19A-B.4 from Wuhan (name starting with 412981) has the least genetic similarity of about 95.5% in the spike region of the genome as compared to the query sequence of Omicron XBB.2.3.2 from Taiyuan (name starting with 18495234), followed by the Omicron FR.1.4 from Taiyuan (name starting with 18495199) with ~97.2% similarity and Omicron DY.3 (name starting with 17485740) with ~97.9% similarity. The rest of the variants showed ≥98% similarity with the query sequence of Omicron XBB.2.3.2 from Taiyuan (name starting with 18495234). In addition, our recombination analysis results show that the SARS-CoV-2 variants have three statistically significant recombinant events which could have possibly resulted in the emergence of Omicron XBB.1.16 (recombination event 3), FY.3 (recombination event 5), and FL.2.4 (recombination event 7), suggesting some very important information regarding viral evolution. Also, our phylogenetic tree and network analyses show that there are a total of 14 clusters and more than 10,000 mutations which may have probably resulted in the emergence of cluster-I, followed by 47 mutations resulting in the emergence of cluster-II and so on. The clustering of the viral variants of both cities reveals significant information regarding the phylodynamics of the virus among them. The results of our temporal phylogenetic analysis suggest that the variants of Taiyuan have likely emerged as independent variants separate from the variants of Wuhan. This study, to the best of our knowledge, is the first ever genetic comparative study between Taiyuan and Wuhan cities in China. This study will help us better understand the virus and cope with the emergence and spread of new variants at a local as well as an international level, and keep the public health authorities informed for them to make better decisions in designing new viral vaccines and therapeutics. It will also help the outbreak investigators to better examine any future outbreak. Full article

COVID-19 is a lung disease caused by a coronavirus family virus. Due to its extraordinary prevalence and associated death rates, it has spread quickly to every country in the world. Thus, achieving peaks and outlines and curing different types of relapses is extremely important. Given the worldwide prevalence of coronavirus and the participation of physicians in all countries, information has been gathered regarding the properties of the virus, its diverse types, and the means of analyzing it. Numerous approaches have been used to identify this evolving virus. It is generally considered the most accurate and acceptable method of examining the patient’s lungs and chest through a CT scan. As part of the feature extraction process, a method known as fractional Fourier transform (FrFT) has been applied as one of the time-frequency domain transformations. The proposed method was applied to a database consisting of 2481 CT images. Following the transformation of all images into equal sizes and the removal of non-lung areas, multiple combination windows are used to reduce the number of features extracted from the images. In this paper, the results obtained for KNN and SVM classification have been obtained with accuracy values of 99.84% and 99.90%, respectively. Full article

Accurate forecasting of the coronavirus disease 2019 (COVID-19) spread is indispensable for effective public health planning and the allocation of healthcare resources at all levels of governance, both nationally and globally. Conventional prediction models for the COVID-19 pandemic often fall short in precision, due to their reliance on homogeneous time-dependent transmission rates and the oversight of geographical features when isolating study regions. To address these limitations and advance the predictive capabilities of COVID-19 spread models, it is imperative to refine model parameters in accordance with evolving insights into the disease trajectory, transmission rates, and the myriad economic and social factors influencing infection. This research introduces a novel hybrid model that combines classic epidemic equations with a recurrent neural network (RNN) to predict the spread of the COVID-19 pandemic. The proposed model integrates time-dependent features, namely the numbers of individuals classified as susceptible, infectious, recovered, and deceased (SIRD), and incorporates human mobility from neighboring regions as a crucial spatial feature. The study formulates a discrete-time function within the infection component of the SIRD model, ensuring real-time applicability while mitigating overfitting and enhancing overall efficiency compared to various existing models. Validation of the proposed model was conducted using a publicly available COVID-19 dataset sourced from Italy. Experimental results demonstrate the model’s exceptional performance, surpassing existing spatiotemporal models in three-day ahead forecasting. This research not only contributes to the field of epidemic modeling but also provides a robust tool for policymakers and healthcare professionals to make informed decisions in managing and mitigating the impact of the COVID-19 pandemic. Full article

Coronaviruses are highly transmissible and pathogenic viruses for humans and animals. The vast quantity of information collected about SARS-CoV-2 during the pandemic helped to unveil details of the mechanisms behind the infection, which are still largely elusive. Recent research demonstrated that different class I/II human leukocyte antigen (HLA) alleles might define an individual susceptibility to SARS-CoV-2 spreading, contributing to the differences in the distribution of the infection through different populations; additional studies suggested that the homolog of the HLA in cats, the feline leukocyte antigen (FLA), plays a pivotal role in the transmission of viruses. With these premises, this study aimed to exploit a bioinformatic approach for the prediction of the transmissibility potential of two distinct feline coronaviruses (FCoVs) in domestic cats (feline enteric coronavirus (FeCV) and feline infectious peritonitis virus (FIPV)) using SARS-CoV-2 as the reference model. We performed an epitope mapping of nonapeptides deriving from SARS-CoV-2, FeCV, and FIPV glycoproteins and predicted their affinities for different alleles included in the three main loci in class I FLAs (E, H, and K). The predicted complexes with the most promising affinities were then subjected to molecular docking and molecular dynamics simulations to provide insights into the stability and binding energies in the cleft. Results showed the FLA proteins encoded by alleles in the FLA-I H (H*00501 and H*00401) and E (E*01001 and E*00701) loci are largely responsive to several epitopes deriving from replicase and spike proteins of the analyzed coronaviruses. The analysis of the most affine epitope sequences resulting from the prediction can stimulate the development of anti-FCoV immunomodulatory strategies based on peptide drugs. Full article

Background and Objective: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread all over the world. To assess the influence of the COVID-19 pandemic on emergency medical services (EMS) for vulnerable patients transported by ambulance. Materials and Methods: This study was a retrospective, descriptive study with a study period from 1 January 2019 to 31 December 2021 using the Osaka Emergency Information Research Intelligent Operation Network (ORION) system. We included all pediatric patients, pregnant women, and elderly patients ≥ 65 years of age transported by ambulance in Osaka Prefecture. The main outcome of this study was difficult-to-transport cases. We calculated the rate of difficult-to-transport cases under several conditions. Results: For the two year-long periods of 1 January 2019 to 31 December 2019 and 1 January 2021 to 31 December 2021, a total of 887,647 patients were transported to hospital by ambulance in Osaka Prefecture. The total number of vulnerable patients was 579,815 (304,882 in 2019 and 274,933 in 2021). Multivariate logistic regression analysis showed that difficult-to-transport cases were significantly more frequent in 2021 than in 2019. Difficult-to-transport cases were significantly less frequent in the vulnerable population than in the non-vulnerable population (adjusted odds ratio 0.81, 95% confidence interval 0.80–0.83; p < 0.001). Conclusion: During the pandemic (2021), difficult-to-transport cases were more frequent compared to before the pandemic (2019); however, vulnerable patients were not the cause of difficulties in obtaining hospital acceptance for transport. Full article

Porcine epidemic diarrhea virus (PEDV) is a porcine enteric coronavirus, which is one of the main causative agents of porcine epidemic diarrhea (PED), with 100% morbidity and 80–100% mortality in neonatal piglets. Since 2010, large-scale PED caused by highly pathogenic variants of PEDV has occurred successively in China and other countries in the world, posing a great threat to the global pig industry. It has been demonstrated in many investigations that the classic attenuated vaccine strain, PEDV CV777, is insufficient to fully protect against the PEDV variants. Moreover, the maternally derived antibodies elicited by inactivated vaccines also cannot completely protect piglets from infection. In addition, feedback feeding poses a risk of periodic PEDV recurrence in pig farms, making it challenging to successfully limit the spread of PEDV in China. This review focuses on the etiology, epidemiology, antigenicity, and control strategies of PEDV in China and provides information for the formulation of effective control measures. Full article