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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.

Objective: To survey the knowledge, attitude, and practices of Vietnamese pharmacists regarding the COVID-19 pandemic., Method: This cross-sectional, paper-based study was conducted from June to August 2020. A validated questionnaire (Cronbach's alpha = 0.84) was used to interview 1,023 pharmacists in nine provinces of Vietnam. Analysis of covariance was employed to identify factors associated with the knowledge of pharmacists. The best model was chosen by using the Bayesian Model Averaging method in R software version 4.0.4., Results: The mean knowledge score was 12.02 ± 1.64 (range: 6-15), which indicated that 93.4% of pharmacists had good knowledge of COVID-19. There was no difference in the average score between males and females (p > 0.05). The multivariate linear regression model revealed that the knowledge was significantly associated with pharmacists' age, education level, and residence (p < 0.001). About attitude and practices, pharmacists daily sought and updated information on the COVID-19 pandemic through mass media and the internet (social network and online newspapers). Nearly 48% of them conceded that they communicated with customers when at least one person did not wear a face mask at the time of the COVID-19 outbreak. At medicine outlets, many measures were applied to protect pharmacists and customers, such as equipping pharmacists with face masks and hand sanitizers (95.0%), using glass shields (83.0%), and maintaining at least one-meter distance between two people (85.2%)., Conclusion: The pharmacists' knowledge of COVID-19 transmission, symptoms, and prevention was good. Many useful measures against the spread of this perilous virus were applied in medicine outlets. However, pharmacists should restrict forgetting to wear face masks in communication with medicine purchasers. The government and health agencies should have practical remedies to reduce the significant differences in the COVID-19 knowledge of pharmacists among provinces and education-level groups., Competing Interests: The authors have declared that no competing interests exist.

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.

Following the fast spread of Covid-19 across Europe and North America in March 2020, many people started stockpiling commodities like toilet paper. Despite the high relevance for public authorities to adequately address stockpiling behavior, empirical studies on the psychological underpinnings of toilet paper stockpiling are still scarce. In this study, we investigated the relation between personality traits, perceived threat of Covid-19, and stockpiling of toilet paper in an online survey (N = 996) across 22 countries. Results suggest that people who felt more threatened by Covid-19 stockpiled more toilet paper. Further, a predisposition towards Emotionality predicted the perceived threat of Covid-19 and affected stockpiling behavior indirectly. Finally, Conscientiousness was related to toilet paper stockpiling, such that individuals higher in Conscientiousness tended to stockpile more toilet paper. These results emphasize the importance of clear communication by public authorities acknowledging anxiety and, at the same time, transmitting a sense of control. [ABSTRACT FROM AUTHOR]

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]

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: 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 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]

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]

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.)

As a response to the damage caused by the spread of COVID-19, the Chinese government has implemented severe quarantine measures that have greatly affected the operational patterns of small and medium-sized enterprises (SMEs). This paper explores the critical role of dynamic capabilities (DCs) in helping Chinese SMEs manage crises, adjust their business strategies, and mitigate the uncertainty caused by the epidemic. Although the importance of DCs in promoting organizational resilience is well recognized, academic research on their specific contributions to business model innovation (BMI) and SME performance improvement during crises remains scarce. Our study fills this gap by pioneering the development and empirical testing of a microintegrated mediation model linking DCs, BMI and organizational performance. By surveying 257 Chinese SMEs severely affected by a pandemic, we verify our hypotheses using partial least squares structural equation modeling (PLS-SEM). Our results strongly show a positive relationship between DCs and BMI and SME performance. In addition, we found that BMI plays a partial mediating role in the interrelationship between DCs and SME performance. Our findings clarify the critical role of BMI as a channel through which SMEs' DCs can be transformed into higher performance in the face of sudden crises. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Thus, our results not only contribute to the broader discussion of strategic management and organizational theory but also provide theoretical and practical insights into the mechanisms by which SMEs can increase their flexibility and resilience in a crisis. Importantly, this study suggests policy and market strategies that can support SMEs in leveraging DCs and BMI for sustained performance, thereby contributing valuable insights for policymakers and business leaders aiming to fortify economic stability and growth in the face of global health emergencies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Huang, Ichikohji. 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 triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ying 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.)

Classroom and staffroom floor swabs across six elementary schools in Ottawa, Canada were tested for SARS-CoV-2. Environmental test positivity did not correlate with student grade groups, school-level absenteeism, pediatric COVID-19-related hospitalizations, or community SARS-CoV-2 wastewater levels. Schools in neighbourhoods with historically elevated COVID-19 burden showed a negative but non-significant association with lower swab positivity., Competing Interests: ED works for DNA Genotek that provided sampling swabs in-kind for this study in an unrestricted fashion. DNA Genotek had no control over the findings, interpretations, or conclusions published in this paper. MF is a consultant for ProofDx, a start-up company that has created a point of care testing device for adults with COVID-19 using CRISPR. DM is a consultant for wastewater surveillance, World Bank. All other authors have no relevant conflicts to disclose. “This does not alter our adherence to PLOS ONE policies on sharing data and materials.”, (Copyright: © 2024 Thampi 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.)

As an emerging business modality and Internet format, live streaming e-commerce has developed rapidly since its emergence in 2016, especially since the outbreak of the COVID-19 epidemic in late 2019, when an increasing number of businesses from other industries attracted participation. However, with the development of the live streaming e-commerce industry, the industry's market environment is becoming increasingly chaotic. Therefore, during this period, government departments continuously formulate and implement relevant industry policies. In order to exploring the cooperation network structure, policy content distribution, and implementation effectiveness characteristics among publishers, this paper constructs a three-dimensional analysis framework of policy from the perspective of policy tools, policy effectiveness evaluation and policy publishers. The results show that in terms of policy tools, the overall structure of policy tools in the live streaming e-commerce industry is unreasonable, and different types of policy tools are significantly diverse. The proportion of environmental policy tools is greater than that of demand-based and supply-based policy tools, accounting for 62.97%, and among them, the tools related to industry regulation and management account for the largest proportion of the total, which greatly suppresses the enthusiasm of various entities in the industry for development. In terms of policy effectiveness evaluation, most of the policies do not formulate detailed long-, medium-, or short-term goals, nor are the policy priorities, incentive measures, or action modes perfect, indicating that the government's pushing and pulling forces for the live streaming e-commerce industry are insufficient. Finally, in the subject dimension of policy release, the synergy of relevant subjects is constantly improving, but there is also a phenomenon of over-concentration in the synergistic departments., 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.)

Introduction: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort., Methods: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors., Results: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response., Conclusion: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yaseen 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 manage the negative impact of job burnout for the researchers, especially during COVID-19 pandemic, is not easy. Thus, it is essential for educational institutions to provide them with the support they need to improve the person-organization (P-O) fit. Drawing upon the data from the Nature's Global Survey initiated in 2021, this paper analyzed 2,424 effective samples from eleven countries in the world to investigate how P-O fit impacts researchers' job burnout in different countries and their career stages during the COVID-19 pandemic. The findings show that both organizational support and P-O fit have significantly assisted researchers in all career stages to reduce job burnout. Moreover, P-O fit has a greater inhibitory effect on job burnout than organizational support. However, when resources are relatively scarce in some developing countries, it is more important to provide organizational support for researchers. Therefore, in order to improve the efficiency of organizational support and reduce researchers' job burnout, those aspects which are less fit but helpful should be increased appropriately. Moreover, it implies that it would be significant to emphasize the differentiated and career-stage-sensitive resources and support to researchers in different countries in the post-pandemic era to improve researchers' well-being and organizational performance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu, Xie. 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.)

Importance: The prevalence, pathophysiology, and long-term outcomes of COVID-19 (post-acute sequelae of SARS-CoV-2 [PASC] or "Long COVID") in children and young adults remain unknown. Studies must address the urgent need to define PASC, its mechanisms, and potential treatment targets in children and young adults., Observations: We describe the protocol for the Pediatric Observational Cohort Study of the NIH's REsearching COVID to Enhance Recovery (RECOVER) Initiative. RECOVER-Pediatrics is an observational meta-cohort study of caregiver-child pairs (birth through 17 years) and young adults (18 through 25 years), recruited from more than 100 sites across the US. This report focuses on two of four cohorts that comprise RECOVER-Pediatrics: 1) a de novo RECOVER prospective cohort of children and young adults with and without previous or current infection; and 2) an extant cohort derived from the Adolescent Brain Cognitive Development (ABCD) study (n = 10,000). The de novo cohort incorporates three tiers of data collection: 1) remote baseline assessments (Tier 1, n = 6000); 2) longitudinal follow-up for up to 4 years (Tier 2, n = 6000); and 3) a subset of participants, primarily the most severely affected by PASC, who will undergo deep phenotyping to explore PASC pathophysiology (Tier 3, n = 600). Youth enrolled in the ABCD study participate in Tier 1. The pediatric protocol was developed as a collaborative partnership of investigators, patients, researchers, clinicians, community partners, and federal partners, intentionally promoting inclusivity and diversity. The protocol is adaptive to facilitate responses to emerging science., Conclusions and Relevance: RECOVER-Pediatrics seeks to characterize the clinical course, underlying mechanisms, and long-term effects of PASC from birth through 25 years old. RECOVER-Pediatrics is designed to elucidate the epidemiology, four-year clinical course, and sociodemographic correlates of pediatric PASC. The data and biosamples will allow examination of mechanistic hypotheses and biomarkers, thus providing insights into potential therapeutic interventions., Clinical Trials.gov Identifier: Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT05172011., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Brett Anderson reported receiving direct support for work not related to RECOVER work/publications from Genentech and the National Institute of Allergy and Immunology. Walter Dehority reported receiving grant support from Merck and participating in research for the Moderna COVID-19 pediatric vaccine trial and the Pfizer Paxlovid trial. Alex Fiks reported receiving support from NJM insurance and personal consulting fees not related to this paper from Rutgers University and the American Academy of Pediatrics. Ashraf Harahsheh reported serving as a scientific advisory board member unrelated to this paper for OP2 DRUGS. Lawrence Kleinman reported serving as an unpaid member of the Board of Directors for the DARTNet Institute, as a principle investigator at Quality Matters, Inc., and as the Vice Chair for the Borough of Metuchen Board of Health. Dr. Kleinman also reported grant support for work not related to RECOVER work/publications from NIH, HRSA, and the Robert Wood Johnson Foundation. Dr. Kleinman also reported minority individual stock ownership in Apple Computer, Sanofi SA, Experion, GlaxoSmithKline, Magyar Bank, Regeneron Pharmaceuticals, JP Morgan Chase, and Amgen Inc. Torri Metz reported participating as a Principle Investigator in the medical advisory board for the planning of a Pfizer clinical trial of SARS-CoV-2 vaccination in pregnancy. She is also a principle investigator for a Pfizer study evaluating the pharmacokinetics of Paxlovid in pregnant people with COVID-19. Joshua Milner reported serving as a member of the Scientific Advisory Board for Blueprint Medicines, in a capacity unrelated to RECOVER work/publications. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2024 Gross 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.)

Celiac disease exhibits a higher prevalence among patients with coronavirus disease 2019. However, the potential influence of COVID-19 on celiac disease remains uncertain. Considering the significant association between gut microbiota alterations, COVID-19 and celiac disease, the two-step Mendelian randomization method was employed to investigate the genetic causality between COVID-19 and celiac disease, with gut microbiota as the potential mediators. We employed the genome-wide association study to select genetic instrumental variables associated with the exposure. Subsequently, these variables were utilized to evaluate the impact of COVID-19 on the risk of celiac disease and its potential influence on gut microbiota. Employing a two-step Mendelian randomization approach enabled the examination of potential causal relationships, encompassing: 1) the effects of COVID-19 infection, hospitalized COVID-19 and critical COVID-19 on the risk of celiac disease; 2) the influence of gut microbiota on celiac disease; and 3) the mediating impact of the gut microbiota between COVID-19 and the risk of celiac disease. Our findings revealed a significant association between critical COVID-19 and an elevated risk of celiac disease (inverse variance weighted [IVW]: P = 0.035). Furthermore, we observed an inverse correlation between critical COVID-19 and the abundance of Victivallaceae (IVW: P = 0.045). Notably, an increased Victivallaceae abundance exhibits a protective effect against the risk of celiac disease (IVW: P = 0.016). In conclusion, our analysis provides genetic evidence supporting the causal connection between critical COVID-19 and lower Victivallaceae abundance, thereby increasing the risk of celiac disease., 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: © 2024 Zou 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 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]

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]

Gautret and colleagues reported the results of a non-randomised case series which examined the effects of hydroxychloroquine and azithromycin on viral load in the upper respiratory tract of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients. The authors reported that hydroxychloroquine (HCQ) had significant virus reducing effects, and that dual treatment of both HCQ and azithromycin further enhanced virus reduction. In light of criticisms regarding how patients were excluded from analyses, we reanalysed the original data to interrogate the main claims of the paper. We applied Bayesian statistics to assess the robustness of the original paper's claims by testing four variants of the data: 1) The original data; 2) Data including patients who deteriorated; 3) Data including patients who deteriorated with exclusion of untested patients in the comparison group; 4) Data that includes patients who deteriorated with the assumption that untested patients were negative. To ask if HCQ monotherapy was effective, we performed an A/B test for a model which assumes a positive effect, compared to a model of no effect. We found that the statistical evidence was highly sensitive to these data variants. Statistical evidence for the positive effect model ranged from strong for the original data (BF+0 ~11), to moderate when including patients who deteriorated (BF+0 ~4.35), to anecdotal when excluding untested patients (BF+0 ~2), and to anecdotal negative evidence if untested patients were assumed positive (BF+0 ~0.6). The fact that the patient inclusions and exclusions are not well justified nor adequately reported raises substantial uncertainty about the interpretation of the evidence obtained from the original paper. [ABSTRACT FROM AUTHOR]

Background: We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles. Methods: We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses. Results: The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7–85.8 95%) and protocol registration (53.5%, 95% CI: 50.7–56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3–2.8) and code (0.4%, 95% CI: 0.4–0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited. Conclusion: While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders. [ABSTRACT FROM AUTHOR]

Background: A COVID-19 hospital guideline was implemented across all 18 acute hospitals in Wales in March 2020, promoting ward management of COVID pneumonitis and data collected across the first 3 Waves of the pandemic (Wave 1 March 1st 2020 to November 1st 2020, Wave 2 November 2st 2020 to February 21st 2021 and Wave 3 June 1st 2021 to December 14th 2021). The aim of this paper is to compare outcomes for patients by admission setting and type of ventilatory support given, with a particular focus on CPAP therapy. Methods: This is a retrospective observational study of those aged over 18 admitted to hospital with community acquired COVID-19 between March 2020 and December 2021. The outcome of interest was in-hospital mortality. Univariate logistic regression models were used to compare crude outcomes across the waves. Multivariable logistic regression models were used to assess outcomes by different settings and treatments after adjusting for Wave, age, sex, co-morbidity and deprivation. Results: Of the 7,803 records collected, 5,887 (75.4%) met the inclusion criteria. Analysis of those cases identified statistically significant outcome improvements across the waves for all patients combined (Waves 1 to 3: 31.5% to 18.8%, p<0.01), all ward patients (28.9% to 17.7%, p<0.01), and all ICU patients (44.3% to 32.2%, p = 0.03). Sub group analyses identified outcome improvements in ward patients without any oxygen therapy (Waves 1 to 3: 22.2% to 12.7%, p<0.01), with oxygen therapy only (34.0% to 12.9%, p<0.01) and with CPAP only (63.5% to 39.2%, p<0.01). The outcome improvements for ICU patients receiving CPAP only (35.7% to 24.6%, p = 0.31) or invasive ventilation (61.6% to 54.6%, p = 0.43) were not statistically significant though the numbers being admitted to ICU were small. The logistic regression models identified important age and comorbidity effects on outcomes. The multivariable model that took these into account suggested no statistically significantly greater risk of death for those receiving CPAP on the ward compared to those receiving CPAP in ICU (OR 0.89, 95% CI: 0.49 to 1.60). Conclusions: There were successive reductions in mortality in inpatients over the three Waves reflecting new treatments and better management of complications. Mortality for those requiring CPAP was similar in respiratory wards and ICUs after adjusting for differences in their respective patient populations. [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]

To control the SARS-CoV-2 pandemic, healthcare systems have focused on ramping up their capacity for epidemiological surveillance through viral whole genome sequencing. In this paper, we tested the performance of two protocols of SARS-CoV-2 nucleic acid enrichment, an amplicon enrichment using different versions of the ARTIC primer panel and a hybrid-capture method using KAPA RNA Hypercap. We focused on the challenge of the Omicron variant sequencing, the advantages of automated library preparation and the influence of the bioinformatic analysis in the final consensus sequence. All 94 samples were sequenced using Illumina iSeq 100 and analysed with two bioinformatic pipelines: a custom-made pipeline and an Illumina-owned pipeline. We were unsuccessful in sequencing six samples using the capture enrichment due to low reads. On the other hand, amplicon dropout and mispriming caused the loss of mutation G21987A and the erroneous addition of mutation T15521A respectively using amplicon enrichment. Overall, we found high sequence agreement regardless of method of enrichment, bioinformatic pipeline or the use of automation for library preparation in eight different SARS-CoV-2 variants. Automation and the use of a simple app for bioinformatic analysis can simplify the genotyping process, making it available for more diagnostic facilities and increasing global vigilance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Daviña-Núñez 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 coronavirus disease 2019 (COVID-19) pandemic has affected vulnerable households' livelihoods in developing countries. Using high-frequency phone survey data from the World Bank, we assess rural Indian households' vulnerability and poverty status during the pandemic. Results reveal that over three-fifths of Indian rural households are vulnerable to poverty in the context of COVID-19, despite India's evident progress in mitigating poverty in the pre-pandemic era. Poverty plays a major role in accounting for variations in household vulnerability; however, the impact of risks on household welfare is not negligible. On average, households with more members, older household heads, and more outmigrants are more vulnerable to poverty during the pandemic. The impacts of the gender of the household head, access to masks, consumption loans, and COVID-related information are nevertheless insignificant. Results stress the urgent necessity of deploying concerted interventions to strengthen household vulnerability in rural India., Competing Interests: The author has declared that no competing interests exist. The opinions expressed and arguments employed in this paper are the sole responsibility of the author and do not necessarily reflect the official position or opinions of the organization that the author is affiliated with., (Copyright: © 2024 Junyan Tian. 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: Health communication in the COVID-19 pandemic can be effectively implemented if all members of the populations, including marginalized population such as migrant domestic workers (MDWs), have good eHealth literacy. Lessons learned during this critical period may help improve planning and mitigation of the impacts of future health crises., Methods: This study aimed to examine and explore the eHealth literacy levels of the MDWs in Hong Kong during the COVID-19 pandemic by using a convergent mixed methods research design. A total of 1156 Hong Kong MDWs participated in a paper-based survey using a multistage cluster random sampling design for the quantitative component. eHealth literacy was measured using an eHealth literacy Scale (eHEALS). For the qualitative component, a purposive sampling of 19 MDWs participated in face-to-face, semi-structured, in-depth interviews. Descriptive statistics and multiple regression analyses were used to carry out the quantitative analysis, while thematic analysis was used for the qualitative analysis. Both quantitative and qualitative data were merged and integrated for mixed-methods analysis., Results: The meta-inferences of the quantitative and qualitative results mainly confirmed that MDWs in Hong Kong had good levels of eHealth literacy. The use of Instagram, YouTube and WhatsApp as the COVID-19 information sources, in addition to having an interest in the topic of the current spread of COVID-19 together with the Hong Kong government's policies related to COVID-19, were found to be associated with eHealth literacy., Conclusions: The eHealth literacy level of MDWs in Hong Kong was shown to be good and it was influenced by the use of popular social media platforms including Instagram, YouTube, and WhatsApp. It is realistic to suggest that such platforms should be harnessed for health communication during the pandemic. Yet, regulations to combat false information on these media are also urgently needed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Milanti 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.)

Brazil counts among the countries the hardest hit by the Covid-19 pandemic. A great deal has been said about the negative role played by President Bolsonaro's denialism, but relatively few studies have attempted to measure precisely what impact it actually had on the pandemic. Our paper conducts econometric estimates based on observational data at municipal level to quantitatively assess the 'Bolsonaro effect' over time from March 2020 to December 2022. To our knowledge, this paper presents the most comprehensive investigation of Bolsonaro's influence in the spread of the pandemic from two angles: considering Covid-19 mortality and two key transmission mitigation channels (social distancing and vaccination); and exploring the full pandemic cycle (2020-2022) and its dynamics over time. Controlling for a rich set of relevant variables, our results find a strong and persistent 'Bolsonaro effect' on the death rate: municipalities that were more pro-Bolsonaro recorded significantly more fatalities. Furthermore, evidence suggests that the president's attitude and decisions negatively influenced the population's behaviour. Firstly, pro-Bolsonaro municipalities presented a lower level of compliance with social distancing measures. Secondly, vaccination was relatively less widespread in places more in favour of the former president. Finally, our analysis points to longer-lasting and damaging repercussions. Regression results are consistent with the hypothesis that the 'Bolsonaro effect' impacted not only on Covid-19 vaccination, but has affected vaccination campaigns in general thereby jeopardizing the historical success of the National Immunization Program in Brazil., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 Razafindrakoto 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 aim of the paper is to provide an ex-post assessment of the impact of the COVID-19 pandemic on food insecurity in the EU-27 countries expressed by physical and economic food access. We analysed trade and price effects, together with food insecurity and malnutrition indicators. Actual levels of the indicators were compared with their pre-pandemic magnitudes and/or with counterfactual levels derived from predictive models. We also aimed to compare the objective statistics with the subjective consumers' perception of their households' food security. Our research indicates that the EU food trade was more resilient to COVID-19 impacts than the trade in non-food products, while food trade decreases were of a temporary nature. This did not affect the trade balance significantly; however, the import reduction threatened the physical food access in most EU countries. Regarding economic food access, the results indicate that the increase in food prices was offset by the increase in disposable income. It may suggest that the COVID-19 pandemic did not significantly affect the deterioration of economic access to food in the EU countries. However, the prevalence of severe food insecurity in the total population or the proportion of households reporting inability to afford a meal with meat, chicken, fish, or a vegetarian equivalent increased in 2020-2021 compared to 2019. This means that the comparative analysis of the real data on prices and households' income, as well as consumer financial situation and food consumption affordability, does not offer a clear answer concerning the impact of the COVID-19 pandemic on the food security of EU households., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pawlak 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 NIMH-funded Multilevel Community-Based Mental Health Intervention to Address Structural Inequities and Adverse Disparate Consequences of COVID-19 Pandemic on Latinx Immigrants and African Refugees study aims to advance the science of multilevel interventions to reduce the disparate, adverse mental health, behavioral, and socioeconomic consequences of the COVID-19 pandemic that are a result of complex interactions between underlying structural inequities and barriers to health care. The study tests three nested levels of intervention: 1) an efficacious 4-month advocacy and mutual learning model (Refugee and Immigrant Well-being Project, RIWP); 2) engagement with community-based organizations (CBOs); and 3) structural policy changes enacted in response to the pandemic. This community-based participatory research (CBPR) study builds on long-standing collaboration with five CBOs. By including 240 Latinx immigrants and 60 African refugees recruited from CBO partners who are randomly assigned to treatment-as-usual CBO involvement or the RIWP intervention and a comparison group comprised of a random sample of 300 Latinx immigrants, this mixed methods longitudinal waitlist control group design study with seven time points over 36 months tests the effectiveness of the RIWP intervention and engagement with CBOs to reduce psychological distress, daily stressors, and economic precarity and increase protective factors (social support, access to resources, English proficiency, cultural connectedness). The study also tests the ability of the RIWP intervention and engagement with CBOs to increase access to the direct benefits of structural interventions. This paper reports on the theoretical basis, design, qualitative and quantitative analysis plan, and power for the study., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Jessica R. Goodkind is an unpaid Board Member of United Voices for Newcomer Rights, a partner organization in the study., (Copyright: © 2024 Goodkind 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.)

Human-to-human contact plays a leading role in the transmission of infectious diseases, and the contact pattern between individuals has an important influence on the intensity and trend of disease transmission. In this paper, we define regular contacts and random contacts. Then, taking the COVID-19 outbreak in Yangzhou City, China as an example, we consider age heterogeneity, household structure and two contact patterns to establish discrete dynamic models with switching between daytime and nighttime to depict the transmission mechanism of COVID-19 in population. We studied the changes in the reproduction number with different age groups and household sizes at different stages. The effects of the proportion of two contacts patterns on reproduction number were also studied. Furthermore, taking the final size, the peak value of infected individuals in community and the peak value of quarantine infected individuals and nucleic acid test positive individuals as indicators, we evaluate the impact of the number of random contacts, the duration of the free transmission stage and summer vacation on the spread of the disease. The results show that a series of prevention and control measures taken by the Chinese government in response to the epidemic situation are reasonable and effective, and the young and middle-aged adults (aged 18-59) with household size of 6 have the strongest transmission ability. In addition, the results also indicate that increasing the proportion of random contact is beneficial to the control of the infectious disease in the phase with interventions. This work enriches the content of infectious disease modeling and provides theoretical guidance for the prevention and control of follow-up major infectious diseases., Competing Interests: NO authors have competing interests., (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.)

The spread of the COVID-19 had profoundly affected the development of the air transportation. In order to determine the changes in air transportation volume associated with the development of the epidemic, this paper takes Southwest China as the study area. Monthly data and methods, such as the coefficient of variation, rank-size analysis and spatial matching index, were applied. The results found that: (1) during 2020-2022, there was a positive relationship between passenger volume and epidemic development, while freight volume increased for most airports in the first quarter of 2020-2022, particularly in the eastern region; (2) From the perspective of changes in air transportation volume under the development of the COVID-19, among various types of airports, the changes in transportation volume of main trunk airports were more significant than those of regional feeder airports in remote areas; (3) however, under the influence of the epidemic, main trunk airports still exhibited stronger attraction in passenger volume. That is to say, the passengers who chose to travel by air still tended to choose the main trunk airports and formed the agglomeration distribution pattern which around high-level airports in the provincial capital. Whereas the freight volume had a tendency of equalization among airports in Southwest China; (4) Over the course of time, the consistency of the spatial distribution of the number of cases and the passenger or freight volume in southwest China gradually increased. Among them, the spatial matching rate of the passenger volume and the number of COVID-19 cases was always higher than that of the cases and freight volume, which might indicate that there was a stronger correlation relationship. Therefore, it is proposed that the construction of multi-center airport system should be strengthened, the resilience of the route network for passenger transportation should be moderately enhanced, and the risk-resistant capacity of mainline airports and airports in tourist cities should be upgraded, so as to provide references for the orderly recovery of civil aviation and regional development., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li 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.)

Automatic and accurate segmentation of medical images plays an essential role in disease diagnosis and treatment planning. Convolution neural networks have achieved remarkable results in medical image segmentation in the past decade. Meanwhile, deep learning models based on Transformer architecture also succeeded tremendously in this domain. However, due to the ambiguity of the medical image boundary and the high complexity of physical organization structures, implementing effective structure extraction and accurate segmentation remains a problem requiring a solution. In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module. The convolutional structure encoder can extract fine spatial contextual details in images. Meanwhile, the Transformer structure encoder is designed using a hierarchical Swin Transformer architecture to model global contextual information. The novel feature fusion decoder integrates the multi-scale representation from two encoders and selects features that focus on segmentation tasks by channel attention mechanism. Further, a deep supervision module is used to accelerate the convergence of the proposed method. Extensive experiments demonstrate that, compared to the other seven models, the proposed method achieves state-of-the-art results on four segmentation tasks: skin lesion segmentation, polyp segmentation, Covid-19 lesion segmentation, and MRI cardiac segmentation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li 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.)

Post vaccine immunity following COVID-19 mRNA vaccination may be driven by extrinsic, or controllable and intrinsic, or inherent health factors. Thus, we investigated the effects of extrinsic and intrinsic on the peak antibody response following COVID-19 primary vaccination and on the trajectory of peak antibody magnitude and durability over time. Participants in a longitudinal cohort attended visits every 3 months for up to 2 years following enrollment. At baseline, participants provided information on their demographics, recreational behaviors, and comorbid health conditions which guided our model selection process. Blood samples were collected for serum processing and spike antibody testing at each visit. Cross-sectional and longitudinal models (linear-mixed effects models) were generated to assess the relationship between selected intrinsic and extrinsic health factors on peak antibody following vaccination and to determine the influence of these predictors on antibody over time. Following cross-sectional analysis, we observed higher peak antibody titers after primary vaccination in females, those who reported recreational drug use, younger age, and prior COVID-19 history. Following booster vaccination, females and Hispanics had higher peak titers after the 3rd and 4th doses, respectively. Longitudinal models demonstrated that Moderna mRNA-1273 recipients, females, and those previously vaccinated had increased peak titers over time. Moreover, drug users and half-dose Moderna mRNA-1273 recipients had higher peak antibody titers over time following the first booster, while no predictive factors significantly affected post-second booster antibody responses. Overall, both intrinsic and extrinsic health factors play a significant role in shaping humoral immunogenicity after initial vaccination and the first booster. The absence of predictive factors for second booster immunogenicity suggests a more robust and consistent immune response after the second booster vaccine administration., Competing Interests: Florian Krammer has been consulting for Curevac, Seqirus and Merck and is currently consulting for Pfizer, Third Rock Ventures, Avimex and GSK. He is named on several patent regarding influenza virus and SARS-CoV-2 virus vaccines, influenza virus therapeutics and SARS-CoV-2 serological tests. Some of these technologies have been licensed to commercial entities and Dr. Krammer is receiving royalties from these entities. Dr. Krammer is also an advisory board member of Castlevax, a spin-off company formed by the Icahn School of Medicine at Mount Sinai to develop SARS-CoV-2 vaccines. The Krammer laboratory has received funding for research projects from Pfizer, GSK and Dynavax and three of Dr. Krammer’s mentees have recently joined Moderna. All other 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. This does not alter our adherence to PLOS ONE policies on sharing data and/or materials., (Copyright: © 2024 Tribin 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.)

Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue., Competing Interests: NO authors have competing interests., (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.)