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Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers' comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has severely affected people's lives worldwide in an unexpected manner. According to the World Health Organization (WHO), several viral epidemics continue to occur and pose a significant public health problem. Until May 2023, there have been 676 million cases of COVID-19 infections and over 6.8 million deaths, globally. This paper surveys the role and effectiveness of advanced fifth-generation (5G)/beyond 5G (B5G)/sixth-generation (6G) technologies, combined with mobile applications (apps) and the Internet of Medical Things (IoMT), in detecting, managing, and mitigating the spread of COVID-19 and designing smart healthcare infrastructures for future pandemics. Analyzing and summarizing the research of relevant scholars based on the impact of 5G/B5G/6G and other technologies on COVID-19. The study tabulates the technical characteristics and effectiveness of different technologies in the context of COVID-19, summarizing the research of previous scholars. Challenges and design issues in the implementation of advanced information and telecommunication systems were demonstrated. These technologies can inspire the design of smart healthcare infrastructures to combat future virus pandemics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disrupted global energy markets and caused significant socio-economic impacts worldwide, including the energy sector due to lockdowns and restricted economic activity. This paper presents a comprehensive and analytical review of the impact of COVID-19 on the energy sector and explores the potential role of artificial intelligence (AI) in mitigating its effects. This review examines the changes in energy demand patterns during the pre-, mid-, and post-pandemic periods, analyzing their implications for the energy industries, including policymaking, communication, digital technology, energy conversion, the environment, energy markets, and power systems. Additionally, we explore how AI can enhance energy efficiency, optimize energy use, and reduce energy wastage. The potential of AI in developing sustainable energy systems is discussed, along with the challenges it poses in the energy sector's response to the pandemic. The recommendations for AI applications in the energy sector for the transition to a more sustainable energy future, with examples drawn from previous successful studies, are outlined. Information corroborated in this review is expected to provide important guidelines for crafting future research areas and directions in preparing the energy sector for any unforeseen circumstances or pandemic-like situations. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been responsible for the initiation of the global pandemic since 2020. The virus spreads through contaminated air particles, fomite, and surface-contaminated porous (i.e., paper, wood, and masks) and non-porous (i.e., plastic, stainless steel, and glass) materials. The persistence of viruses on materials depends on porosity, adsorption, evaporation, isoelectric point, and environmental conditions, such as temperature, pH, and relative humidity. Disinfection techniques are crucial for preventing viral contamination on animated and inanimate surfaces. Currently, there are few effective methodologies for preventing SARS-CoV-2 and other coronaviruses without any side effects. Before infection can occur, measures must be taken to prevent the persistence of the coronavirus on the surfaces of both porous and non-porous inanimate materials. This review focuses on coronavirus persistence in surface materials (inanimate) and control measures. Viruses are inactivated through chemical and physical methods; the chemical methods particularly include alcohol, chlorine, and peroxide, whereas temperature, pH, humidity, ultraviolet irradiation (UV), gamma radiation, X-rays, ozone, and non-thermal, plasma-generated reactive oxygen and nitrogen species (RONS) are physical methods. [ABSTRACT FROM AUTHOR]

This paper examines the subjective realities of female students in higher education as they navigated their well-being and active lives during the COVID-19 pandemic. Drawing upon Merleau-Ponty's phenomenological framework, which confronts an often-neglected dimension of our experience—the lived body and the phenomenal world—we aim to inform the development of targeted support and interventions to address the specific needs of female students, in future pandemic times. By exploring how three female students studying sport and recreation in higher education perceived their experiences, this article seeks to shed light on the unique challenges faced by these women in maintaining physical activity, coping with isolation and a change in social networks, and managing their mental health in the context of the pandemic. Understanding these subjective realities (such as isolation, restricted physical activity, and loss of social connections) and interpreting the three students' lived habitats, their bodily responsiveness, and their spontaneity ensures that their experience is heard, and appropriate future support can be given. [ABSTRACT FROM AUTHOR]

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated. [ABSTRACT FROM AUTHOR]

As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination. [ABSTRACT FROM AUTHOR]

X-ray diagnostics are widely used to detect various diseases, such as bone fracture, pneumonia, or intracranial hemorrhage. This method is simple and accessible in most hospitals, but requires an expert who is sometimes unavailable. Today, some diagnoses are made with the help of deep learning algorithms based on Convolutional Neural Networks (CNN), but these algorithms show limitations. Recently, Capsule Networks (CapsNet) have been proposed to overcome these problems. In our work, CapsNet is used to detect whether a chest X-ray image has disease (COVID or pneumonia) or is healthy. An improved model called DRCaps is proposed, which combines the advantage of CapsNet and the dilation rate (dr) parameter to manage images with 226 × 226 resolution. We performed experiments with 16,669 chest images, in which our model achieved an accuracy of 90%. Furthermore, the model size is 11M with a reconstruction stage, which helps to avoid overfitting. Experiments show how the reconstruction stage works and how we can avoid the max-pooling operation for networks with a stride and dilation rate to downsampling the convolution layers. In this paper, DRCaps is superior to other comparable models in terms of accuracy, parameters, and image size handling. The main idea is to keep the model as simple as possible without using data augmentation or a complex preprocessing stage. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may cause severe respiratory illness with high mortality. SARS-CoV-2 infection results in a massive inflammatory cell infiltration into the infected lungs accompanied by excessive pro-inflammatory cytokine production. The lung histology of dead patients shows that some areas are severely emphysematous, with enormously dilated blood vessels and micro-thromboses. The inappropriate inflammatory response damaging the pulmonary interstitial arteriolar walls suggests that the respiratory distress may come in a large part from lung vasculature injuries. It has been recently observed that low plasmatic sphingosine-1-phosphate (S1P) is a marker of a worse prognosis of clinical outcome in severe coronavirus disease (COVID) patients. S1P is an angiogenic molecule displaying anti-inflammatory and anti-apoptotic properties, that promote intercellular interactions between endothelial cells and pericytes resulting in the stabilization of arteries and capillaries. In this context, it can be hypothesized that the benefit of a normal S1P level is due to its protective effect on lung vasculature functionality. This paper provides evidence supporting this concept, opening the way for the design of a pharmacological approach involving the use of an S1P lyase inhibitor to increase the S1P level that in turn will rescue the lung vasculature functionality. [ABSTRACT FROM AUTHOR]

Neuropsychiatric manifestations of viral infections (both per se and secondary to the neuroinflammatory reaction of the host) are mainly attributed to immunological reactions, so many aspects of their pathogenesis are still nuclear. Some novel therapeutic strategies are progressively emerging in which a vaccination may be having a particular impact on recovery and reduction of death. In this context, it is accepted that the SARS-CoV-2 virus is profoundly neurotropic and neuroinvasive, with various effects on the nervous system, although there is no complete understanding of the mechanism of neuroinvasion, brain injury, or short- or long-term neuropsychiatric sequelae. Therefore, it is necessary to understand the post-infectious manifestations of COVID-19 to guide the management of neuropsychiatric diseases. Thus, based on different research groups focused on this field, in this manuscript we summarize papers on COVID-19 and the nervous system (NS) published in a series of articles by Cuban authors. This review focuses on cognitive and affective emotional states, pathogenesis, biomarkers, clinical manifestations, and intervention strategies. [ABSTRACT FROM AUTHOR]

Literature about global emergencies and their impact on people's health underlines the need to improve the social cohesion of human community and the availability of tools to support people and foster community interactions. This paper illustrates research aimed at describing and measuring human interactions in the Veneto community and its changing during the COVID-19 pandemic. 50,000 text occurrences from social media and newspapers about these topics were analyzed between December 2021 and January 2022. People present themselves as members of different teams, pursuing conflicting aims, and attributing the decision-making responsibility of emergencies management exclusively to governments, without considering themselves as active parts of the community. This delegation process on citizens' behalf can affect their health: by taking minor responsibility in handling the repercussions of these events on the community and by arguing over the most effective way to deal with them, they risk freezing and waiting for action by third parties, thus leaving mutual interactions and the promotion of their own health at a standstill. Local institutions can use these data to shape prevention policies to manage the community's emergencies and use them as opportunities to promote public health. [ABSTRACT FROM AUTHOR]

In today's digital age, the financial industry, a cornerstone of the global economy, is undergoing monumental shifts. While business performance hinges on proficient leadership, the seismic wave of digital transformation poses new challenges and magnifies the need for specific leadership competencies. The urgency is clear: adapt or become obsolete. However, there is a lack of clarity regarding which competencies are essential during such transformations. To address this gap, the purpose of this study is to identify the leadership competencies that leaders in the financial industry should possess during the digital transformation process and to determine the influential relationships among these competencies and which ones are highly influential. This paper extensively investigates the criteria for evaluating leadership competencies and integrates expert judgments to construct an evaluation framework for leadership competencies during digital transformation. The Z-based decision-making trial and evaluation laboratory (Z-DEMATEL) technique was applied to identify the influential relationships among the criteria and generate influence weights for each criterion. In addition, based on the results of Z-DEMATEL, an Influence Classification Map (ICM) was plotted that categorized the criteria into four groups: core, driving, independent, and impact. Management implications and improvement suggestions are provided accordingly. Z-DEMATEL enhances the general form of fuzzy DEMATEL. By integrating the Z-fuzzy theory, Z-DEMATEL not only accounts for the uncertainty of the evaluation environment but also measures the confidence level of experts. Taking Taiwan's financial industry as a case study, this study revealed that "vision and imagination," "critical analysis," and "strategic perspective" were the top three criteria with higher weights, while "critical analysis" and "interpersonal sensitivity" were the core criteria. Business managers can use the results to design leadership training programs that meet the requirements of digital transformation and ultimately improve the performance of organizations during the transformation. Moreover, the concepts and methods presented in this study can be applied to other industries that are embarking on their digital transformation journeys. [ABSTRACT FROM AUTHOR]

This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were expected to be mean reverting and the shocks were transitory, having similar behavior. However, after the appearance of COVID-19, results suggest that consumer sentiment recovered before the luxury sector. Results from the use of cointegration methodologies show that the effects of COVID-19 disappeared in the short-run. Finally, the sentiment of consumers acts as a leading indicator of the behavior of the luxury sector according to wavelet analysis. Thus, an increase in consumer sentiment implies an increase of 3.6% in the luxury sector. [ABSTRACT FROM AUTHOR]

Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked–Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, and evaluate its performance on multi-scale masked faces. The experimental results illustrate that the proposed method surpasses the current state-of-the-art techniques in both qualitative and quantitative metrics. It achieves a Peak Signal-to-Noise Ratio (PSNR) improvement of 4.18 dB over the second-best method, with the PSNR reaching 30.96. Additionally, it exhibits a 1% increase in the Structural Similarity Index Measure (SSIM), achieving a value of 0.95. [ABSTRACT FROM AUTHOR]

Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China's crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security. [ABSTRACT FROM AUTHOR]

The coronavirus disease-2019 (COVID-19) pandemic has had a profound impact on the world, causing loss of life, economic damage, and social disruption. Individuals with chronic kidney disease (CKD) are prone to complications and increased mortality related to COVID-19. Efforts have been made to increase understanding of the effects of COVID-19 in individuals with CKD. This paper aims to gather and discuss the state-of-the-art in the COVID-19 and CKD literature, involving the early history of COVID-19, the immunological aspects of CKD (such as abnormalities in neutrophilicand dendritic cells functions), the mechanisms of kidney injury by SARS-CoV-2 (for example, viral tropism to kidney tissue and direct cytotoxicity), the clinical course of the disease and consequences for CKD individuals (including kidney transplant recipients), and the emerging treatments (such as the use of Remdesivir, nirmatrelvir, and monoclonal neutralizing antibodies) and immunization for the CKD population. [ABSTRACT FROM AUTHOR]

From the moment a patient is admitted to the hospital, monitoring begins, and specific information is collected. The continuous flow of parameters, including clinical and analytical data, serves as a significant source of information. However, there are situations in which not all values from medical tests can be obtained. This paper aims to predict the medical test values of COVID-19 patients in the intensive care unit (ICU). By retrieving the missing medical test values, the model provides healthcare professionals with an additional tool and more information with which to combat COVID-19. The proposed approach utilizes a customizable deep learning model. Three types of neural networks, namely Multilayer Perceptron (MLP), Long/Short-Term Memory (LSTM), and Gated Recurrent Units (GRU), are employed. The parameters of these neural networks are configured to determine the model that delivers the optimal performance. Evaluation of the model's performance is conducted using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The application of the proposed model achieves predictions of the retrieved medical test values, resulting in RMSE = 7.237, MAPE = 5.572, and MAE = 4.791. Moreover, the article explores various scenarios in which the model exhibits higher accuracy. This model can be adapted and utilized in the diagnosis of future infectious diseases that share characteristics with Coronavirus Disease 2019 (COVID-19). [ABSTRACT FROM AUTHOR]

The documentation and management of the cultural heritage of the COVID-19 pandemic as well as the heritage of the digital age are emerging discourses in cultural heritage management. The enthusiastic uptake of a generative artificial intelligence application (ChatGPT) by the general public and academics alike has provided an opportunity to explore (i) whether, and to what extent, generative AI can conceptualize an emergent, not well-described field of cultural heritage (the heritage of COVID-19), (ii), whether it can design an exhibition on the topic, and (iii) whether it can identify sites associated with the pandemic that may become significant heritage. Drawing on an extended 'conversation' with ChatGPT, this paper shows that generative AI is capable of not only developing a concept for an exhibition of the heritage of COVID-19 but also that it can provide a defensible array of exhibition topics as well as a relevant selection of exhibition objects. ChatGPT is also capable of making suggestions on the selection of cultural heritage sites associated with the pandemic, but these lack specificity. The discrepancy between ChatGPT's responses to the exhibition concept and its responses regarding potential heritage sites suggests differential selection and access to the data that were used to train the model, with a seemingly heavy reliance on Wikipedia. The 'conversation' has shown that ChatGPT can serve as a brainstorming tool, but that a curator's considered interpretation of the responses is still essential. [ABSTRACT FROM AUTHOR]

Accurate segmentation of infected lesions in chest images remains a challenging task due to the lack of utilization of lung region information, which could serve as a strong location hint for infection. In this paper, we propose a novel segmentation network Co-ERA-Net for infections in chest images that leverages lung region information by enhancing supervised information and fusing multi-scale lung region and infection information at different levels. To achieve this, we introduce a Co-supervision scheme incorporating lung region information to guide the network to accurately locate infections within the lung region. Furthermore, we design an Enhanced Region Attention Module (ERAM) to highlight regions with a high probability of infection by incorporating infection information into the lung region information. The effectiveness of the proposed scheme is demonstrated using COVID-19 CT and X-ray datasets, with the results showing that the proposed schemes and modules are promising. Based on the baseline, the Co-supervision scheme, when integrated with lung region information, improves the Dice coefficient by 7.41% and 2.22%, and the IoU by 8.20% and 3.00% in CT and X-ray datasets respectively. Moreover, when this scheme is combined with the Enhanced Region Attention Module, the Dice coefficient sees further improvement of 14.24% and 2.97%, with the IoU increasing by 28.64% and 4.49% for the same datasets. In comparison with existing approaches across various datasets, our proposed method achieves better segmentation performance in all main metrics and exhibits the best generalization and comprehensive performance. [ABSTRACT FROM AUTHOR]

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought increased attention to the existing gender asymmetries in academia, which have been extensively studied in the literature. Using the analytical lens of "gendered academic citizenship", this paper takes a micropolitical perspective to examine how the pandemic has impacted the dynamics of academic work, specifically focusing on the often-overlooked aspect of everyday interactions in the work environment. Through qualitative and quantitative analysis of survey data collected from 1750 lecturers and researchers in Portuguese higher education and research institutions, the study uncovers that women, especially those who are younger, single, have specific caregiving responsibilities, and are in the early stages of their careers, were more vulnerable to experiencing "everyday" microaggressions such as silencing, exclusion, belittlement, being ignored, lack of validation, and invisibility compared to men during the pandemic. These findings suggest that, while the marginalization of women in academia is a longstanding issue, the virtual work model has created a platform for the expression of subtle forms of disempowerment and discrimination against women. These dynamics are perpetuated within the micropolitics of organizations, shaping subjective feelings of belonging and circulating differential levels of recognition within academic institutions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was complicated by the spread of false information leading to what became widely called an "infodemic". The present opinion paper was written by an ad hoc international team united under the European Union of Medical Specialists (UEMS) umbrella and reflects the organizations' effort to contribute to the resolution of these issues, by highlighting and reflecting on them and by suggesting the medical community's necessary activities resulting in the formulation of effective future communication strategies. The importance of physicians' and other health workers' role and mission as educators and leaders in communities in critical situations should be reassessed and upgraded. We need to equip future doctors with strong and sustainable leadership and communication skills through relevant undergraduate and postgraduate education programs, in order that compliance with preventive medical advice is increased. To avoid possible politically and otherwise biased communication in health crises of the future, European nations should establish independent advisory bodies providing evidence-based advice and participate in communication campaigns. Medical and other health professional organizations should build organizational and personal capacities of their members to enable them to reliably inform and adequately educate governments, populations, civic society, employers' and employees' organizations, schools and universities, and other stakeholders. [ABSTRACT FROM AUTHOR]

The first pandemic of the 21st Century was declared at the beginning of the year 2020 due to the spread of the COVID-19 virus. Its effects devastated the world economy and greatly affected maritime transport, one of the precursors of globalisation. This paper studies the effects of the pandemic on this type of transport, using data from 23,803 Paris Memorandum of Understanding Port State Control (PSC) inspections conducted in the top 10 major European ports. Comparisons have been made between Pre-COVID (2013–2019) and COVID (2020–2021) years, by way of multivariate methodologies: CO-X-STATIS, X-STATIS, and correspondence tables. The results were striking and indicate a clear change in the conduct of inspections during the COVID period, both quantitatively and qualitatively, showing a drastic reduction in the number of inspections and a change in type, with exhaustive inspections assuming a secondary role. Another notable result came from the use of the same methodology to study the different countries of registry and their evolution within PSC inspections during the Pre-COVID and COVID periods, where different behaviours were identified based on a ship's flag. These results can help us to determine important supervisory objectives for each country's maritime administration and their inspectors, to indicate weaknesses in the inspection routines caused by the pandemic, and to attempt corrections to improve maritime safety. [ABSTRACT FROM AUTHOR]

The number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it. [ABSTRACT FROM AUTHOR]

The purpose of this review was to conduct a comparative assessment of the concepts of therapy for pediatric patients with COVID-19 in the framework of global clinical practice. A structural analysis of the range of drugs and treatment strategies in the context of etiotropic, pathogenetic, and symptomatic therapy has shown that in the global context and in real clinical practice, the etiotropic-pathogenetic approach based on information about the effectiveness of individual medical technologies prevails today. It has been established that eight international nonproprietary/grouping names are present in international practice as means of etiotropic therapy for pediatric patients with COVID-19, and 18 positions are used for pathogenetic therapy. In terms of frequency of occurrence, the leading positions are occupied by remdesivir and the combination of nirmatrelvir with ritonavir, as well as dexamethasone and tocilizumab. The paper emphasizes the relevance of research in the field of evaluating the effectiveness of individual treatment regimens as well as the analysis of the delayed consequences of pathology suffered in childhood under the conditions of using various approaches to pharmacotherapy. [ABSTRACT FROM AUTHOR]

There are a variety of reasons for which one might claim an exemption from a public health mandate such as a required COVID-19 vaccine. Good-faith exemption requests—for medical, religious, or other reasons—are generally recognized as legitimate and granted to individuals when the imposition of the mandate on the requestor is perceived to outweigh the corresponding risk their lack of vaccination poses to the health and rights of others. This paper develops a method of analysis rooted in Western analytic philosophy designed to examine these issues and arrive at a framework for assessing the scientific, moral, and religious claims for exemptions from COVID-19 vaccinations. I argue that some empirical and moral beliefs are epistemically superior to others when they have a correspondence with agreed-upon facts about the world, are grounded in shared human experience, employ strong and substantive reasons for their claims, and embrace common convictions evidenced in the character of moral agents. Such facts must be demonstrable in the form of observably verifiable evidence and reliable testimony. Only then should a request for an exemption to an otherwise-required public health mandate (including a vaccine) be recognized. The alternative creates various difficulties, including the problem of moral arbitrariness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic necessitated sensitive, fast, and inexpensive testing for the virus in 2020 prior to the widespread availability of vaccines. Early testing efforts were limited by bottlenecks on reagents, low-throughput testing options, and the slow return of test results. In this paper, we detail the testing pipeline we established at the University of Wisconsin-Madison for rapid, inexpensive, and sensitive surveillance testing for SARS-CoV-2, and we highlight the strengths of the platform that would allow it to be applied to other disease surveillance projects, SARS-CoV-2 variant testing, or future pandemics. This pipeline can be quickly established for further accreditation and clinical application. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has expanded fast over the world, affecting millions of people and generating serious health, social, and economic consequences. All South East Asian countries have experienced the pandemic, with various degrees of intensity and response. As the pandemic progresses, it is important to track and analyse disease trends and patterns to guide public health policy and treatments. In this paper, we carry out a sequential cross-sectional study to produce reliable weekly COVID-19 death (out of cases) rates for South East Asian countries for the calendar years 2020, 2021, and 2022. The main objectives of this study are to characterise the trends and patterns of COVID-19 death rates in South East Asian countries through time, as well as compare COVID-19 rates among countries and regions in South East Asia. Our raw data are (daily) case and death counts acquired from "Our World in Data", which, however, for some countries and time periods, suffer from sparsity (zero or small counts), and therefore require a modelling approach where information is adaptively borrowed from the overall dataset where required. Therefore, a sequential cross-sectional design will be utilised, that will involve examining the data week by week, across all countries. Methodologically, this is achieved through a two-stage random effect shrinkage approach, with estimation facilitated by nonparametric maximum likelihood. [ABSTRACT FROM AUTHOR]

Pregnant women with SARS-CoV-2 infection have a significantly higher risk of maternal death, ICU admission, preterm delivery, and stillbirth compared to those without infection. Additionally, the risk of preeclampsia (PE) increases in pregnant women infected with SARS-CoV-2, particularly in severe cases. The association between COVID-19 and PE is likely attributed to various mechanisms, including direct effects of the virus on trophoblast function and the arterial wall, exaggerated inflammatory response in pregnant women, local inflammation leading to placental ischemia, SARS-CoV-2-related myocardial injury, cytokine storm, and thrombotic microangiopathy. This paper aims to explore the similarities between PE and SARS-CoV-2 infection, considering COVID-19 as a valuable study model. By examining these parallels, we can enhance our knowledge and comprehension of PE. We wish to emphasize the potential for COVID-19-induced myocardial injury in pregnant women and its connection to the increased maternal mortality rate. [ABSTRACT FROM AUTHOR]

A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world's population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection. [ABSTRACT FROM AUTHOR]

Background: In our current digital world, smartphones are no longer limited to communication but are used in various real-world applications. In the healthcare industry, smartphones have sensors that can record data about our daily activities. Such data can be used for many healthcare purposes, such as elderly healthcare services, early disease diagnoses, and archiving patient data for further use. However, the data collected from the various sensors involve high dimensional features, which are not equally helpful in human activity recognition (HAR). Methods: This paper proposes an algorithm for selecting the most relevant subset of features that will contribute efficiently to the HAR process. The proposed method is based on a hybrid version of the recent Coronavirus Disease Optimization Algorithm (COVIDOA) with Simulated Annealing (SA). SA algorithm is merged with COVIDOA to improve its performance and help escape the local optima problem. Results: The UCI-HAR dataset from the UCI machine learning repository assesses the proposed algorithm's performance. A comparison is conducted with seven well-known feature selection algorithms, including the Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Reptile Search Algorithm (RSA), Zebra Optimization Algorithm (ZOA), Gradient-Based Optimizer (GBO), Seagull Optimization Algorithm (SOA), and Coyote Optimization Algorithm (COA) regarding fitness, STD, accuracy, size of selected subset, and processing time. Conclusions: The results proved that the proposed approach outperforms state-of-the-art HAR techniques, achieving an average performance of 97.82% in accuracy and a reduction ratio in feature selection of 52.7%. [ABSTRACT FROM AUTHOR]

Breast cancer is the most commonly diagnosed cancer among women worldwide, and is a leading cause of cancer-related deaths. When diagnosed at an early stage, appropriate and timely treatment results in a high cure rate and better quality of life. Delays in initiating anti-cancer therapy, including surgical resection, adjuvant/neoadjuvant chemotherapy and radiation therapy are commonly encountered, even in developed health care systems. Existing comorbidities that mandate referral to other services, genetic counseling and testing that may dictate the extent and type of anti-cancer therapy and insurance coverage, are among the most commonly cited factors. However, delays can be unavoidable; for over three years, health care systems across the globe were busy dealing with the unprecedented COVID-19 pandemic. War across hot zones around the globe resulted in millions of refugees; most of them have no access to cancer care, and when/where available, there may be significant delays. Thus, cancer patients across the globe will probably continue to suffer from significant delays in diagnosis and appropriate treatment. Many retrospective reports showed significant negative impacts on different aspects of treatment outcomes and on patients' psychosocial wellbeing and productivity. In this paper, we review the available data on the impact of delays in initiating appropriate treatment on the outcomes of patients with early-stage breast cancer. [ABSTRACT FROM AUTHOR]

Although the WHO has declared the end of the pandemic emergency, COVID-19 still poses a threat to immunocompromised patients. The COVID-19 pandemic has spread throughout the world over the last two years, causing a significant number of deaths. After three years, SARS-CoV-2 has lost its initial lethality but has shown a significantly worse prognosis for immunocompromised patients, especially those who have undergone lung transplantation, compared with the general population. This paper presents two compelling case studies that highlight the complex challenges of COVID-19 infection in lung transplant recipients. The first case involves a patient who received a bilateral lung transplant for pulmonary artery hypertension in 2009, followed by a kidney transplant in 2022. Surprisingly, despite an initially favorable clinical course after contracting COVID-19, the patient deteriorated rapidly and died within a few days due to extensive lung involvement. This case highlights the unpredictable nature of COVID-19 and its potentially devastating impact on lung transplant recipients. The second case involves a patient who underwent bilateral lung transplantation five years earlier for chronic obstructive pulmonary disease (COPD). This individual also contracted COVID-19 and had pre-existing complications, including chronic lung allograft rejection (CLAD) and diffuse bronchial stenosis. Following viral infection, the patient's clinical condition deteriorated rapidly, with worsening bronchial stenosis. This case highlights the ability of COVID-19 to exacerbate pre-existing pulmonary complications in transplant recipients. These cases highlight the urgent need for increased vigilance and tailored management strategies when dealing with COVID-19 in lung transplant recipients. The unpredictable and detrimental course of the disease observed in these patients highlights the importance of implementing stringent preventive measures, such as vaccination and strict adherence to infection control protocols, in this vulnerable population. Further research is essential to gain a full understanding of the unique dynamics of COVID-19 in lung transplant recipients and to develop targeted interventions to improve their outcomes. [ABSTRACT FROM AUTHOR]

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in either increasing or decreasing the risk of airborne disease transmission. High ventilation, for instance, is a common method used to control and reduce the infection risk of airborne diseases such as COVID-19. On the other hand, high ventilation will increase energy consumption and cost. This paper proposes an optimal HVAC controller to assess the trade-off between energy consumption and indoor infection risk of COVID-19. To achieve this goal, a nonlinear model predictive controller (NMPC) is designed to control the HVAC systems of a university building to minimize the risk of COVID-19 transmission while reducing building energy consumption. The NMPC controller uses dynamic models to predict future outputs while meeting system constraints. To this end, a set of dynamic physics-based models are created to capture heat transfer and conservation of mass, which are used in the NMPC controller. Then, the developed models are experimentally validated by conducting experiments in the ETLC building at the University of Alberta, Canada. A classroom in the building is equipped with a number of sensors to measure indoor and outdoor environmental parameters such as temperature, relative humidity, and CO 2 concentration. The validation results show that the model can predict room temperature and CO 2 concentration by 0.8%, and 2.4% mean absolute average errors, respectively. Based on the validated models, the NMPC controller is designed to calculate the optimal airflow and supply air temperature for every 15 min. The results for real case studies show that the NMPC controller can reduce the infection risk of COVID-19 transmission below 1% while reducing energy consumption by 55% when compared to the existing building controller. [ABSTRACT FROM AUTHOR]

Intimate partner violence (IPV) is a pervasive issue, and during the COVID-19 pandemic, it has been speculated that the prevalence rates of IPV increased. This paper aims to understand how pandemic-specific distress was related to experiencing and perpetrating IPV. Using self-reported survey data from 371 individuals living in the United States, this study used multiple logistic regressions to examine how reports of distress related to working from home, working outside the home, isolation, stay-at-home orders, mask mandates, physical and mental health, finances, interpersonal relationships, taking care of children, and online learning for children, as well as reports of partner conflict regarding COVID-19, were associated with physical, psychological, and sexual IPV perpetration and victimization. Our results indicated that distress related to family relationships, taking care of children, and COVID-19 as a source of conflict were all associated with an increased risk of IPV victimization, while distress related to mask mandates and friendships was associated with a decreased risk. Distress related to physical health, family relationships, taking care of children, and COVID-19 being a source of conflict were associated with an increased risk of IPV perpetration, while distress related to mental health and friendships was associated with a decreased risk. Implications for researchers and clinicians are discussed. [ABSTRACT FROM AUTHOR]

Rough-based topology has become an important technique for decision making in numerous real-life problems. The main purpose of this research paper is to use some topological notions in the approximation space of a rough set. Firstly, some concepts of topological near open sets and rough concepts are presented. A new approximation structure based on the topological near open sets is introduced. We debate the properties of the new approximation structure. We included an algorithm for detecting COVID-19 infection by its side effects. We believe that our approach will be helpful for any future detection. [ABSTRACT FROM AUTHOR]

The emergence of SARS-CoV-2 virus mutations is a pressing concern in the context of the COVID-19 pandemic. In this paper, a Susceptible–Infected–Recovered (SIR) compartmental model has been formulated in terms of cellular automata and ordinary differential equations to simulate the transmission dynamics of a virus. This model accounts for the potential of new virus mutations to create additional infections of a multi-strain disease while also considering the impact of vaccination on disease control within a population. The basic reproduction number of the disease is derived, and the effect of virus mutations and vaccination rates is evaluated on a population over five years. The results of numerical simulations demonstrate the significant role of maintaining high vaccination rates in controlling the spread of the virus, even when assuming that all variants have similar illness characteristics and that a single shot of vaccine provides complete and lifelong protection against all strains. The findings underscore the necessity for countries to implement a consistent and high-level vaccination plan as soon as vaccines become available in order to mitigate the impact of a pandemic effectively. In conclusion, this study highlights the importance of taking into account the potential impact of virus mutations for controlling the COVID-19 pandemic. Furthermore, it emphasizes the critical role of vaccination in limiting the spread of the virus, and emphasizes the need to implement and maintain high vaccination rates as part of a comprehensive approach to managing the ongoing pandemic. [ABSTRACT FROM AUTHOR]

This paper analyses the structures of covariance functions of digital electroencephalography measurement vectors and digital vectors of two coronavirus images. For this research, we used the measurement results of 30-channel electroencephalography (E1–E30) and digital vectors of images of two SARS-CoV-2 variants (cor2 and cor4), where the magnitudes of intensity of the electroencephalography parameters and the parameters of the digital images of coronaviruses were encoded. The estimators of cross-covariance functions of the digital electroencephalography measurements' vectors and the digital vectors of the coronavirus images and the estimators of auto-covariance functions of separate vectors were derived by applying random functions constructed according to the vectors' parameter measurement data files. The estimators of covariance functions were derived by changing the values of the quantised interval k on the time and image pixel scales. The symmetric matrices of correlation coefficients were calculated to estimate the level of dependencies between the electroencephalography measurement results' vectors and the digital vectors of the coronavirus images. The graphical images of the normalised cross-covariance functions for the electroencephalography measurement results' vectors and the digital vectors of the coronavirus images within the period of all measurements are asymmetric. For all calculations, a computer program was developed by applying a package of Matlab procedures. A probabilistic interdependence between the results of the electroencephalography measurements and the parameters of the coronavirus vectors, as well as their variation on the time and image pixel scales, was established. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic resulted in widescale changes to how UK puppy buyers purchased their puppies. Many of these changes threatened canine welfare by increasing the likelihood of buyers purchasing a puppy from a low-welfare source, e.g., collecting a puppy away from its place of birth or without seeing their mother, both of which are currently illegal in the UK. However, whether these puppy-purchasing changes were limited only to the 2020 'peak pandemic' phase and had returned to their pre-pandemic baseline afterwards or had persisted into the later phases of the pandemic in 2021 was not known. This study explored how and why puppies were purchased in 2021 and then compared this to the same date-periods in 2020 and 2019. Valid responses were analysed from the owners of 1148 "2019 puppies", 4369 "2020 Pandemic Puppies", and 2080 "2021 puppies". Although some of the peak pandemic changes had returned to pre-pandemic levels in 2021, some worrying trends still persisted, including 2021 puppies being less likely to be viewed in-person pre-purchase or to be collected from inside their breeders' property, compared to 2019 puppies. Furthermore, concerning year-on-year increases were documented between 2019 and 2021 in the number of puppies being sold with a passport indicating importation to the UK, often under the minimum legal age for import. The issues identified in this paper require further monitoring and intervention to protect canine welfare in the future. The COVID-19 pandemic led to a surge in acquisitions of puppies in the UK, dubbed the "Pandemic Puppy" phenomenon. In addition to an increased demand for puppies, widespread changes to both why and how puppies were purchased during this period compared to pre-pandemic 2019 purchases were documented, many of which threatened canine welfare (e.g., puppies being collected away from their place of birth, without seeing their mother). This study aimed to explore which changes to the pre-purchase and purchase motivations and behaviours of UK owners who purchased a puppy aged <16 weeks in the 2020 phase of the COVID-19 pandemic had persisted into 2021 or had returned to pre-pandemic 2019 levels. An online survey was conducted during February to April 2022 from which 2080 valid responses were analysed ("2021 puppies") and compared with previously collected data from comparable cohorts in 2019 (n = 1148, "2019 puppies") and 2020 (n = 4369, "Pandemic Puppies"). While the majority of the peak pandemic changes documented in 2020 had returned to their 2019 pre-pandemic baseline, others persisted into 2021. Multinomial logistic regression models revealed that the shifts during 2020 towards owners viewing their puppy pre-purchase over video calls or via video recordings/photos rather than in-person and towards collecting their puppy from outside of their breeders' property rather than inside had persisted into 2021 and had not returned to pre-pandemic levels. Year-on-year significant rises in the number of puppies sold with a passport were documented between 2019 and 2021, with over 1 in 10 2021 puppies having been sold with a passport, the figure more than doubling since 2019. An increasing number of these puppies sold with a passport were under the minimum legal age for import at sale. Going forward, these concerning changes require further monitoring and human behaviour change interventions to tackle, including increased buyer awareness but also legislative approaches to prevent the greatest harm. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

In light of the impact of the COVID-19 epidemic on organizations and company human resource policies, multiple changes have been made to employee work behaviors. This paper developed a concept model on career plateaus, job burnout, work engagement, and turnover intention and examined it through a non-random sampling survey of 285 employees at resorts in Macao. The results revealed that career plateaus positively impact job burnout and turnover intention, and job burnout positively impacts turnover intention. The study found that career plateau negatively impacts work engagement and positively impacts turnover intention, and work engagement negatively influences turnover intention. Job burnout and work engagement partially mediate the relationship between career plateau and turnover intention. Training negatively moderates career plateau toward work engagement, and job rotation moderates career plateau toward turnover intention. Accordingly, organizations should consider the impact on employees' careers when designing training and job rotation policies in response to the epidemic. [ABSTRACT FROM AUTHOR]

Unaccompanied minors are among the most vulnerable undocumented immigrants facing removal proceedings in US immigration court. To avoid being sent back to violence and deprivation in their home countries, unaccompanied minors may apply for asylum or deportation relief for abandoned, abused, or neglected children. The COVID-19 pandemic represented a crisis for American society that also had key impacts on immigrants' lives and their ability to interact with state systems to apply for legal status and claim rights. This paper asks: (1) How did the COVID-19 pandemic affect unaccompanied minors' social vulnerabilities in the US?; and (2) How did the COVID-19 pandemic impact the US immigration bureaucracy and the work of nonprofit advocates who broker unaccompanied minors' interactions with these state systems? Findings show that the pandemic exacerbated unaccompanied minors' social vulnerabilities, with especially adverse effects on indigenous youths, those in legal limbo, unrepresented youths, and those experiencing job loss. The pandemic also disrupted the US immigration bureaucracy and, consequently, the work of advocates who broker immigrants' interactions with these systems, making it more difficult to interact with traumatized youths to obtain necessary information for their cases, meet strict deadlines, and identify their needs. This case study provides lessons on how states and civil society strategically manage a "crisis" and discusses the implications for immigrants' rights and vulnerabilities. [ABSTRACT FROM AUTHOR]

Telogen effluvium post-COVID-19 is a condition characterized by the diffuse and reversible loss of scalp hair in the period following infection with SARS-CoV-2, and it is currently the second cause of alopecia in women. In the context of the COVID-19 infection, intense psychological stress contributes to alopecia appearance, along with systemic inflammation, autoimmune reactions, oxidative stress, and virally induced hypoxia. Cytokines with proinflammatory action and vasoactive substances negatively modulate the metabolism of some molecules, such as proteoglycans, involved in the hair follicle's growth cycle. Studies show that a large percentage of hairs will suddenly enter the catagen phase during a moderate to severe COVID-19 infection. In the present paper, we update the data from the literature with a clinical example. Our case highlighted that the telogen effluvium after infections with SARS-CoV-2 is reversible with appropriate dermatological treatment. For therapeutic success, informing the patient about this pathology's self-limited and reversible character is essential to reduce the emotional stress that may aggravate the disease. [ABSTRACT FROM AUTHOR]

At the end of 2019, COVID-19 outbreaks occurred one after another in countries worldwide. Managing the outbreak efficiently and stably is an essential public health issue facing countries worldwide. In this paper, based on the SEIR model, we propose a SCEIR model that incorporates close contacts (C) and self-protectors (P). Firstly, the epidemic data of China, the USA, and Italy are predicted and compared with the actual data. Secondly, sensitivity analysis of each parameter in the SCEIR model was conducted using Anylogic. The study shows that the SCEIR model established in this paper has a certain validity. The infection rate in contact with E () etc., has positive effects on the basic regeneration number (R0); the self-isolation rate (φ) etc., has a negative effect on the basic regeneration number (R0). Emergency management measures are proposed according to the influencing factors corresponding to the model parameters. These can provide theoretical guidance for developing effective epidemic prevention and control measures in areas where the epidemic has not yet been controlled. It also provides some reference for formulating prevention and control policies for similar epidemics that may occur in the future. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic has placed a massive impact on global civilization. Finding effective treatments and drugs for these viral diseases was crucial. This paper outlined and highlighted key elements of recent advances in nonthermal biocompatible plasma (NBP) technology for antiviral applications. We searched for papers on NBP virus inactivation in PubMed ePubs, Scopus, and Web of Science databases. The data and relevant information were gathered in order to establish a mechanism for NBP-based viral inactivation. NBP has been developed as a new, effective, and safe strategy for viral inactivation. NBP may be used to inactivate viruses in an ecologically friendly way as well as activate animal and plant viruses in a number of matrices. The reactive species have been shown to be the cause of viral inactivation. NBP-based disinfection techniques provide an interesting solution to many of the problems since they are simply deployable and do not require the resource-constrained consumables and reagents required for traditional decontamination treatments. Scientists are developing NBP technology solutions to assist the medical community in dealing with the present COVID-19 outbreak. NBP is predicted to be the most promising strategy for battling COVID-19 and other viruses in the future. [ABSTRACT FROM AUTHOR]

Definition: The relationship between COVID-19 and peace has been considered from a variety of perspectives. In addition, different empirical studies on the link between the pandemic and peace in conflict-affected areas exist. However, little work has been performed on examining these studies to highlight key findings on the theme of COVID-19 and peace in conflict-affected areas. A conflict-affected area is a country, or part of a country, where widespread violence or armed conflict was present when COVID-19 emerged in December 2019, or that was transitioning from recent armed conflict to peace by the time the disease arose. What do we know, so far, about how peace has been affected by the COVID-19 pandemic in conflict-affected areas? To address this question, this paper begins by recognizing the multidimensionality of peace and clarifying that the main aspects of peace in conflict-affected areas being considered in the article are relations between conflict parties, peace efforts, and peace processes. Afterwards, the paper discusses existing evidence regarding the impact of COVID-19 and state responses to it on these components of peace in conflict-affected areas. The conflict-affected areas considered include Afghanistan, Colombia, Iraq, Israel-Palestine, Libya, Myanmar, Nigeria, South Sudan, Syria, the Philippines, Yemen, and other zones. The central finding of the article is that the existing studies on COVID-19 and peace in conflict-affected areas present mixed findings. On the one hand, the virus generated opportunities for cooperation between conflict parties in some cases, such as in the West Bank and Gaza Strip of Israel-Palestine. However, on the other hand, it created conditions that enabled conflict and impeded peace efforts and processes in many or most conflict-affected areas. The paper explains that two factors that determined how the disease affected peace in conflict-affected and non-conflict-affected areas are the pre-COVID-19 political and socio-economic conditions in an area, and how state and non-state actors responded to the pandemic. The article closes with a summary of the discussion and identification of its major limitations. [ABSTRACT FROM AUTHOR]

This paper draws on a survey of contemporary collecting projects aiming to document the COVID-19 pandemic in museums and other memory organizations. The survey focused on European memory organizations and investigated the practices they adopted in collecting, accessioning, recording, preserving, and displaying material and immaterial witnesses of the pandemic. This paper presents the results of the survey, with a particular attention on the challenges faced by these projects in relation to born-digital objects. It discusses how organizations were able to quickly launch initiatives aimed at collecting memories of the pandemic, drawing on different collection methods, adapting to the circumstances, and using a proactive attitude to reach out to different communities. The paper highlights the solutions adopted to ensure legal compliance in these projects and discusses the need for ethical considerations in relation to the collection of traumatic memories. It suggests that these collecting projects are likely to face significant challenges in the subsequent processing of this material—due to its volume and the need for new digital curation and preservation workflows. Therefore, the paper argues that these projects could also lead to a renewed attention and collaboration across the heritage sector on issues of digital curation and preservation. [ABSTRACT FROM AUTHOR]

Simple Summary: As COVID-19 shifts from pandemic to endemic, emerging variants may be more or less virulent. Predicting whether an emerging COVID-19 variant has of high risk of causing severe disease is needed to plan for potential burdens on hospital capacity and protecting vulnerable populations. However, it takes time to do laboratory and animal experiments to determine whether a new genetic variant might be more severe, and the results may not be representative of when the virus infects humans. By the time there is epidemiological data on the severity of disease associated with a new variant, it can be too late for designing an optimal public health response. There is a critical need for computer models that can predict severe disease risk from genetic sequence data, which can be obtained from just the first few infections in a potential incoming wave. Two key challenges make computer modeling difficult: (1) sequence changes are complex, and (2) using historical data to predict future disease requires accounting for the confounding effects of changing patient demographics, improving therapeutics, and increased vaccination. In this paper, we introduce a novel interpretable deep learning architecture to solve this problem, demonstrating that it can make robust predictions for emerging variants. Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data. [ABSTRACT FROM AUTHOR]

This review aims to evaluate the existing literature on Vaccine Supply Chain Management (VSCM). All relevant articles between 2002 and 2022 were systematically collected. The retrieved articles were further analyzed using bibliometric data analysis techniques. The unit of analysis is research papers published from 2002 to 2022. Vaccine Supply Chain Management (VSCM) literature has gained prominence since early 2000 and has now become voluminous. A review is the first endeavor to provide a unified body of literature. This study contributes to the existing research through insights from the bibliometric analysis and critical measurement of the literature. The results show 4288 papers on VSCM in the last 20 years. The top five countries contributing to VSCM literature are the USA, France, China, the United Kingdom, and Switzerland. Supply chain, vaccine, immunization, and Vaccine Supply Chain Management are the high-frequency keywords in the area of VSCM. The research hotspots mainly focus on healthcare, drugs, and manufacturers. In light of the COVID-19 era, this review paper indicates the area of VSCM is diversified. This study is useful for policymakers and other stakeholders to understand the existing issues in VSCM. The research trends and patterns from the literature review of VSCM will help in designing AAA (agile, adaptive, and aligned) VSCM in the future from the viewpoint of public health. This study attempts to analyze existing works, trends, developments, and potential research directions. [ABSTRACT FROM AUTHOR]