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The usual lives have been changed since the COVID-19 Outbreak took place. Elderly people suffer as much as others and their lives have been impacted a lot. This paper aims to analyze the social network use and the recreation and socialization activity participation before and after the COVID-19 pandemical appearance in long-term care institutions. In the empirical side of this research, the authors of this paper sent an email questionnaire to long-term care institutions located in Rio de Janeiro city, Brazil. In the theoretical side of this research, an investigation was carried out through the scientific databases. Data were analyzed using the programming languages Python, Excel, and IBM SPSS Statistics. As a result, this paper shows an apparent increase in social network use and a drop in recreation and socialization activity participation after the COVID-19 pandemical appearance in long-term care institutions. The presented findings are the first step, which can be part of future studies analyzing the potential growth in social network use among elderly people by comparing elderly residents with non-residents in the long-term care institutions. [ABSTRACT FROM AUTHOR]

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO's superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 has had unprecedented effects on construction markets globally due to supply chain disruptions and workforce restrictions, etc. However, construction industries in developing countries are more vulnerable to the obstacles associated with the pandemic. As such, the goal of this paper is to capture the impact of COVID-19 on construction projects' performance in developing countries by taking the Pakistani construction sector into perspective. A multistep project-based research methodology was adopted, including a literature analysis and questionnaire survey with 20 construction professionals to assess factors affected by COVID-19; formulation of open-ended questions against each factor; collecting data using semi structured interviews; and presenting challenges and adopted mitigation strategies to examine the perceived impact. Results showed that the pandemic has caused a total of 13 factors to impact the project performance, which are grouped under four construction groups, including material and equipment, human resource, occupational health and safety, and financial and contracts. The factors impacted the most by the pandemic are cash flow management, supply chain disruptions, increased overheads, construction safety measures, material shortage, contractual issues, and construction workforce. This research contributes to the body of knowledge by providing a foundation for researchers to enhance investigating the impact of the COVID-19 in developing countries. [ABSTRACT FROM AUTHOR]

Background and aim of the work: The COVID-19 pandemic has deeply affected the quality of people's social life, strongly impacting family dynamics, too, not only in the harshest periods of the pandemic but also afterwards. Pandemic-related measures led to a 'stay-at-home' approach that increased the mental and physical burdens of family caregivers, irrespective of whether they were living together with the person they were caring for or not. In this paper, we provide an overview of the impact that the COVID-19 pandemic had on family relationships and dynamics, as well as on family caregivers' mental burden, and outline how this developed. Methods: We collected relevant info by searching the PubMed/Medline database with appropriate keywords. The search was performed up to 28 February 2023. This paper is reported in line with PRISMA guidance. Results: Given the recent onset of the issue, the number of relevant papers was limited. However, the additional burden that the pandemic has caused worldwide to informal caregivers clearly emerges. Conclusions: The worldwide impact of the pandemic on informal caregiving is assessed, and recommendations on how the issue can be handled are briefly sketched, too. [ABSTRACT FROM AUTHOR]

Apart from reverse-transcription polymerase chain reaction (RT-PCR) testing, chest radiographs (CXR) and computed tomography (CT) scans were employed as crucial diagnostic methods for detecting the 2019 new coronavirus disease (COVID-19). Our objective is to examine three notable COVID-19 instances from patients across the globe, along with their CXR and CT data. The evaluation of the imaging characteristics of the reported instances was the primary objective of a methodical examination of the literature. We located more than several articles that had been published between 2020 and 2023. After the papers were examined, three major cases were chosen, including a COVID-19 assessment of imaging features (chest X-ray and CT scan). Corona viral diseases (COVID-19) pose a significant risk to healthcare facilities, especially when the patient has additional medical issues. It is challenging to understand the various chest radiography results because of the use of specialized and ambiguous terminology such as "airspace disease", "pneumonia", "infiltrates", "patchy opacities", and "hazy opacities". The current investigation considered peer-reviewed case reports with Images features. Study designs, including reporting cases, were considered for imaging feature analysis. [ABSTRACT FROM AUTHOR]

The reproduction number, R 0 , is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R 0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R 0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R 0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R 0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered. [ABSTRACT FROM AUTHOR]

In the development of an inactivated virus vaccine, the isolation of a single virus strain plays a key role in determining potency. The conventional methods of quantification of virus number are mainly based on virus titers, which are subjective and time-consuming, especially in the early stage of virus isolation, in which the titer difference is weak. Previous reports have shown a high correlation between the spike protein and the potency of COVID-19 vaccines. In this paper, we report a novel, fast, and convenient method of screening an Omicron virus strain by quantifying the spike protein content, where the isolated strain shows high affinity to Omicron-specific antibodies and a high titer, and it can induce high levels of neutralization antibodies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has placed enormous pressure on healthcare systems. In the first line of the "war" against the virus, hospitals tried to maintain their general operations, while supplementing their services with COVID-19 patient care. To tackle the new difficulties, hospitals had to develop strategic response plans, and were in need of finding the most effective management structure for their institution. The focus of this paper is the aforementioned management structures. After overviewing the international literature, we identified three different approaches and we named them the Horizontal, Vertical, and Intermedier Approach. The separation is based on the following features: decision making, information distribution, command forwarding, intra-hospital communication channels, hierarchy, and control. After summarizing the international experiences, we introduce a Hungarian centrum hospital's approach, as the institution was assigned to fulfil COVID-19 centrum hospital duties in the middle of the first wave. The North-Buda Szent János Central hospital was in need of restructuring their management structure, and the leadership decided to transform it into a hierarchical, vertical structure, operating with centralized decision making and personal control. This control–command system idea came from the hospital's Internist Head Coordinator Physician, who is the first author of our article, and had serious military medicine experiences (in Afghanistan). [ABSTRACT FROM AUTHOR]

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today's environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices' limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model's efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems. [ABSTRACT FROM AUTHOR]

Coronavirus disease-19 (COVID-19) has disproportionately affected certain demographics in England, exacerbating existing health disparities. Effective therapeutics are a critical line of defence against COVID-19, particularly for patients at elevated risk for severe disease. Surveillance systems were established to monitor the usage of COVID-19 therapeutics in hospital and community settings and to inform stewardship. Three antiviral therapies—nirmatrelvir plus ritonavir (Paxlovid®), remdesivir (Veklury®), and molnupiravir (Lagevrio®)—and two neutralising monoclonal antibody therapies (nMAbs)—sotrovimab (Xevudy®) and casirivimab with imdevimab (Ronapreve®)—were in use in England between July 2020 and April 2023. This paper aims to illuminate trends in the utilisation of COVID-19 therapeutics treatment in both hospital and community settings, stratified by the Index of Multiple Deprivation (IMD) in England. Chapter 3 of the English Surveillance Programme for Antimicrobial Utilisation and Resistance (ESPAUR) Report 2022 to 2023 also discusses the epidemiological surveillance of these five directly acting antiviral COVID-19 therapeutics' use in England between 2022 and 2023. [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]

This paper highlights curriculum and policy reforms and innovations in a Higher Education (HE) institution in response to undergraduate student perspectives about the phenomenon of the COVID-19 restrictions imposed by the New Zealand government (2020–2022). The advent of the coronavirus pandemic precipitated unexpected and unprecedented changes in HE learning and in the pedagogy required for undergraduate students. Consequently, HE institutions have had to be agile, resulting in 'emergency' adaptations to curriculum practice and policy implementation. This paper discusses triggers for change, reactions to restrictions, strategic measures, and the emergence of a 'new normal' from an undergraduate student perspective. Local and global concerns about student participation in undergraduate study (in Sport and Recreation), sport, and physical activity during restricted periods prompted the researchers to investigate the challenges these young people faced. Student voice was collected through semi-structured interviews and focus groups, collected post lockdown periods. Research findings, elicited from lecturers using a collaborative auto-ethnographical method, along with interpretative analysis (hermeneutic phenomenology) of students' perspectives about the four pillars of lifestyle, active lives, learning, and livelihood provide insights about the impact of unexpected curriculum and policy change for HE students in New Zealand. This research provides real-time data to inform recommendations on policy and practice in HE institutions, to enhance student wellbeing in times of crises, like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The rationale for this review paper is to take stock of the current knowledge in the literature on the intersection of telework and work–life balance—an area that has grown in importance due to the impact of the COVID-19 pandemic. The review also considers the context of the government's role in pursuing policies to reduce the impacts of the pandemic in order to improve societal if not personal resilience, as these policies sometimes had unintended adverse impacts. After a section on the literature search method, sections follow on the literature considering telework/working from home, stress, and gender; work–life balance figures prominently in the papers reviewed. An additional category for the government and its role in concerns related to this topic follows. For future research, the differences between groups in responding to the demands of telework and work–life balance, particularly in regard to gender, are worth further investigation, as the COVID-19 pandemic has offered great challenges but also immense opportunities to learn and prepare organizations for future crises. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic affected many aspects of human life including consumer behavior. The main aim of this paper was to identify basic patterns of changes in consumer attitudes towards agri-food products under the influence of the 2020 pandemic and to better understand to what extent and what kind of food market problems appeared for the inhabitants of the Danube microregion. For this purpose, an explorative study was elaborated. Assuming that the experience of COVID-19 affected consumer attitudes and sense of food security, a hybrid survey was conducted in the Danube microregion (Croatia, Serbia and Romania) during 2022. Data collected from a total of 903 respondents were statistically analyzed in SPSS. Descriptive statistics, PCA, ANOVA and t-Test were employed. The main results have shown that although the surveyed population of the Danube microregion during the pandemic in 2020 was generally not afraid of food shortages, food was in most part available for their families and their shopping habits have not changed to a large degree, the experience of the pandemic has raised the level of awareness about some issues related to food and specifically the prices of food products. Also, three different patterns of attitude and behavior towards food and agriculture, which emerged as a result of the experience of the pandemic in 2020, were identified. These patterns also proved to be different for different segments of the population. The findings suggest the need for stronger support for the development of locally affordable food systems with the use of ICT as a coping mechanism in crises. [ABSTRACT FROM AUTHOR]

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has sparked intense research on its spike protein, which is essential for viral entrance into host cells. Viral reproduction and transmission, host immune response regulation, receptor recognition and host cell entrance mechanisms, as well as structural and functional effects have all been linked to mutations in the spike protein. Spike protein mutations can also result in immune evasion mechanisms that impair vaccine effectiveness and escape, and they are linked to illness severity and clinical consequences. Numerous studies have been conducted to determine the effects of these mutations on the spike protein structure and how it interacts with host factors. These results have important implications for the design and development of medicines and vaccines based on spike proteins as well as for the assessment of those products' efficiency against newly discovered spike protein mutations. This paper gives a general overview of how spike protein mutations are categorized and named. It further looks at the links between spike protein mutations and clinical outcomes, illness severity, unanswered problems, and future research prospects. Additionally, explored are the effects of these mutations on vaccine effectiveness as well as the possible therapeutic targeting of spike protein mutations. [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]

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]

During COVID-19, self-directed learning, contrasted with standardized learning, became a necessary and promoted learning method in public schools—one potentially supportive of mental health regularly in public schools through the use of online learning. This is important because negative mental health has been classified as a global crisis, with the highest and lowest student achievers recognized as at greatest risk. Therefore, the conditions under which public school students' mental health has been improved, leading to positive psychosocial outcomes, are relevant. Studies have identified that positive psychosocial outcomes in this regard require self-initiation of students' self-directed learning. Also necessary is a reduction in the standardized expectations of parents to lead to positive psychosocial outcomes. Unknown is what research identifies the relevance of both self-initiated self-directed online learning and a reduction in parental expectations of standardization. To investigate this, self-directed learning, online learning, mental health, public schools, and COVID-19 were keywords searched following PRISMA guidelines for scoping reviews. The result: few returns considered either factor and those that did reinforce the need for both. The conclusion: self-initiated self-directed online learning supported by public schools and parents should be central in the aim of reducing the mental health crisis in students post COVID-19. [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 had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered. [ABSTRACT FROM AUTHOR]

The Spike (S) protein of the SARS-CoV-2 virus that causes the COVID-19 disease is considered the most important target for vaccine, drug and therapeutic research as it attaches and binds to the ACE2 receptor of the host cells and allows the entry of this virus. Analysis and classification of newly determined S protein structures for SARS-CoV-2 are critical to properly understand their functional, evolutionary and architectural relatedness to already known protein structures. In this paper, first, the comparative analysis of SARS-CoV-2 S protein structures is performed. Through comparative analysis, the S protein structures in the PDB (protein data bank) database are compared and analyzed not only with each other but with the structures of other viruses for various parameters. Second, the S protein structures in PDB are classified into different variants, and the associated published literature is studied to investigate what kind of therapeutics (antibodies, T-cell receptors and small molecules) are used on the structures. This is the first study that classifies the S protein structures of the SARS-CoV-2 in PDB into various variants, and the obtained comparative analysis results could be beneficial to the research community, in general, and to crystallographers and health workers, in particular. [ABSTRACT FROM AUTHOR]

The SI4CARE project is a transnational project which aims to develop both strategy and action plans to improve health and social care in the Adriatic–Ionian region. Starting from a survey of the status quo, each partner has developed some pilots to support the development and monitoring of the policy actions. In particular, partner number three, the Municipality of Miglierina, designed and developed a pilot related to the use of wearable devices for monitoring elderly patients in rural areas. With the collaboration of the complex unity of primary care (UCCP) of the Reventino area, the pilot is based on the use of smart wearable devices to monitor some parameters of older adults after their vaccinations for flu and covid. This paper focused on the design and implementation of the system. It describes its application in the Municipality of Miglierina. Presentation of the results and a discussion of the strengths and weaknesses will be presented, in detail, in future work. Finally, the possibility of extending the experiment to other Adriatic–Ionian regions is addressed. [ABSTRACT FROM AUTHOR]

In the ongoing COVID-19 pandemic, digital technologies have played a vital role to minimize the spread of COVID-19, and to control its pitfalls for the general public. Without such technologies, bringing the pandemic under control would have been tricky and slow. Consequently, exploration of pandemic status, and devising appropriate mitigation strategies would also be difficult. In this paper, we present a comprehensive analysis of community-beneficial digital technologies that were employed to fight the COVID-19 pandemic. Specifically, we demonstrate the practical applications of ten major digital technologies that have effectively served mankind in different ways during the pandemic crisis. We have chosen these technologies based on their technical significance and large-scale adoption in the COVID-19 arena. The selected technologies are the Internet of Things (IoT), artificial intelligence(AI), natural language processing(NLP), computer vision (CV), blockchain (BC), federated learning (FL), robotics, tiny machine learning (TinyML), edge computing (EC), and synthetic data (SD). For each technology, we demonstrate the working mechanism, technical applications in the context of COVID-19, and major challenges from the perspective of COVID-19. Our analysis can pave the way to understanding the roles of these digital COVID-19-fighting technologies that can be used to fight future infectious diseases to prevent global crises. Moreover, we discuss heterogeneous data that have significantly contributed to addressing multiple aspects of the ongoing pandemic when fed to the aforementioned technologies. To the best of the authors' knowledge, this is a pioneering work on community-beneficial and transformative technologies in the context of COVID-19 with broader coverage of studies and applications. [ABSTRACT FROM AUTHOR]

COVID-19 represents the greatest health crisis in recent human history. To date, it is still difficult to estimate its impact on mortality. This paper investigates the excess crude mortality in 27 European countries. The differences between the values observed in 2020 and 2021 with those predicted by a joinpoint regression model were evaluated. A multi-regression analysis was implemented to assess the relationship between health variables and excess mortality. Europe experienced a marked and surprising (S-value > 52) increase in crude mortality during 2020 (Δ% = +10.0%, 95% CI: [2.5; 18.7]) and 2021 (Δ% = +12.1%, 95% CI: [4.3; 21.2]). The difference between average excesses of Eastern and Western countries was not surprising (S < 2) and had little relevance (ΔE-W = −2.4, 95% CI: [−2; 7]) during 2020 but was more pronounced (S = 15, ΔE-W = +17.2, 95% CI: [11.0; 23.5]) during 2021. Excess crude mortality increased in 2021 (Δ% = +65%, 95% CI: [12.6; 118], S = 5.9). Evidence has been found for a surprising and marked negative linear relationship between COVID-19 vaccinations and excess mortality ("2021 excess mortality = A + BX4", with "A = 58 ± 7, S = 28" and "B = −0.65 ± 0.10, S = 22, Radj2 = 0.65, 95% CI: [0.38; 0.82]). In light of the current literature, these findings provide solid evidence of the substantial role of COVID-19 in the unexpected and marked excess mortality recorded in Europe. COVID-19 vaccinations have appeared to be one of the main determinants for reducing mortality. Future research should explore these aspects in more detail. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 presented many challenges to the global community. The scientific community developed vaccines to mitigate the virus spread and morbidity. Higher Education institutions shifted their research and development activities toward developing COVID-19 interventions. The University of Botswana set up a multi-disciplinary design team that responded to society's COVID-19 needs. An agile methodology and co-creation process to design medical assistive devices was adopted for the study. These methods are human-centred, participatory, and have the added advantage of developing impactful, value-added, and acceptable products. This paper reports on the experiences of developing assistive breathing devices in response to the COVID-19 crisis. Experience gained from the study has enabled universities to continue to build local capacity in research and development to respond effectively and efficiently to future pandemics and any pressing societal challenge. [ABSTRACT FROM AUTHOR]

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12,028 tweets about the Omicron variant were studied, and the specific characteristics of the tweets that were analyzed include sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a 'neutral' emotion. The other emotions—'bad', 'good', 'terrible', and 'great'—were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that "Twitter for Android" was associated with 35.2% of tweets. It was followed by "Twitter Web App", "Twitter for iPhone", "Twitter for iPad", "TweetDeck", and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other domains. Finally, to support research and development in this field, we have developed an open-access Twitter dataset that comprises Tweet IDs of more than 500,000 tweets about the Omicron variant, posted on Twitter since the first detected case of this variant on 24 November 2021. [ABSTRACT FROM AUTHOR]

COVID-19, or coronavirus disease, has caused an ongoing global pandemic causing un-precedented damage in all scopes of life. An infected person with underlaying medical conditions is at greater risk than the rest of the population. Obstructive sleep apnea (OSA) is an illness associated with disturbances during sleep or an unconscious state with blockage of the airway passage. The comobordities of OSA with high blood pressure, diabetes, obesity, and age can place the life of an already infected COVID-19 patient into danger. In this paper, a prediction model for the mortality of a COVID-infected patient suffering from OSA is developed using machine learning algorithms. After an extensive methodical search, we designed an artificial neural network that can predict the mortality with an overall accuracy of 99% and a precision of 100% for forecasting the fatality chances of COVID-infected patients. We believe our model can accurately predict the mortality of the patients and can therefore assist medical health workers in predicting and making emergency clinical decisions, especially in a limited resource scenario, based on the medical history of the patients and their future potential risk of death. In this way, patients with a greater risk of mortality can receive timely treatment and benefit from proper ICU resources. Such artificial intelligent application can significantly reduce the overall mortality rate of vulnerable patients with existing medical disorders. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has been causing negative impacts on various sectors in Sri Lanka, as a result of the public health interventions that the government had to implement in order to reduce the spread of the disease. Equivalent work carried out in this context is outdated and close to ideal models. This paper presents a mathematical epidemiological model, called SEQIJRDS, having additional compartments for quarantine and infected people divided into two compartments as diagnosed and non diagnosed, compared to the SEIR model. We have presented the rate equations for the model and the basic reproduction number is derived. This model considers the effect of vaccination, the viral load of the variants, mask use, mobility, contact tracing and quarantine, natural immunity development of the infected people, and immunity waning of the recovered group as key developments of the model. The model has been validated for the COVID-19 pandemic in Sri Lanka by parameter derivation using mathematical formulations with the help of the existing data, the literature, and by model fitting for historical data. We present a comparison of the model projections for hospitalized infected people, the cumulative death count, and the daily death count against the ground truth values and projections of the SEIR and SIR models during the model validation. The validation results show that the proposed SEQIJRDS model's 12-week projection performance is significantly better than both the SEIR and SIR models; the 2-, 6-, 8-, and 10-week projection performance is always better, and the 4-week projection performance is only slightly inferior to other models. Using the proposed SEQIJRDS model, we project mortality under different lockdown procedures, vaccination procedures, quarantine practices, and different mask-use cases. We further project hospital resource usage to understand the best intervention that does not exhaust hospital resources. At the end, based on an understanding of the effect of individual interventions, this work recommends combined public health interventions based on the projections of the proposed model. Specifically, three recommendations—called minimum, sub-optimum, and optimum recommendations—are provided for public health interventions. [ABSTRACT FROM AUTHOR]

A new class of coronavirus, known as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been discovered, which is responsible for the occurrence of the disease, COVID-19. A comparative study with SARS, MERS and other human viruses was conductedand concluded that SARS-CoV-2 spread more rapidly due to increased globalization and adaptation of the virus in every environment. According to recent WHO reports, by 16 May 2021, the current outbreak of COVID-19 had affected over 174,054,314 people and killed more than 3,744,116 people in more than 222 countries acrossthe world. Finding a solution against the deadly COVID-19 has become an enormous challenge for researchers and virologists. A ring vaccination trial, which recruits subjects connected to a known case either socially or geographically, is a solution to evaluate vaccine efficacy and control the spread of the disease simultaneously, although its implementation is challenging. This review aims to summarize the noteworthy features of the world-intimidating SARS-CoV-2 global pandemic along with its evaluation, problems and challenges in the treatment strategies, clinical efficiency and detection methods proposed so far. This paper describes the impact of the lockdown in response to the COVID-19 pandemic on social, economic, health, and National Health Programs in India; possible ways to control the disease are also discussed. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease was first identified in December 2019 in Wuhan, the capital of China's Hubei province, and has since spread globally, resulting in the ongoing coronavirus pandemic. The most important type of COVID-19 confrontation is the use of polymerase chain reaction testing (PCR) to detect if a person is healthy or infected with COVID-19. Many countries released different numbers about the total cases of infected persons with the pandemic based on the number of total PCRs and other statistical numbers related to this disease. The total case numbers for some countries were very promising, such that they implied that these countries were to recover soon from these diseases. At other times, some countries overestimated the total number of cases or deaths to get financial aid. Therefore, there is a need to verify and evaluate these numbers by using machine-learning algorithms that proved to be efficient in solving many problems. The convolutional neural network (CNN) is an advanced machine-learning algorithm that was deployed to detect COVID-19 from different medical images such as X-ray images. However, CNN was not used to validate the published COVID-19 statistical data. In this paper, we adapted the self-organizing UNet (SO-UNet) in the cooperative convolutional neural network (CCNN) model to detect the accuracy of the reported COVID-19 statistics. The detection is based on using COVID-19 statistical variables that are reported by reliable and credible databases provided by known global organizations. These variables are used to create multi-dimension images to train and test the CCNN model. The results showed that reported important statistics for COVID-19 could be verified using the CCNN model. It also showed that the verified data coincided with the verification reported by the most important and trusted organizations in the world. Finally, the comparison of the new model to Benford's law outcome proved the model's credibility and efficiency in validating COVID-19 reported data. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) has exposed the unpreparedness of governments in their capacities to prevent, detect, and respond to emerging infectious diseases. Many healthcare systems have been overburdened and the coordinated efforts in different countries have focused on containment and mitigation, with varying degrees of success. A delay in the detection of and response to infectious diseases can lead to the overburdening of already challenged health systems. Containment strategies, such as social distancing, contact tracing, quarantining of exposed individuals and lockdowns, can help control the spread of the infection in communities. Still, long-term solutions should be sought to counter future outbreaks. In this paper, we focus on Zimbabwe to identify and discuss public health strategies that can result in an effective response to future infectious disease outbreaks. We consider potential solutions to facilitate early detection, control, and mitigation of any similar emerging infectious disease. We argue that sustained financial support in public health infrastructure, both locally and nationally, integrated surveillance response systems, and improved communication and research within and across public and private sectors can be instrumental in limiting the damage caused by future outbreaks. [ABSTRACT FROM AUTHOR]

We systematically reviewed published data on autopsies of patients who died from COVID-19 to identify pathological changes that are consistently reported in autopsies and those that were reported anecdotally, and to compare consistent findings with autopsy findings in similar diseases. The MEDLINE, Google Scholar, and Embase databases were searched for full-text articles in the English language from December 2019 to March 2021 to include publications on autopsy findings in patients whose main cause of death was COVID-19 and COVID-19-related complications. This review included 124 published papers documenting COVID-19 patient autopsies for over 1100 patients. Most frequent and consistently reported pathological findings included diffuse alveolar damage in the lungs, cardiac inflammation, and hypertrophy, vascular microthrombi, shock kidney and acute tubular necrosis, congested liver and spleen, and shock-associated changes in other organs. These findings by themselves were not unique, and were similar to other severe viral infections and septic shock. However, the frequency of diffuse alveolar damage and the extent of microvascular thrombosis were much higher among the autopsies of patients who had died from COVID-19 compared with other severe viral infections. Autopsy data confirm clinical reports of high incidences of diffuse alveolar damage and microvascular thrombosis in the most severe cases of COVID-19 that caused death. [ABSTRACT FROM AUTHOR]

This paper highlights curriculum and policy reforms and innovations in a Higher Education (HE) institution in response to undergraduate student perspectives about the phenomenon of the COVID-19 restrictions imposed by the New Zealand government (2020–2022). The advent of the coronavirus pandemic precipitated unexpected and unprecedented changes in HE learning and in the pedagogy required for undergraduate students. Consequently, HE institutions have had to be agile, resulting in ‘emergency’ adaptations to curriculum practice and policy implementation. This paper discusses triggers for change, reactions to restrictions, strategic measures, and the emergence of a ‘new normal’ from an undergraduate student perspective. Local and global concerns about student participation in undergraduate study (in Sport and Recreation), sport, and physical activity during restricted periods prompted the researchers to investigate the challenges these young people faced. Student voice was collected through semi-structured interviews and focus groups, collected post lockdown periods. Research findings, elicited from lecturers using a collaborative auto-ethnographical method, along with interpretative analysis (hermeneutic phenomenology) of students’ perspectives about the four pillars of lifestyle, active lives, learning, and livelihood provide insights about the impact of unexpected curriculum and policy change for HE students in New Zealand. This research provides real-time data to inform recommendations on policy and practice in HE institutions, to enhance student wellbeing in times of crises, like the COVID-19 pandemic.

Background and aim of the work: The COVID-19 pandemic has deeply affected the quality of people’s social life, strongly impacting family dynamics, too, not only in the harshest periods of the pandemic but also afterwards. Pandemic-related measures led to a ‘stay-at-home’ approach that increased the mental and physical burdens of family caregivers, irrespective of whether they were living together with the person they were caring for or not. In this paper, we provide an overview of the impact that the COVID-19 pandemic had on family relationships and dynamics, as well as on family caregivers’ mental burden, and outline how this developed. Methods: We collected relevant info by searching the PubMed/Medline database with appropriate keywords. The search was performed up to 28 February 2023. This paper is reported in line with PRISMA guidance. Results: Given the recent onset of the issue, the number of relevant papers was limited. However, the additional burden that the pandemic has caused worldwide to informal caregivers clearly emerges. Conclusions: The worldwide impact of the pandemic on informal caregiving is assessed, and recommendations on how the issue can be handled are briefly sketched, too.

The usual lives have been changed since the COVID-19 Outbreak took place. Elderly people suffer as much as others and their lives have been impacted a lot. This paper aims to analyze the social network use and the recreation and socialization activity participation before and after the COVID-19 pandemical appearance in long-term care institutions. In the empirical side of this research, the authors of this paper sent an email questionnaire to long-term care institutions located in Rio de Janeiro city, Brazil. In the theoretical side of this research, an investigation was carried out through the scientific databases. Data were analyzed using the programming languages Python, Excel, and IBM SPSS Statistics. As a result, this paper shows an apparent increase in social network use and a drop in recreation and socialization activity participation after the COVID-19 pandemical appearance in long-term care institutions. The presented findings are the first step, which can be part of future studies analyzing the potential growth in social network use among elderly people by comparing elderly residents with non-residents in the long-term care institutions.

Purpose: This study aimed to explore how race and the socioeconomic index (SES) of residential zip codes influenced COVID-19 testing rates in St. Louis during the early and peak phases of the pandemic. Method: An ecological study was conducted using COVID-19 testing data from March to November 2020 in St. Louis City, including 16,915 tests from 30 residential zip codes. Geographical mapping identified areas lacking testing, descriptive statistics characterized testing rates by race and SES, and robust linear regression modeled the association between testing rates and race, controlling for SES. Results: The overall testing rate was 303.97 per 10,000 population. Testing rates among Blacks, Whites, American Indians/Alaskan Natives, and Asians were 374.26, 247.77, 360.63, and 242.41 per 10,000, respectively. Higher rates were observed in low- and middle-SES zip codes compared with high-SES areas. Adjusted for SES, testing rates per 10,000 were higher by 125 (1.25%), 448 (4.48%), and 32 (0.32%) among Blacks, American Indians/Alaskan Natives, and Asians compared with Whites. Testing rates decreased by 56 (0.56%) per unit SES improvement. There was statistically significant SES finding and that of the difference between American Indians/Alaskan Natives and Whites. Geographical mapping showed higher rates in the inner core and lower rates in the periphery of St. Louis. Conclusions: Race and SES influence testing rates. Targeted interventions are needed in areas with low testing rates in St. Louis. [ABSTRACT FROM AUTHOR]

Since the WHO declared COVID-19 a pandemic, prominent social actors and institutions have warned about the threat of misinformation, calling for policy action to address it. However, neither the premises underlying expert claims nor the standards to separate truth from falsehood have been appraised. We conducted a scoping review of the medical and social scientific literature, informed by a critical policy analysis approach, examining what this literature means by misinformation. We searched academic databases and refereed publications, selecting a total of 68 articles for review. Two researchers independently charted the data. Our most salient finding was that verifiability relied largely on the claims of epistemic authorities, albeit only those vetted by the establishment, to the exclusion of independent evidentiary standards or heterodox perspectives. Further, "epistemic authority" did not depend necessarily on subject matter expertise, but largely on a new type of "expertise": in misinformation itself. Finally, policy solutions to the alleged threat that misinformation poses to democracy and human rights called for suppressing unverified information and debate unmanaged by establishment approved experts, in the name of protecting democracy and rights, contrary to democratic practice and respect for human rights. Notably, we identified no pockets of resistance to these dominant meanings and uses. We assessed the implications of our findings for democratic public policy, and for fundamental rights and freedoms. [ABSTRACT FROM AUTHOR]

Amid the array of challenges prompted by the COVID-19 pandemic, the policy response of governments proved vital. Different states in the U.S. pursued varied policy approaches that demonstrated distinct divergences in impact. Particularly, contrasting state contexts produced markedly assorted frameworks for the delivery of public health services, which indicate potential effects for COVID-19 policy implementation. Using the rates of COVID-19 vaccination as a measure of policy execution, the purpose of this study is to examine the political and administrative influences contributing to differences in COVID-19 public health policy outcomes among the states in the U.S. Ordinary least squares (OLS) regression models included data sources of nationally representative information pertaining to state public health attributes of politics and administration during the height of the pandemic and vaccine campaign. The findings suggest that state political leadership and fiscal capacity display significant associations with COVID-19 vaccination disparities, while other administrative indicators did not. The results signal important policy implications for the politics–administration dynamic within the COVID-19 pandemic and offer further understanding toward the roles of federalism and governance in future public health emergencies. [ABSTRACT FROM AUTHOR]

The emergence of COVID-19 has evolved into a global pandemic, causing an unprecedented public health crisis marked by unprecedented levels of morbidity never seen in the recent past. Considerable research efforts have been made in the scientific community to establish an optimal method to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and to understand the induced immune response. This review examined the development of serological tests during the COVID-19 pandemic, considering the factors affecting sensitivity and specificity, which are key to promote an efficient vaccination strategy for public health. The market has witnessed the introduction of various serological tests for the detection of SARS-CoV-2, such as the chemiluminescence immunoassay (CLIA), which emerged as a powerful and rapid tool to monitor the antibody response before and after vaccination or infection. Therefore, developing serological tests by studying antibody trends and persistence is essential for creating long-term strategies. Our analysis underscores the multifaceted applications of serological tests in pandemic management with a focus on the critical insights they provide into antibody dynamics that help in managing the ongoing pandemic and shaping future public health initiatives, providing a basis for optimizing the future response to viral threats. [ABSTRACT FROM AUTHOR]

This study offers an in-depth analysis of the COVID-19 pandemic's trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatiotemporal algorithm to identify five distinct and recurring phases that each country underwent at different times during the COVID-19 pandemic. These stages include: Comfort Period, characterized by minimal COVID-19 activity and limited impacts; Preventive Situation, demonstrating the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions; Worrying Situation, is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels; Panic Situation, marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic; and finally, Epidemic Control Situation, distinguished by limited numbers of COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis had a significant impact. This comparative analysis highlights the time lags between countries as they transitioned through these different critical stages and navigated the waves of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Understanding the factors that influence the intention to use vaccines is crucial for implementing effective public health policies. This study examined the impact of various cognitive, affective, normative, and sociodemographic variables on the intention to be vaccinated against COVID-19 with the first-generation AstraZeneca vaccine. A survey of 600 residents of Spain was used to assess the influence and hierarchy of the drivers of the intention to vaccinate via least-squares and quantile regressions. The most significant factors were the perceptions of efficacy and social influence, both of which had positive impacts (p < 0.0001). The positive influence of fear of COVID-19 and the negative influence of fear of the vaccine were also significant in shaping the central tendency toward vaccination. However, these fear-related variables, particularly the fear of COVID-19, lost importance in quantile adjustments outside the central tendency. Among the sociodemographic variables, only the negative impact of income was statistically significant. These results are valuable for the development of vaccination policies because they measure the sensitivity of attitudes toward vaccination to exogenous variables not only in the central values, as is common in similar studies, but also across the entire range of responses regarding the intention to vaccinate. This additional analysis, which is not commonly performed in studies on vaccine acceptance, allows us to distinguish between variables which are consistently related to the intention to vaccinate and those that influence only expected responses. [ABSTRACT FROM AUTHOR]

Patients who survive critical COVID-19 frequently report post-acute sequelae of COVID-19 (PASC) such as psychosomatic and neurocognitive health problems. The goal of this study was to identify clinical risk factors and other predictors for such long-term consequences in severely ill COVID-19 patients. Adult COVID-19 intensive care unit (ICU) survivors from August 2020 to May 2021 were enrolled. A broad range of clinical, laboratory and chest computed tomography (CT) data was collected during their ICU stays. The association between ICU predictors and psychosomatic, respiratory, and neurocognitive assessments 12 months after ICU discharge was analyzed using univariate regression analysis. In 17 patients (mean age 58.9 ± 11.4 years), laboratory markers (CRP, lymphocytes, hemoglobin), ICU severity (SOFA, SAPS II, need for mechanical ventilation), complications (ARDS), and lung CT data (ground-glass opacity) were promising predictors of depressive and anxiety symptoms, fatigue, and sleep problems. Recovery of psychosomatic health such as fatigue, depression, and anxiety correlated with lower levels of inflammation and high hemoglobin levels. ARDS, mechanical ventilation, and worse SOFA and SAPS II scores were further risk factors for depressive and anxiety symptoms. Our study identified novel associations such as pulmonary ground-glass opacity being positively associated with depression, anxiety, fatigue, and insomnia levels. [ABSTRACT FROM AUTHOR]

Introduction: COVID-19, caused by the SARS-CoV-2 virus, has been associated with oligosymptomatic cases or severe acute respiratory syndrome, with multiple organ failure and death. One of the most significant events for clinical outcomes is Acute Kidney Injury (AKI). It is known that AKI in COVID-19 is multifactorial, and the main mechanisms are cytokine storm, metabolic stress, use of nephrotoxic drugs, rhabdomyolysis, viral tropism to kidney tissues, and multiple organ failure. However, little is known about the impact of AKI clinical presentation and pathophysiological mechanisms on the outcome of patients affected by COVID-19. Objectives: To identify AKI clinical presentation and etiology, also known as phenotypes, and pathophysiological mechanisms, also known as subphenotypes, in patients affected by COVID-19 and associate them with death. This cohort and retrospective study evaluate the medical records of patients with SARS-CoV-2 infection admitted to a tertiary public hospital from 1 June 2020, to 31 July 2021, from admission to clinical outcome (hospital discharge or death). Clinical and laboratory data were analyzed during the hospitalization. Renal function was estimated by urine output and serum creatinine; therefore, the diagnosis and AKI classification were based on the 2012 KDIGO criteria. The occurrence of AKI was the inclusion criterion. According to clinical and laboratory presentations, we recognized two phenotypes of AKI (the direct and indirect impact of SARS-CoV-2 on the kidney) and several pathophysiological mechanisms. Subphenotypes of the direct impact of SARS-CoV-2 on kidneys were associated with Kidney Viral Tropism, Cytokine Storm, COVID-19-Related Multiple Organ Failure, and Mixed (more than one mechanism associated with COVID-19). Subphenotypes of indirect impact of SARS-CoV-2 on kidney phenotypes were Ischemic, Nephrotoxic due to rhabdomyolysis, and Septic. Univariate and multivariate analyses were performed to identify risk factors associated with death. Result: In total, 372 patients were included; 55.6% were male, 82.3% were Caucasians, and the mean age was 61.4 years. The majority of patients were admitted to the ICU (88.2%) and required mechanical ventilation (86.3%). AKI was predominantly KDIGO 3 (65.6%). When classifying our patients' AKI in two kidney phenotypes based on their clinical presentation, the direct impact of the SARS-CoV-2 phenotype was predominant (71,5%) and associated with higher mortality (83.8 vs. 46.3%, p = 0.001). Among the AKI pathophysiological mechanisms, Mixed—synergism of viral mechanisms—was the most prevalent (23.4%), followed by Viral Tropism (19.9%), Multiple Organ Failure—MOF (18%), Septic (15.6%), Ischemic (12.9%), and Cytokine Storm (10.2%). Mortality was high (73.1%). Logistic regression identified APACHE II, ATN-ISS, and the direct impact of SARS-CoV-2 on the kidney as factors associated with death, while ischemic AKI was associated with lower mortality. Conclusions: We can conclude that APACHE II and ATN-ISS scoring are clinical predictions of hospital mortality in COVID patients with AKI, as well as AKI etiology involving the direct impact of SARS-CoV-2 on the kidney, while ischemic pathophysiological mechanisms of AKI are associated with lower mortality. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has spread worldwide for over two years. It was considered a significant threat to global health due to its transmissibility and high pathogenicity. The standard test for COVID-19, namely, reverse transcription polymerase chain reaction (RT–PCR), is somehow inaccurate and might have a high false-negative rate (FNR). As a result, an infected person with a negative test result may unknowingly continue to spread the virus, especially if they are infected with an undiscovered COVID-19 strain. Thus, a more accurate diagnostic technique is required. In this study, we propose 3Cs, which is a capsule neural network (CapsNet) used to classify computed tomography (CT) images as novel coronavirus pneumonia (NCP), common pneumonia (CP), or normal lungs. Using 6123 CT images of healthy patients' lungs and those of patients with CP and NCP, the 3Cs method achieved an accuracy of around 98% and an FNR of about 2%, demonstrating CapNet's ability to extract features from CT images that distinguish between healthy and infected lungs. This research confirmed that using CapsNet to detect COVID-19 from CT images results in a lower FNR compared to RT–PCR. Thus, it can be used in conjunction with RT–PCR to diagnose COVID-19 regardless of the variant. [ABSTRACT FROM AUTHOR]

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10−⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO’s superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic.

COVID-19 has had unprecedented effects on construction markets globally due to supply chain disruptions and workforce restrictions, etc. However, construction industries in developing countries are more vulnerable to the obstacles associated with the pandemic. As such, the goal of this paper is to capture the impact of COVID-19 on construction projects’ performance in developing countries by taking the Pakistani construction sector into perspective. A multistep project-based research methodology was adopted, including a literature analysis and questionnaire survey with 20 construction professionals to assess factors affected by COVID-19; formulation of open-ended questions against each factor; collecting data using semi structured interviews; and presenting challenges and adopted mitigation strategies to examine the perceived impact. Results showed that the pandemic has caused a total of 13 factors to impact the project performance, which are grouped under four construction groups, including material and equipment, human resource, occupational health and safety, and financial and contracts. The factors impacted the most by the pandemic are cash flow management, supply chain disruptions, increased overheads, construction safety measures, material shortage, contractual issues, and construction workforce. This research contributes to the body of knowledge by providing a foundation for researchers to enhance investigating the impact of the COVID-19 in developing countries.

Apart from reverse-transcription polymerase chain reaction (RT-PCR) testing, chest radiographs (CXR) and computed tomography (CT) scans were employed as crucial diagnostic methods for detecting the 2019 new coronavirus disease (COVID-19). Our objective is to examine three notable COVID-19 instances from patients across the globe, along with their CXR and CT data. The evaluation of the imaging characteristics of the reported instances was the primary objective of a methodical examination of the literature. We located more than several articles that had been published between 2020 and 2023. After the papers were examined, three major cases were chosen, including a COVID-19 assessment of imaging features (chest X-ray and CT scan). Corona viral diseases (COVID-19) pose a significant risk to healthcare facilities, especially when the patient has additional medical issues. It is challenging to understand the various chest radiography results because of the use of specialized and ambiguous terminology such as “airspace disease”, “pneumonia”, “infiltrates”, “patchy opacities”, and “hazy opacities”. The current investigation considered peer-reviewed case reports with Images features. Study designs, including reporting cases, were considered for imaging feature analysis.

The reproduction number, R0, is an important parameter in epidemic models. It is interpreted as the average number of new cases resulted from each infected individual during the course of infection. In this paper, the R0 estimates since the outbreak of COVID-19 till 10 August 2020 for eight countries were computed using the package R{eSIR}. The computed values were examined and compared with the daily R0 estimates obtained by a static SIR model by aligning the days of infection, assuming a fixed number of days for the infected person to become confirmed/recover/die. The results showed that running R{eSIR} to obtain R0 estimates provided an easy mean of exploring epidemic data. Care must be taken in the interpretation of R0 as a measure of severity of the spread of an epidemic. Other factors, such as imported cases, need to be considered.

The COVID-19 pandemic has placed enormous pressure on healthcare systems. In the first line of the “war” against the virus, hospitals tried to maintain their general operations, while supplementing their services with COVID-19 patient care. To tackle the new difficulties, hospitals had to develop strategic response plans, and were in need of finding the most effective management structure for their institution. The focus of this paper is the aforementioned management structures. After overviewing the international literature, we identified three different approaches and we named them the Horizontal, Vertical, and Intermedier Approach. The separation is based on the following features: decision making, information distribution, command forwarding, intra-hospital communication channels, hierarchy, and control. After summarizing the international experiences, we introduce a Hungarian centrum hospital’s approach, as the institution was assigned to fulfil COVID-19 centrum hospital duties in the middle of the first wave. The North-Buda Szent János Central hospital was in need of restructuring their management structure, and the leadership decided to transform it into a hierarchical, vertical structure, operating with centralized decision making and personal control. This control–command system idea came from the hospital’s Internist Head Coordinator Physician, who is the first author of our article, and had serious military medicine experiences (in Afghanistan).