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Introduction: Literature is lacking on the safety of storing contaminated PPE in paper bags for reuse, potentially increasing exposure to frontline healthcare workers (HCW) and patients. The aim of this study is to evaluate the effectiveness of paper bags as a barrier for fomite transmission of SARS-CoV-2 by storing face masks, respirators, and face shields., Methods: This quasi-experimental study evaluated the presence of SARS-CoV-2 on the interior and exterior surfaces of paper bags containing PPE that had aerosolized exposures in clinical and simulated settings. Between May and October 2020, 30 unique PPE items were collected from COVID-19 units at two urban hospitals. Exposed PPE, worn by either an infected patient or HCW during a SARS-CoV-2 aerosolizing event, were placed into an unused paper bag. Samples were tested at 30-minute and 12-hour intervals., Results: A total of 177 swabs were processed from 30 PPE samples. We found a 6.8% positivity rate among all samples across both collection sites. Highest positivity rates were associated with ventilator disconnection and exposure to respiratory droplets from coughing. Positivity rates differed between hospital units. Total positivity rates were similar between 30-minute (6.7%) and 12-hour (6.9%) sample testing time intervals. Control samples exposed to inactivated SARS-CoV-2 droplets had higher total viral counts than samples exposed to nebulized aerosols., Conclusions: Data suggests paper bags are not a significant fomite risk for SARS-CoV-2 transmission. However, controls demonstrated a risk with droplet exposure. Data can inform guidelines for storing and re-using PPE in situations of limited supplies during future pandemics., Competing Interests: Van Den Wymelenberg has a company called Duktile through which he provides healthy building consulting, including consulting related to viral pathogens, and he serves as a scientific advisor to EnviralTech, a company that conducts viral environmental surveillance, including in senior care facilities. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

The highly contagious nature of SARS-CoV-2 has led to several studies on the transmission of the virus. A little studied potential fomite of great concern in the community is currency, which has been shown to harbor microbial pathogens in several studies. Since the onset of the COVID-19 pandemic, many businesses in the United States have limited the use of banknotes in favor of credit cards. However, SARS-CoV-2 has shown greater stability on plastic in several studies. Herein, the stability of SARS-CoV-2 at room temperature on banknotes, money cards and coins was investigated. In vitro studies with live virus suggested SARS-CoV-2 was highly unstable on banknotes, showing an initial rapid reduction in viable virus and no viral detection by 24 hours. In contrast, SARS-CoV-2 displayed increased stability on money cards with live virus detected after 48 hours. Environmental swabbing of currency and money cards on and near the campus of Brigham Young University supported these results, with no detection of SARS-CoV-2 RNA on banknotes, and a low level on money cards. However, no viable virus was detected on either. These preliminary results suggest that the use of money cards over banknotes in order to slow the spread of this virus may be ill-advised. These findings should be investigated further through larger environmental studies involving more locations., Competing Interests: The authors have declared that no competing interests exist.

The ongoing COVID-19 pandemic produced far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community's COVID-19 response lead to the emergence of new research at the remarkable rate of more than 250 papers published per day. This posed a challenge for the scientific community as traditional methods of engagement with the literature were strained by the volume of new research being produced. Meanwhile, the urgency of response lead to an increasingly prominent role for preprint servers and a diffusion of relevant research through many channels simultaneously. These factors created a need for new tools to change the way scientific literature is organized and found by researchers. With this challenge in mind, we present an overview of COVIDScholar https://covidscholar.org, an automated knowledge portal which utilizes natural language processing (NLP) that was built to meet these urgent needs. The search interface for this corpus of more than 260,000 research articles, patents, and clinical trials served more than 33,000 users at an average of 2,000 monthly active users and a peak of more than 8,600 weekly active users in the summer of 2020. Additionally, we include an analysis of trends in COVID-19 research over the course of the pandemic with a particular focus on the first 10 months, which represents a unique period of rapid worldwide shift in scientific attention. [ABSTRACT FROM AUTHOR]

Covid-19 has been front and center in the global landscape since the beginning of 2020. In response, the scientific field has dedicated enormous amounts of resources to researching the virus and its effects. The number of times Covid-19 publications are being cited throughout the literature appears remarkably high but has not been directly compared to non-Covid-19 papers in the same journals over an extended period. In our study, we use Clarivate's Web of Science—Science Citation Index Expanded™ database to identify Covid-19 papers published in 24 major scientific journals over a period of 24 months from January 1, 2020 to December 31, 2021. We conduct our search using keywords "Covid-19", "coronavirus", and "sars-cov-2" to locate publications with these words in the title. We then quantify the number of citations these papers have received and compare rates to non-Covid-19 papers in the same journals over the same timeframe. We find that, across 24 open-access and subscription-based scientific journals, Covid-19 papers published in the past 2 years currently have a median citation rate of 120.79 compared to 21.63 for non-Covid-19 papers. When negative binomial regression is used to minimize the influence of other variables such as article number variation and field of research, Covid-19 papers have still experienced more than 80% increase in citations relative to non-Covid-19 papers. These novel findings demonstrate that Covid-19 papers are being cited at remarkably higher rates than non-Covid-19 articles contained within the same journals. This suggests that journal impact factor, which is a product of the number of citations that recently published articles receive, will likely be drastically influenced by the number of Covid-19 papers that a journal has included within its pages in the previous years. [ABSTRACT FROM AUTHOR]

This paper examines the role of temporality in the negotiation of unplanned adaptive tasks that were part of the health system response to the Covid-19 pandemic in Colombia. While research has been carried out on the temporal aspects of emergency preparedness, we argue that there is an empirical gap concerning how health care organizations responded with temporal urgency to Covid-19. The dataset (118 interviews) from which a subset of interviews were analysed for this paper was collected during the first wave of the pandemic in Colombia in 2020. Interviewees included representatives of national and regional governments, public hospitals and private clinic managers, clinicians, including physicians and nurses, laboratory directors, and academics. Narratives of two tasks are presented: reconfiguring clinical laboratories to expand the testing capacity for Covid-19 and increasing intensive care unit capacity for patients hospitalized with Covid-19. Through thematic analysis of the navigation of these tasks, the concept of "temporal shifts", which signifies how organizations use time as a resource (analogous experiences, future projections) to negotiate unplanned service changes, is developed. This study highlights how powerful stakeholders deploy past and future projections to influence others´ perceptions in the negotiation of temporal shifts: a type of change that differs from the incremental and planned types described in previous organizational literature on temporality. This shift was initiated by rapid task delegation via organizational hierarchy, but accomplished through pressured, improvised actions at the operational level. The policy and practice implications we suggest relate to addressing social and organizational effects, including consequences for stakeholder engagement and staff wellbeing, generated by organizational leaders making decisions under "time stress". [ABSTRACT FROM AUTHOR]

Objectives: This paper utilizes an ontological approach to conduct a qualitative literature review in order to investigate the emotional impact of the Covid-19 pandemic on nurses internationally. After identifying common themes in the literature review, primary research is conducted to investigate the emotional impact of the Covid-19 pandemic on nurses working in Bermuda´s only acute healthcare facility. Methods: The literature review used the FRAMEWORK approach (Richie and Spencer, 1994, as cited in Hackett et al. (2018) to select a total of 16 papers for review, each of them qualitative primary research, aside from one paper reviewing international literature. Within the second part of this paper, investigating the experience of nurses in Bermuda, a grounded theory approach was utilised to collect primary data. Convenience sampling was used to recruit subjects to participate in one-on-one semi-structured interviews. Data saturation was achieved after 9 interviews. The FRAMEWORK method was utilised to analyse the interview transcripts, and identify, organise and collate themes. Results: The literature review highlights that nurses have strong emotional responses to caring for patients during the Covid-19 pandemic. Specific responses include: higher stress levels at work due to higher, more challenging workload, and a reliance on clinical leaders to assuage this stress. Stigma experienced outside of work lead to feelings of anxiety and isolation. Despite this, there was a common theme that nurses felt a satisfaction within their role to ´play their part´ in the pandemic. The primary research conducted in Bermuda demonstrates that there was an initial fear of Covid-19, receding as the pandemic developed. Much anxiety was due to a perceived lack of knowledge about the disease, lack of a coherent strategy, and inadequate equipment to protect staff, and properly care for patients. An increased level of cooperation amongst staff, and mutual support amongst nurses was noted, as was social stigma leading to feelings of isolation and anxiety. Social interactions and holistic activities were identified as primary resources to alleviate stress and uncertainty. Conclusions: Nurses in Bermuda elicited many of the same emotional responses as their international colleagues, as a result of caring for patients during the pandemic. These manifested as a result of higher, more challenging workload, and uncertainty about preparedness plan and quickly changing situations within the working environment. The results from this study can help formulate changes in policy to ensure smoother transitions to pandemic preparedness in the future. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic elicited a substantial hike in journal submissions and a global push to get medical evidence quickly through the review process. Editorial decisions and peer-assessments were made under intensified time constraints, which may have amplified social disparities in the outcomes of peer-reviewing, especially for COVID-19 related research. This study quantifies the differential impact of the pandemic on the duration of the peer-review process for women and men and for scientists at different strata of the institutional-prestige hierarchy. Using mixed-effects regression models with observations clustered at the journal level, we analysed newly available data on the submission and acceptance dates of 78,085 medical research articles published in 2019 and 2020. We found that institution-related disparities in the average time from manuscript submission to acceptance increased marginally in 2020, although half of the observed change was driven by speedy reviews of COVID-19 research. For COVID-19 papers, we found more substantial institution-related disparities in review times in favour of authors from highly-ranked institutions. Descriptive survival plots also indicated that scientists with prestigious affiliations benefitted more from fast-track peer reviewing than did colleagues from less reputed institutions. This difference was more pronounced for journals with a single-blind review procedure compared to journals with a double-blind review procedure. Gender-related changes in the duration of the peer-review process were small and inconsistent, although we observed a minor difference in the average review time of COVID-19 papers first authored by women and men. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, concerns were raised that face covering use may elicit risk compensation; a false sense of security resulting in reduced adherence to other protective behaviours such as physical distancing. This systematic review aimed to investigate the effect of face covering use on adherence to other COVID-19 related protective behaviours. Medline, Embase, PsychInfo, EmCare, medRxiv preprints, Research Square and WHO COVID-19 Research Database were searched for all primary research studies published from 1st January 2020 to 17th May 2022 that investigated the effect of face covering use on adherence to other protective behaviours in public settings during the COVID-19 pandemic. Papers were selected and screened in accordance with the PRISMA framework. Backwards and forwards citation searches of included papers were also conducted on 16th September 2022, with eligible papers published between 1st January 2020 and that date being included. A quality appraisal including risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. This review is registered on PROSPERO, number CRD42022331961. 47 papers were included, with quality ranging from low to high. These papers investigated the effects of face covering use and face covering policies on adherence to six categories of behaviour: physical distancing; mobility; face-touching; hand hygiene; close contacts; and generalised protective behaviour. Results reveal no consistent evidence for or against risk compensation, with findings varying according to behaviour and across study types, and therefore confident conclusions cannot be made. Any policy decisions related to face coverings must consider the inconsistencies and caveats in this evidence base. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources. [ABSTRACT FROM AUTHOR]

Background: According to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), scientific research data should be findable, accessible, interoperable, and reusable. The COVID-19 pandemic has led to massive research activities and an unprecedented number of topical publications in a short time. However, no evaluation has assessed whether this COVID-19-related research data has complied with FAIR principles (or FAIRness)., Objective: Our objective was to investigate the availability of open data in COVID-19-related research and to assess compliance with FAIRness., Methods: We conducted a comprehensive search and retrieved all open-access articles related to COVID-19 from journals indexed in PubMed, available in the Europe PubMed Central database, published from January 2020 through June 2023, using the metareadr package. Using rtransparent, a validated automated tool, we identified articles with links to their raw data hosted in a public repository. We then screened the link and included those repositories that included data specifically for their pertaining paper. Subsequently, we automatically assessed the adherence of the repositories to the FAIR principles using FAIRsFAIR Research Data Object Assessment Service (F-UJI) and rfuji package. The FAIR scores ranged from 1-22 and had four components. We reported descriptive analysis for each article type, journal category, and repository. We used linear regression models to find the most influential factors on the FAIRness of data., Results: 5,700 URLs were included in the final analysis, sharing their data in a general-purpose repository. The mean (standard deviation, SD) level of compliance with FAIR metrics was 9.4 (4.88). The percentages of moderate or advanced compliance were as follows: Findability: 100.0%, Accessibility: 21.5%, Interoperability: 46.7%, and Reusability: 61.3%. The overall and component-wise monthly trends were consistent over the follow-up. Reviews (9.80, SD = 5.06, n = 160), articles in dental journals (13.67, SD = 3.51, n = 3) and Harvard Dataverse (15.79, SD = 3.65, n = 244) had the highest mean FAIRness scores, whereas letters (7.83, SD = 4.30, n = 55), articles in neuroscience journals (8.16, SD = 3.73, n = 63), and those deposited in GitHub (4.50, SD = 0.13, n = 2,152) showed the lowest scores. Regression models showed that the repository was the most influential factor on FAIRness scores (R2 = 0.809)., Conclusion: This paper underscored the potential for improvement across all facets of FAIR principles, specifically emphasizing Interoperability and Reusability in the data shared within general repositories during the COVID-19 pandemic., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sofi-Mahmudi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The purpose of this paper is to describe a protocol for a systematic review (SR) and meta-analysis examining the associations between an episode of COVID-19 and trajectory as well as the risk of frailty. The protocol for this SR has been registered in the PROSPERO database (CRD42023468297) and conforms to the guidelines proposed by the Preferred Reporting Items for Systematic Reviews (PRISMA). The search strategy will involve retrieving literature from six different databases and will be guided by keywords encompassing population (community-dwelling or institution-dwelling adults), exposure (episode of COVID-19), and outcome (frailty). The citations retrieved from the search process will be screened for their eligibility. The risk of bias for the articles identified to be eligible for the review will be examined using the Quality In Prognosis Studies (QUIPS) tool. The Metafor package in R will be used for quantitative data synthesis of the literature. Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be used to assess the quality and certainty of the body of evidence. This systematic review will provide crucial information to determine whether an episode of COVID-19 is associated with an increased risk of frailty. The results of this review will have significant clinical implications in mitigating the risk of frailty after COVID-19., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mehta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: The pandemic saw widespread use of home pulse oximeters to patients diagnosed with COVID-19 to support early detection of low oxygen saturation levels and appropriate care. Rapid implementation made conventional evaluation challenging, highlighting the need for rigorous non-randomised methods to support decision-making about future use of these technologies. We used routine data to explore the benefits of pulse oximetry in Greater Manchester, under the 'COVID-19 oximetry at home' (CO@h) programme., Methods: We used data from the Greater Manchester Secure Data Environment and defined study parameters using a 'target trial' model to compare patients receiving pulse oximetry under the CO@h programme, with matched controls using various comparator groups. Primary outcomes were unplanned hospitalisation and all-cause mortality. This study is based on data from the Greater Manchester Care Record (GMCR), using anonymised, routinely collected data provided in a de-identified format for research. Informed written consent is needed for primary care patient data to be collected for service improvement and research, before data extraction to the GMCR. The study was approved under protocol GMCR RQ-048, on 12/05/2022. As indicated by the University of Manchester ethics decision tool, formal ethical approval was not required for this study., Results: The adjusted odds ratios for an unplanned hospitalisation were higher among patients receiving pulse oximetry: OR 1.86 (95% CI 1.54-2.25) at 28 days, 1.5 (95% CI 1.3-1.74) at 90 days and 1.63 (95% CI 1.44-1.83) at 1 year. Overall odds of mortality were lower among patients receiving pulse oximetry: adjusted ORs of 0.5 (95% CI 0.25-0.98) at 28 days, 0.5 (95% CI 0.32-0.78) at 90 days and 0.58 (95% CI 0.44-0.76) at 1 year. The results were robust to different comparison groups., Conclusion: Use of pulse oximetry at home under the CO@h programme, through the resulting prioritisation for appropriate care, was associated with a higher frequency of unplanned admissions and a reduction in the risk of mortality up to 1 year later. Therefore, it is likely effective for early detection of clinical deterioration and timely intervention among patients with COVID-19. Further research is needed to understand whether this is a cost-effective use of healthcare resources., Competing Interests: RW, JD, PB, FR and EK were supported by the NIHR Applied Research Collaboration Greater Manchester (NIHR200174) and RW and JD by the NIHR Manchester Biomedical Research Centre (NIHR203308). As explained in the data availability section, there are restrictions using patient level data and we cannot freely share the data used in this paper through the journal., (Copyright: © 2024 Rubinstein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper uses the GJRSK model to estimate the high-order moments of energy (oil, natural gas, and coal), the carbon market, and tourism stocks. Then, it utilizes a novel TVP-VAR time-frequency connectedness approach to examine higher-order moments spillovers among them. The results show a strong connectedness among the three markets. The energy market is the emitter of volatility, skewness and kurtosis spillovers; tourism stock is the receiver; and the carbon market is the transmitter. From a time-domain perspective, the higher-order moments spillovers of the three markets are time-varying, especially during extreme periods, where the energy market's spillover effects on tourism stocks are significantly enhanced, indicating that tourism stocks bear a greater risk at leptokurtosis and fat-tail moment. From a frequency-domain perspective, the long-term asymmetric spillovers of oil, natural gas, and tourism markets on the carbon market are more pronounced than the short-term. Moreover, the COVID-19 pandemic exacerbated the higher-moment spillovers of energy and tourism stocks on the carbon market. Meanwhile, the Russia-Ukraine conflict led to extreme risk transmission within the energy market. These findings have significant implications for cross-industry investors and green finance risk management., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gao, Yang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper analyses the relationship between working from home (WFH) and mental well-being at different stages during the first two critical years of the COVID-19 pandemic, when governments repeatedly imposed lockdowns and enacted WFH mandates to contain the spread of the virus. Using data from a representative survey conducted at four different time periods in 2020 (first lockdown, subsequent gradual reopening), 2021 (further lockdown) and 2022 (restrictions widely lifted) in the 27 EU member states, it examines the changing role of several mediators over time: work-family conflict, family-work conflict, stability, resilience, isolation, the importance of different support networks, workload, physical risk of contracting COVID-19 at work and housing conditions. For the first lockdown, it also differentiates by previous WFH experience, in terms of WFH novices and experienced WFH workers. It likewise differentiates by gender, in order to take the potential gendered nature of COVID-19 measures into account. The results point to several important mediators: for those who work from home, less family-work conflict and isolation, but greater stability, resilience, network support from family and friends, and superior housing conditions were associated with better mental well-being. The relevance of mediators was specific to certain stages of the pandemic. Stability was the most important mediator during the first lockdown. Work-family conflict and family-work conflict were only relevant during the first lockdown, while resilience and isolation mattered especially towards the end of the pandemic. Unlike established WFH workers, WFH novices had an advantage during the first lockdown, benefiting from lower family-work conflict and more helpful networks of family and friends. Our results differ by gender: for females who undertook WFH, important mediators were work-family conflict and family-work conflict. Both were related to adjustments they had to make in work and non-work hours in response to the enforced closure of schools and childcare facilities. For males who undertook WFH, support from networks of family and friends was an important mediator during the first lockdown., Competing Interests: The author has declared that no competing interests exist., (Copyright: © 2024 Sandra M. Leitner. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Nepal established the health partner coordination for COVID-19 response based on national and international plans and framework to support information-sharing for decision- making and course correction. This paper aims to assess the performance of COVID-19 Health Partner Coordination response in Nepal by adopting coordination best practices and tools from the international humanitarian cluster system., Methods: Secondary data from unpublished documents on health partner coordination for COVID-19 response in Nepal was collected and analysed from April 2023 to May 2023. The secondary data were from the review of the health partner coordination meetings conducted for COVID-19 response using a researcher-developed document review tool, responses of health partners on a survey tool adapted from the cluster coordination performance monitoring tool from the Global Health Cluster, and transcript of Focused Group Discussion among health partners. Descriptive analysis of quantitative information and thematic analysis with predefined themes of qualitative information were performed using MS Excel and MS Word respectively. A written approval from the Ministry of Health and Population and an ethical clearance from the Nepal Health Research Council was obtained before conducting the study., Results: More than three-fifths of the meetings showed good results in conduction, process, participation, and documentation of meeting action points with improvement required for follow-up (22.2%). Assessment of health partner coordination subfunctions resulted in either 'good' (>75.0%) or 'satisfactory' (50.1%-75.0%) except for prioritization based on analyses, which was 'unsatisfactory' (<50%). Partners admired good practices of health partner coordination, pointed out some issues, and provided recommendations., Conclusion: COVID-19 has highlighted the importance of effective coordination of health sector for response to a pandemic. Continuation of this practice after addressing the areas for improvement will contribute to health preparedness and readiness for future disasters or public health emergencies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mishra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Despite the worsening health disparities among youth in detention during the COVID-19 pandemic, there has been minimal exploration into the pandemic experiences of detained youth and opportunities for pandemic mitigation. This paper analyzes the perspectives of youth in detention on the pandemic, including the effect of the pandemic on their detention experience and their perceptions about COVID-19 vaccination. The study used purposive sampling to recruit 16 participants (aged 14-17 years) from two juvenile detention centers in the urban Midwest. We conducted semi-structured interviews and analyzed verbatim transcripts using a hybrid deductive-inductive approach and thematic analysis. Four themes emerged: 1) personal experience influenced youth perceptions of pandemic severity and risk; 2) distrust and misconceptions contributed to youth vaccine hesitancy or refusal; 3) desired opportunities and parental opinion motivated youth to get the COVID-19 vaccine; and 4) pandemic mitigation strategies negatively impacted youths' detention center experience. Study findings identify opportunities for detention centers to minimize the negative impacts of pandemic mitigation strategies on youth in detention, expand vaccination knowledge and uptake, and build trust to positively impact the health and wellbeing of detained youth currently and during future pandemics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 McNeill-Johnson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The Tokyo 2020 Olympic and Paralympic Games (Games) were held during the height of the coronavirus disease 2019 (COVID-19) pandemic. To detect people infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) early enough to contain the spread and to facilitate the timely arrival of athletes at their game venues, all participating athletes staying in the Olympic Village (up to 14,000) were screened daily for the infection. Toward this aim, a two-step strategy was adopted comprising screening of self-collected saliva samples using a chemiluminescence enzyme immunoassay, followed by confirmatory testing using polymerase chain reaction. The testing system was integrated with an information management system covering all steps. To ensure the accuracy of the test results, rigorous quality assurance measures and monitoring of performance/specimen quality were implemented. A chronological chart analysis was implemented to monitor the holistic process and to give feedback to improve the sampling. Nearly all test results for 418,506 saliva samples were reported within 12 hours of sample collection, achieving the target mean turnaround time of 150 minutes for confirmatory testing. As a result, athlete activity and performance for the Games were ensured. The chronological chart confirmed that no athletes were withdrawn due to a false-positive result, and no infection clusters were identified among the athletes in the Olympic Village. In conclusion, continuous quality improvement as part of the two-step strategy for mass screening for COVID-19 contributed to the success of the Games during the pandemic. The quality practice, systems, and workflows described here may offer a model for future mass-gathering sporting events during similar major infectious disease epidemics., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: For non-financial conflicts of interest, H.M., S.A., S.S., and C.I. have published several papers about COVID-19 and could have conflicts of interest among healthcare professionals. For financial conflicts of interest, H.M. was a consultant at SRL Laboratories and an adviser for the laboratory in the Olympic and Paralympic Village in Harumi, Tokyo, Japan. H.M. and S.A. were involved in the nationwide external quality assessment of SARS-CoV-2 nucleic acid tests, a project commissioned by the Ministry of Health, Labour and Welfare of Japan. S.A. contributed to studies on SARS-CoV-2 PCR tests developed by DENSO Corp. and KYORIN Pharm. Co., Ltd. that were financially supported by the Japan Agency for Medical Research and Development. R.K. and K.O. rendered laboratory service for athletes and team officials during the Tokyo 2020 Games as a commission project by the Tokyo Organizing Committee of the Olympic and Paralympic Games. S.S. and C. I. worked as staff members for Tokyo 2020 during the Tokyo 2020 Games. The authors report no other potential conflicts of interest., (Copyright: © 2024 Miyachi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To effectively respond to an emerging infectious disease outbreak, policymakers need timely and accurate measures of disease prevalence in the general population. This paper presents a new methodology to estimate real-time population infection rates from non-random testing data. The approach compares how the observed positivity rate varies with the size of the tested population and applies this gradient to infer total population infections. Applying this methodology to daily testing data across U.S. states during the first wave of the COVID-19 pandemic, we estimated widespread undiagnosed COVID-19 infections. Nationwide, we found that for every identified case, there were 12 population infections. Our prevalence estimates align with results from seroprevalence surveys, alternate approaches to measuring COVID-19 infections, and total excess mortality during the first wave of the pandemic., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Benatia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Using publicly available data, this paper investigates the diffusion of COVID-19 across neighborhoods in two major cities, London and New York. We link neighborhood demographics to incidence, and we investigate patterns of change over time in conjunction with changing policy responses to the pandemic. By comparing and contrasting these two cities, we are able to exploit surveillance and policy differences, demonstrating how each contributes information to the other. We conclude that better coordination can be translated into improved health policy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 de Coulon, Scott. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic introduced imminent and lasting impacts on the opioid crisis in the U.S., including a significant increase in opioid overdose and deaths and in use of telehealth in treatment. What lessons can we learn from the treatment transition during the pandemic that could help tackle the opioid crisis when future pandemics strike? In this paper, we conducted a phone survey with opioid treatment facilities in Pennsylvania to examine the COVID-19's impacts on treatment facilities and individuals with opioid use disorder during the first year of the pandemic. We separated the lockdown period (Mid-March through Mid-May, 2020) from the reopening period that followed, and urban areas from rural areas, to explore temporal evolution and rural-urban variations in the COVID-19's impacts. We found rural-urban heterogeneity in facilities' adoption of telehealth in treatment and in challenges and risk factors faced by their clients during the lockdown period. During the reopening, telehealth was adopted by most facilities, and telehealth-related challenges became less salient; however, both rural and urban facilities reported higher relapse risks faced by their clients, citing factors more likely to be at clients' end and related to socioeconomic stressors and mental health. Our results highlight the vitality of addressing socioeconomic and mental health challenges faced by individuals with OUD, via government policies and community interventions, when future pandemics strike. The findings also indicate the importance of maintaining facilities' financial well-being to provide treatment services., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: People with lived experience of a health issue can be engaged in research to address issues related to social justice, informing change through partnerships and an understanding of community perspectives and needs. Although photovoice has been applied to various disciplines and topics across the health sciences, the concrete design of the photovoice process and participants' experience of engaging in photovoice is not always well documented or understood., Objective: This paper describes youth participants' experiences and perspectives with a youth-led photovoice design process on a study regarding COVID-19 vaccine confidence., Method: The sample consisted of 27 youth aged 14-24 who reported experiencing mental health and/or substance use challenges [MHSU] during the COVID-19 pandemic and some degree of COVID-19 vaccine confidence. Youth participated in a series of photography workshops, then each attended one of the six focus groups about both the topic and experience of the photovoice project., Results: Four themes were constructed from the data: 1) Participating in a photovoice project was an enjoyable experience that had a positive effect on participants; 2) Shared group experiences contributed to building a safe space for participants; 3) Photography and the photovoice process served as a catalyst for reflection; 4) Photovoice shifted participants' perspectives on both the COVID-19 vaccine and photography., Conclusions: This project, a youth-engaged and youth-led photovoice study, describes how the photovoice methodology can be applied in a public health context to meaningfully involve young people and impact their lives. By involving youth in the co-construction of the study design and implementation, photovoice research can represent positive and empowering experiences for participants. Bringing together a diverse and multifaceted lived experience engagement research team structure strengthened the design, delivery, analysis, and interpretation of the project., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mckee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 outbreak has posed an unprecedented challenge to humanity and science. On the one side, public and private incentives have been put in place to promptly allocate resources toward research areas strictly related to the COVID-19 emergency. However, research in many fields not directly related to the pandemic has been displaced. In this paper, we assess the impact of COVID-19 on world scientific production in the life sciences and find indications that the usage of medical subject headings (MeSH) has changed following the outbreak. We estimate through a difference-in-differences approach the impact of the start of the COVID-19 pandemic on scientific production using the PubMed database (3.6 Million research papers). We find that COVID-19-related MeSH terms have experienced a 6.5 fold increase in output on average, while publications on unrelated MeSH terms dropped by 10 to 12%. The publication weighted impact has an even more pronounced negative effect (-16% to -19%). Moreover, COVID-19 has displaced clinical trial publications (-24%) and diverted grants from research areas not closely related to COVID-19. Note that since COVID-19 publications may have been fast-tracked, the sudden surge in COVID-19 publications might be driven by editorial policy. [ABSTRACT FROM AUTHOR]

Background: South Asians are a rapidly growing population in the United States. Breast cancer is a major concern among South Asian American women, who are an understudied population. We established the South Asian Breast Cancer (SABCa) study in New Jersey during early 2020 to gain insights into their breast cancer-related health attitudes. Shortly after we started planning for the study, the COVID-19 disease spread throughout the world. In this paper, we describe our experiences and lessons learned from recruiting study participants by partnering with New Jersey's community organizations during the COVID-19 pandemic. Methods: We used a cross-sectional design. We contacted 12 community organizations and 7 (58%) disseminated our study information. However, these organizations became considerably busy with pandemic-related needs. Therefore, we had to pivot to alternative recruitment strategies through community radio, Rutgers Cancer Institute of New Jersey's Community Outreach and Engagement Program, and Rutgers Cooperative Extension's community health programs. We recruited participants through these alternative strategies, obtained written informed consent, and collected demographic information using a structured survey. Results: Twenty five women expressed interest in the study, of which 22 (88%) participated. Nine (41%) participants learned about the study through the radio, 5 (23%) through these participants, 1 (4.5%) through a non-radio community organization, and 7 (32%) through community health programs. Two (9%) participants heard about the study from their spouse. All participants were born outside the US, their average age was 52.4 years (range: 39–72 years), and they have lived in the US for an average of 26 years (range: 5–51 years). Conclusion: Pivoting to alternative strategies were crucial for successful recruitment. Findings suggest the significant potential of broadcast media for community-based recruitment. Family dynamics and the community's trust in our partners also encouraged participation. Such strategies must be considered when working with understudied populations. [ABSTRACT FROM AUTHOR]

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation. [ABSTRACT FROM AUTHOR]

Background: The Brief Resilience Coping Scale (BRCS) is a brief instrument suitable for epidemiological studies. The aims of this paper were to analyze changes in BRCS depending on time, sex, age group, relationship status, as well as risk of poverty, to test the psychometric properties including test-retest reliability and measurement invariance, and to determine associations with psychosocial stress, depressiveness, anxiety, social support, as well as subjective mental and physical health. As the data from this study was collected during the pandemic, an additional sensitivity analysis was performed with pre-pandemic data., Methods: A longitudinal study of resilience and distress in a large-sized community sample was performed at one pre-pandemic (T0) and three pandemic time points (T1-3). Resilient coping was assessed by the 4-Item short form of the BRCS, distress by the PHQ-9 and GAD-2., Results: BRCS decreased between the first and the second and increased at the third pandemic time point. The scale had a good internal consistency. Test-retest correlation coefficients ranged from 0.527 to 0.589. Higher resilient coping was found in younger participants, participants not at-risk-of-poverty and in males. Stability was higher in those with a partner, and at-risk-of-poverty. Significant negative associations with psychosocial stress, loneliness, depressiveness, anxiety, social support, as well as subjective and physical health and SES underscored the construct validity., Conclusion: Overall, findings underscore that resilient coping is a dynamic construct with considerable stability. The scale showed good psychometric properties including test-retest reliability over four months to two years. We found that it is not only important to describe the level of resilient coping, but also its stability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Petersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) models, have been generally used in analyzing epidemiological data and forecasting the trajectory of transmission of infectious diseases such as COVID-19. Experience shows that accurately forecasting the trajectory of COVID-19 transmission curve is a big challenge for researchers in the field of epidemiological modeling because multiple unquantified factors can affect the trajectory of COVID-19 transmission. In the past years, we used a new compartmental model, l-i SEIR model, to analyze the COVID-19 transmission trend in the United States. Unlike the conventional SEIR model and the delayed SEIR model that use or partially use the approximation of temporal homogeneity, the l-i SEIR model takes into account chronological order of infected individuals in both latent (l) period and infectious (i) period, and thus improves the accuracy in forecasting the trajectory of transmission of infectious diseases, especially during periods of rapid rise or fall in the number of infections. This paper describes (1) how to use the new SEIR model (a mechanistic model) combined with fitting methods to simulate or predict trajectory of COVID-19 transmission, (2) how social interventions and new variants of COVID-19 significantly change COVID-19 transmission trends by changing transmission rate coefficient βn, the fraction of susceptible people (Sn/N), and the reinfection rate, (3) why accurately forecasting COVID-19 transmission trends is difficult, (4) what are the strategies that we have used to improve the forecast outcome and (5) what are some successful examples that we have obtained., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 DeVries. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Logistics resilience is a significant representation of sustainable development ability and a necessary support for high-quality economic development. In order to explore the influencing factors and realization mechanism of the improvement of logistics resilience of the Yangtze River Economic Belt and the high-quality and sustainable development of the economy, this paper comprehensively considers factors of the supply and demand relationship of the logistics market, industrial structure and ecological environment, and evaluates the urban logistics resilience of the Yangtze River Economic Belt by using POI data and statistical data. Combined with the spatial Durbin model, the influencing factors and spatial spillover effects of inter-city logistics resilience were revealed. This study found that the urban logistics resilience in the lower reaches of the Yangtze River has been high. Except Chongqing and Shanghai, the COVID-19 epidemic happened in 2020 led to a significant decrease in logistics resilience. In the meanwhile, every 1% increase in the logistics resilience of the city will promote the logistics resilience of the adjacent cities by 0.145%. Economic condition and urban development potential have positive effects on logistics resilience of the city and its adjacent cities. The economic condition has a direct effect coefficient of 0.166 and an indirect effect coefficient of 0.181, The direct and indirect effects of urban development potential are significantly positive, and the coefficients are 0.001 and 0.006, respectively. The level of information, government support and ability of technological innovation are helpful for the improvement of urban logistics resilience while hindering the enhancement of logistics resilience in adjacent cities. The research area can be extended in the future and more influencing factors can be considered in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang, Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic has negatively impacted the mental health and wellbeing of post-secondary students. Resilience has been found to serve as a protective factor against mental distress among students during the pandemic. Despite the plethora of research that exists on post-secondary students during this crisis, most studies exploring students' health and resilience are quantitative and lack diversity. To date, the lived experiences of health-related quality of life (HRQOL) and resilience among graduate students representing diversity in age, gender, ethnicity, parental status, university, degree, and faculty during the COVID-19 pandemic remain unknown. As a part of a larger study, the purpose of this qualitative paper was to understand the lived experiences of resilience and HRQOL among a diverse sample of graduate students approximately 18 months into the COVID-19 pandemic in Ontario, Canada. A total of 14 students participated in semi-structured interviews exploring HRQOL domains, factors that supported/undermined participants' resilience, challenges/barriers to being resilient, and participants' inner strength. Thematic analysis revealed 5 themes: (1) cultural influences on resilience; (2) the role of privilege/power in shaping resilience; (3) how life stage and past experiences support resilience; (4) how the COVID-19 pandemic has undermined the resilience of equity-deserving groups; and (5) the role of disability/chronic pain. This work presents a unique dichotomy between how the COVID-19 pandemic has disrupted the lives of some graduate students, while simultaneously creating opportunities for others to thrive. Findings from this work underscore the importance of creating inclusive and accessible educational spaces to support graduate students' resilience and HRQOL currently, and in times of crisis., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright: © 2024 Shillington et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The past COVID-19 outbreak caused a huge impact on China's cruise industry. Now that China's cruise industry is about to recover, how to assess the risks faced by the cruise industry has become an important issue. On this basis, this paper constructs China's cruise tourism supply chain and supply chain risk assessment system based on the research contributions made by previous researchers, evaluates the risk indicators of China's cruise tourism supply chain based on the catastrophe theory, and derives the risk assessment results through the catastrophe progression method, which can be used as a reference for the safe operation of cruise lines in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gan, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 van Leeuwen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Working from home (WFH) has risen in popularity since the COVID-19 pandemic. There is an ongoing debate about the productivity implications of WFH, but the physical climate of the home office has received only limited attention. This paper investigates the effect of home office satisfaction and environment-improving behavior on productivity and burnout tendency for WFH employees. We surveyed over 1,000 Dutch WFH individuals about their home office and perceived WFH performance. We fit logistic regressions and structural equation models to investigate the effect of home office satisfaction and characteristics on self-reported productivity, burnout tendency, and willingness to continue WFH. Our results reveal that individual differences in WFH productivity are explained by heterogeneity in the physical home office environment. Higher satisfaction with home office factors is significantly associated with increased productivity and decreased burnout tendency. We continue by showing that more ventilation during working hours is associated with increased productivity, willingness to continue WFH, and burnout resilience. This effect is fully mediated by satisfaction with the home office. We find that higher home office satisfaction is associated with WFH success and air-quality-improving behavior is associated with higher satisfaction. Our results underline a holistic perspective such that investing in a healthy and objectively measured physical climate is a key aspect of the bright future of working from home. The move from the work office to the home office needs to be accompanied by careful design and investment in the quality of the office and its climate., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Stroom et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, we introduce the mixed-frequency data model (MIDAS) to China's insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China's insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China's insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China's insurance policymaking., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: As the global community begins recovering from the COVID-19 pandemic, the challenges due to its aftermath remain. This health crisis has highlighted challenges associated with airborne pathogens and their capacity for rapid transmission. While many solutions have emerged to tackle this challenge, very few devices exist that are inexpensive, easy to manufacture, and versatile enough for various settings., Methods: This paper presents a novel suction device designed to counteract the spread of aerosols and droplets and be cost-effective and adaptable to diverse environments. We also conducted an experimental study to evaluate the device's effectiveness using an artificial cough generator, a particle counter, and a mannequin in an isolated system. We measured droplet removal rates with simulated single and repeated cough incidents. Also, measurements were taken at four distinct areas to compare its effectiveness on direct plume versus indirect particle removal., Results: The device reduced airborne disease transmission risk, as evidenced by its capacity to decrease the half-life of aerosol volume from 23.6 minutes to 15.6 minutes, effectively capturing aerosol-sized droplets known for their extended airborne persistence. The suction device lessened the peak total droplet volume from peak counts. At 22 minutes post peak droplet count, the count had dropped 24% without the suction device and 43% with the suction device., Conclusions: The experiment's findings confirm the suction device's capability to effectively remove droplets from the environment, making it a vital tool in enhancing indoor air quality. Given the sustained performance of the suction device irrespective of single or multiple cough events, this demonstrates its potential utility in reducing the risk of airborne disease transmission. 3D printing for fabrication opens the possibility of a rapid iterative design process, flexibility for different configurations, and rapid global deployment for future pandemics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lordly et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper explores a brand-new interdisciplinary approach applied to an enduring problem: the communication of severe diagnoses. The moment when physicians explain the diagnosis to patients and their relatives is sensitive, particularly for a disease that is rarely diagnosed early. The first part of the article is dedicated to the context of this delicate doctor-patient interaction. With this framework in mind, the paper delves into the innovative interdisciplinary methodology developed in the pilot study Communi.CARE, conducted in a hospital in Northern Italy, which focuses on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). SARS-CoV-2 impact on the study development is highlighted. The study aims to explore the topic by combining different areas of expertise, including medicine, philosophy, sociology, and psychology. The contribution of philosophy is here presented as essential: it has a leading role in the conception of the study, its development, and the elaboration of results. It is shown throughout the study, from methodology to the analysis of results. Strengths and weaknesses of the methodology are discussed. In conclusion, further philosophical considerations on effective and ethical communication in this delicate context are recommended., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Monica Consolandi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: The COVID-19 pandemic has accelerated the transition to remote work, leading to increased attention on presenteeism and absenteeism among remote workers. Understanding the implications of these phenomena on worker health and productivity is crucial for optimizing remote work arrangements and developing policies to improve employee well-being., Objectives: This scoping review aims to examine the occurrence of presenteeism and absenteeism among remote workers during the COVID-19 pandemic and the interrelated physical and mental health issues during these periods., Methods: PsycINFO, Medline, Embase, CINAHL, Eric, Business Source Premier, SCOPUS, and sociological abstracts were searched resulting in 1792 articles. Articles were included if the population of interest was 18+ (i.e., working age), engaged in full or part-time work, and the employees shifted from in-person to remote work due to the COVID-19 pandemic. All study designs, geographical areas, and papers written post-onset of the COVID-19 pandemic were included; however, systematic reviews were excluded. Data was charted into Microsoft Excel by 2 independent reviewers., Results: The literature search identified 10 studies (i.e., seven cross-sectional studies, two qualitative studies, and one observational study). Five major overarching themes were identified specifically (1) telework and mental health (2) telework and physical health (3) worker benefits (4) gender dynamics and (5) difficulty navigating the teleworking environment. While remote work offers flexibility in terms of saved commute time and flexible work schedules, it also exacerbates challenges related to presenteeism, absenteeism, and work-life balance. These challenges include experiencing psychological distress, depression, anxiety, stress, sleep deprivation, musculoskeletal pain, difficulties concentrating at work for both women and working parents, struggles disconnecting after hours, and the inability to delineate between the work and home environment., Discussion: The findings suggest that remote work during the COVID-19 pandemic has both positive and negative implications for worker well-being and productivity. However, future research needs to incorporate the potential effects of telework frequency (full time vs. part time) on employee productivity and its role on presenteeism and absenteeism, to gain a more comprehensive understanding on remote work difficulties. Addressing these challenges requires proactive interventions and support mechanisms to promote worker health and productivity in remote settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Nowrouzi-Kia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In the process of hikers' choosing a destination, searching for information is one of the important elements, playing a decisive role in decision-making. Based on the Baidu Index for "hiking," this paper analyzes the spatial and temporal characteristics of and factors that influenced network attention to hiking in China from 2016 to 2021. The study found that (1) Network attention to hiking in China was generally relatively stable across the period studied, with highly uneven distribution between different months. The search volume was higher on weekends, and mobile searches increased year by year, far exceeding computer searches. (2) Different regions in China experienced different levels of network attention, with the highest levels in the east, followed by the center, and the lowest in the west. Except for East China, network attention to hiking was highly unevenly distributed within each region. (3) The COVID-19 pandemic increased the geographical concentration index and coefficient of variation but reduced the primacy index. A region's level of economic development, degree of network development, population size, and population age structure are proposed as factors that affect network attention to hiking., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright: © 2024 Xia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In this paper, we suggest a mathematical model of COVID-19 with multiple variants of the virus under optimal control. Mathematical modeling has been used to gain deeper insights into the transmission of COVID-19, and various prevention and control strategies have been implemented to mitigate its spread. Our model is a SEIR-based model for multi-strains of COVID-19 with 7 compartments. We also consider the circulatory structure to account for the termination of immunity for COVID-19. The model is established in terms of the positivity and boundedness of the solution and the existence of equilibrium points, and the local stability of the solution. As a result of fitting data of COVID-19 in Ghana to the model, the basic reproduction number of the original virus and Delta variant was estimated to be 1.9396, and the basic reproduction number of the Omicron variant was estimated to be 3.4905, which is 1.8 times larger than that. We observe that even small differences in the incubation and recovery periods of two strains with the same initial transmission rate resulted in large differences in the number of infected individuals. In the case of COVID-19, infections caused by the Omicron variant occur 1.5 to 10 times more than those caused by the original virus. In terms of the optimal control strategy, we formulate three control strategies focusing on social distancing, vaccination, and testing-treatment. We have developed an optimal control model for the three strategies outlined above for the multi-strain model using the Pontryagin's Maximum Principle. Through numerical simulations, we analyze three optimal control strategies for each strain and also consider combinations of the two control strategies. As a result of the simulation, all control strategies are effective in reducing disease spread, in particular, vaccination strategies are more effective than the other two control strategies. In addition the combination of the two strategies also reduces the number of infected individuals by 1/10 compared to implementing one strategy, even when mild levels are implemented. Finally, we show that if the testing-treatment strategy is not properly implemented, the number of asymptomatic and unidentified infections may surge. These results could help guide the level of government intervention and prevention strategy formulation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: A number of seroprevalence studies in Zambia document the extent of spread of acute SARS-CoV-2 infection, yet knowledge gaps still exist on symptoms and conditions that continue or develop after acute COVID-19 (long COVID). This is an important gap given the estimated prevalence of long COVID in other African countries. We assessed factors associated with long COVID at the initial visit to a post-acute COVID-19 (PAC-19) clinic and longitudinally among a cohort of patients with ≥2 review visits., Methods: We implemented a cross-sectional and longitudinal analysis of PAC-19 clinic patients from Aug-2020 to Jan-2023. The study outcome was long COVID; defined as the presence of new, relapsing, or persistent COVID-19 symptoms that interfere with the ability to function at home or work. Explanatory variables were demographic and clinical characteristics of patients which included sex, age group, presence of new onset medical conditions, presence of pre-existing comorbidities, vaccination status and acute COVID-19 episode details. We fitted logistic and mixed effects regression models to assess for associated factors and considered statistical significance at p<0.05., Results: Out of a total 1,359 PAC-19 clinic patients in the cross-sectional analysis, 548 (40.3%) patients with ≥2 PAC-19 clinic visits were in the longitudinal analysis. Patients' median age was 53 (interquartile range [IQR]: 41-63) years, 919 (67.6%) were hospitalized for acute COVID-19, and of whom 686 (74.6%) had severe acute COVID-19. Overall, 377 (27.7%) PAC-19 clinic patients had long COVID. Patients with hospital length of stay ≥15 days (adjusted odds ratio [aOR]: 5.37; 95% confidence interval [95% CI]: 2.99-10.0), severe acute COVID-19 (aOR: 3.22; 95% CI: 1.68-6.73), and comorbidities (aOR:1.50; 95% CI: 1.02-2.21) had significantly higher chance of long COVID. Longitudinally, long COVID prevalence significantly (p<0.001) declined from 75.4% at the initial PAC-19 visit to 26.0% by the final visit. The median follow-up time was 7 (IQR: 4-12) weeks., Conclusion: Factors associated with long COVID in Zambia were consistent both cross-sectionally at the initial visit to PAC-19 clinics and longitudinally across subsequent review visits. This highlights the importance of ongoing monitoring and tailored interventions for patients with comorbidities and severe COVID-19 to mitigate the long-term impacts of COVID-19., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Background: The COVID-19 pandemic has had enormous implications for the world of work. However, there has been relatively little focus on the employment and workforce challenges of the virus in relation to workforce health, beyond the immediate management of the spread of the disease. There is an important gap in understanding the ongoing workforce issues created by the significant incidence of Long COVID in the population., Aim: This paper examines the effects of Long COVID on employment and workers' mental health to contribute to understanding of work-limiting health conditions and to offer policy implications for COVID-19 and similar health conditions on employment and the workforce., Methods: A large national panel study for the UK is used to estimate the likelihood of exiting employment as well as on changes in working hours and general mental health and happiness of those who remain in work. The sample includes individuals 16 years and older who were in employment in January/February 2020 and followed during the pandemic 2020-2021. Long COVID is self-reported in the data. Informed by conceptual consideration of employment protection in the UK, two groups of individuals with Long COVID are defined based on the duration of symptoms. Group 1 has Long COVID 5-28 weeks after an infection with COVID-19, which is up to the maximum length of Statutory Sick Pay in the UK. Group 2 has symptoms for 29+ weeks, which is beyond the statutory entitlement to sickness pay. Panel regression models are fitted both with fixed-effects and random-effects. Individual and job characteristics are used as controls Those with no COVID-19 symptoms are the reference group., Results: In between-person comparison, Group 2 is at higher risk of exiting employment compared to those with no COVID-19 symptoms. Between-person estimates of mental health and well-being show negative effects of Long COVID for both groups but these are greatest in Group 2. Within-person estimates suggest that factors associated with earnings mediate the negative Long COVID effects on mental health in Group 1 and that Group 2 adapts to working with Long COVID. Group 1 is at risk of working zero hours (i.e. being on sick leave) but neither Group 1 nor Group 2 have a higher probability of working fewer hours compared to those with no COVID-19 symptoms. The negative impact of Long COVID on working hours stems primarily from working zero hours (sickness leave) rather than working fewer hours, suggesting a lack of accommodation by employers of Long COVID at work., Policy Implications: The extension of Statutory Sickness Pay and greater flexibility to manage partial (returns to) work would help preserve employment and mental health. Those with Long COVID for 12 months are likely to meet the definition of disability and so have a right to receive reasonable workplace adjustments., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Reuschke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To address the epidemic, such as COVID-19, the government may implement the home quarantine policy for the infected residents. The logistics company is required to control the risk of epidemic spreading while delivering goods to residents. In this case, the logistics company often uses vehicles and unmanned aerial vehicles (UAVs) for delivery. This paper studies the distribution issue of cold chain logistics by integrating UAV logistics with epidemic risk management innovatively. At first, a "vehicle-UAV" joint distribution mode including vehicles, small UAVs and large UAVs, is proposed. The green cost for vehicles and UAVs is calculated, respectively. The formula for infection risk due to large numbers of residents gathering at distribution centers to pick up goods is then derived. Furthermore, based on the control of infection risk, an optimization model is developed to minimize the total logistics cost. A modified ant colony algorithm is designed to solve the model. The numerical results show that the maximum acceptable risk and the crowd management level of distribution centers both have significant effects on the distribution network, logistics cost and number of new infections. Our study provides a new management method and technical idea for ensuring the needs of residents during the epidemic., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper investigates the association between several mental health indicators (depression, anxiety, stress, and loneliness) and the overall tendency to follow official recommendations regarding self-protection against COVID-19 (i.e., overall compliance). We employ panel data from the COME-HERE survey, collected over four waves, on 7,766 individuals (22,878 observations) from France, Germany, Italy, Spain, and Sweden. Employing a flexible specification that allows the association to be non-monotonic, we find a U-shaped relationship, in which transitions to low and high levels of mental health are associated with higher overall compliance, while transitions to medium levels of mental health are associated with less overall compliance. Moreover, anxiety, stress, and loneliness levels at baseline (i.e., at wave 1) also have a U-shaped effect on overall compliance later (i.e., recommendations are followed best by those with lowest and highest levels of anxiety, stress, and loneliness at baseline, while following the recommendations is lowest for those with moderate levels of these variables). These U shapes, which are robust to several specifications, may explain some of the ambiguous results reported in the previous literature. Additionally, we observe a U-shaped association between the mental health indicators and a number of specific health behaviours (including washing hands and mask wearing). Importantly, most of these specific behaviours play a role in overall compliance. Finally, we uncover the role of gender composition effects in some of the results. While variations in depression and stress are negatively associated with variations in overall compliance for men, the association is positive for women. The U-shaped relation in the full sample (composed of males and females) will reflect first the negative slope for males and then the positive slope for females., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Apouey et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: Much research on the early stages of the COVID-19 pandemic demonstrates the unequal impact on men and women in many countries but empirical evidence on later stages of the pandemic remains limited. The objective of this paper is to study differences between men and women in work location, the relative division of childcare, and perceived work-life balance across and throughout different phases of the pandemic using six waves of probability-based survey data collected in the Netherlands between April 2020 and April 2022 (including retrospective pre-pandemic measures)., Method: The study used descriptive methods (longitudinal crosstabulations) and multivariate modelling (cross-sectional multinomial logits, with and without moderators) in a repeated cross-sectional design., Results: Results suggest the pandemic is associated with several phase-specific differences between men and women in where they worked and their relative division of childcare in the Netherlands. Men were less likely than women to work fully from home at the start of each lockdown and to work on location during the first lockdown. Amongst parents, fathers increased their share of childcare throughout the first phase of the pandemic, and this increase remains visible at the end of the pandemic. Women in the Netherlands did not experience worse work-life balance than men throughout the pandemic, but mothers did experience worse work-life balance than fathers at various points during the pandemic., Discussion: Our results suggest varying long-term implications for gender inequality in society. Gender differences in work location raise concerns about the possible longer-term impact on gender inequalities in career development. Our findings on childcare suggest that many households have experienced different divisions of childcare at different stages of the pandemic, with some potential for longer-term change., Conclusion: Inequalities between men and women in work, childcare, and wellbeing have neither been alleviated by nor unilaterally worsened during the COVID-19 pandemic., Competing Interests: No authors have competing interests., (Copyright: © 2024 Yerkes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ibrahim, Lyons. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sasse et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide, raising the need to develop novel tools to provide rapid and cost-effective screening and diagnosis. Clinical reports indicated that COVID-19 infection may cause cardiac injury, and electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. This study aims to utilize ECG signals to detect COVID-19 automatically. We propose a novel method to extract ECG signals from ECG paper records, which are then fed into one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. To evaluate the quality of digitized signals, R peaks in the paper-based ECG images are labeled. Afterward, RR intervals calculated from each image are compared to RR intervals of the corresponding digitized signal. Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28.11 ms. The 1D-CNN model (SEResNet18), which is trained on the digitized ECG signals, allows to identify between individuals with COVID-19 and other subjects accurately, with classification accuracies of 98.42% and 98.50% for classifying COVID-19 vs. Normal and COVID-19 vs. other classes, respectively. Furthermore, the proposed method also achieves a high-level of performance for the multi-classification task. Our findings indicate that a deep learning system trained on digitized ECG signals can serve as a potential tool for diagnosing COVID-19. [ABSTRACT FROM AUTHOR]

Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired. [ABSTRACT FROM AUTHOR]

Enterprise resilience captures enterprises' ability to survive and develop under uncertainties and shocks. Taking the shock of COVID-19 as an example, this paper employs a unique firm-level national survey dataset collected from a sample of nearly 40,000 Chinese private enterprises in 2020 to systematically explore the relation between local business environment and enterprise resilience. Our estimation results using ordered probit model indicate that favorable business environment can significantly enhance enterprise resilience and mitigate the shock of COVID-19 on firm performance. Mechanism analysis further shows that enterprises under better business environment generally have greater resilience as the result of timely and effective government support, reliable supply of production factors and logistics service, and lower levels of financial constraints. Our study deepens the understanding of the economic consequences of business environment and also sheds a new light on enterprise resilience enhancement. [ABSTRACT FROM AUTHOR]