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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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.