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Introduction: COVID-19 vaccination substantially reduces morbidity and mortality associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe illness. However, despite effective COVID-19 vaccines many questions remain about the efficacy of vaccines and the durability and robustness of immune responses, especially in immunocompromised persons. The NCI-funded Serological Sciences Network (SeroNet) is a coordinated effort including 11 sites to advance research on the immune response to SARS-CoV-2 infection and COVID-19 vaccination among diverse and vulnerable populations. The goals of the Pooling Project are: (1) to conduct real-world data (RWD) analyses using electronic medical records (EMR) data from four health care systems (Kaiser Permanente Northern California, Northwell Health, Veterans Affairs-Case Western, and Cedars-Sinai) to determine vaccine effectiveness in (a) cancer patients;(b) autoimmune diseases and (c) solid organ transplant recipients (SOTR);(2) to conduct meta-analyses of prospective cohort studies from eight SeroNet institutions (Cedars-Sinai, Johns Hopkins, Northwell Health, Emory University, University of Minnesota, Mount Sinai, Yale University) to determine post-vaccine immune responses in (a) lung cancer patients;(b) hematologic cancers/hematopoietic stem cell transplant (HSCT) recipients;(c) SOTR;(d) lupus. Method(s): For our RWD analyses, data is extracted from EMR using standardized algorithms using ICD-10 codes to identify immunocompromised persons (hematologic and solid organ malignancy;SOTR;autoimmune disease, including inflammatory bowel disease, rheumatoid arthritis, and SLE). We use common case definitions to extract data on demographic, laboratory values, clinical co morbidity, COVID-19 vaccination, SARS-CoV-2 infection and severe COVID-19, and diseasespecific variables. In addition, we pool individual-level data from prospective cohorts enrolling patients with cancer and other immunosuppressed conditions from across network. Surveys and biospecimens from serology and immune profiling are collected at pre-specified timepoints across longitudinal cohorts. Result(s): Currently, we have EMR data extracted from 4 health systems including >715,000 cancer patients, >9,500 SOTR and >180,000 with autoimmune conditions. Prospective cohorts across the network have longitudinal data on >450 patients with lung cancer, >1,200 patients with hematologic malignancies, >400 SOTR and >400 patients with lupus. We will report results examining vaccine effectiveness for prevention of SARS-CoV-2 infection, severe COVID-19 and post-acute sequelae of COVID-19 (PAS-C or long COVID) in cancer patients compared to other immunocompromised conditions. Conclusion(s): Our goal is to inform public health guidelines on COVID-19 vaccine and boosters to reduce SARS-CoV-2 infection and severe illness in immunocompromised populations.
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In the practice of COVID-19 prevention and control in China, the home quarantine policy directly connects and manages the residents, which plays a significant role in preventing the spread of the epi-demic in the community. We evaluate the effectiveness of current home quarantine policy in the actual execution process based on the evolutionary game relationship between the community and res-idents. This paper establishes a double-layer coupled complex network game model, and uses the multi-agent modeling method to study the game relationship between the community and residents in the context of home quarantine policies. The results show that initial strategy of the community with strict supervision and reasonable government reward allocation will increase the proportion of the residents complying with the quarantine rule. When 80% of the communities chose to supervise strictly at the beginning, people are more likely to follow the rules. While when the residents can only get 20% of the government's reward, the proportion of choosing to violate the quarantine rules is much higher than that when they can get 80% of the reward. Besides, the structure of small-world network and environmental noise will also affect the residents' strategy. As the probability of reconnection of the small-world network rises from 0.2 to 0.8, the proportion of residents who choose to comply with the strategy becomes much higher. When the environmental noise reaches 0.5, the ratio of residents who choose to violate the strategy is higher than the ratio of complianc. The study is helpful to provide the basis for the government to formulate the quarantine policy and propose an optimization for making effective quarantine measures. In this way, the government can adjust the parameters to make residents achieve the possible level of compliance with quarantine policies as high as possible to contain the spread of the epidemic.
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[Background] In the post-pandemic period, attention has been paid to the study of psychological stage changes in various groups. Under the stress of pandemics, how to control negative emotions such as anxiety symptoms will have an important impact on medical students' professional identity and future professional competence. [Objective] This study is designed to explore the characteristics of stress and anxiety symptoms of medical students in different stages of the post COVID-19 period, and potential mediating role of psychological resilience in the relationship between stress and anxiety symptoms. [Methods] By convenience sampling method, 3 000 medical students from three medical colleges in Shaanxi Province were selected and completed an online survey reporting the Self-Rating Anxiety Scale (SAS), Stress Scale for College Student (SSCS), and Resilience Scale of Adults (RSA) to assess their stress, psychological resilience, and anxiety symptoms in September and November 2020. SPSS 25.0 software was used to perform dependent-sample t test, variance analysis, Pearson correlation analysis, and mediating effect test (hierarchical regression analysis). [Results] A total of 2 894 valid questionnaires were recovered and the valid recovery rate was 96.5%. The overall scores of stress, psychological resilience, and anxiety symptoms of selected medical students were 56.61+/-17.17, 166.88+/-28.55, and 40.45+/-9.67, respectively in the post COVID-19 period. The positive rate of high stress was 72.2%, and the positive rate of anxiety symptoms was 16.0%. There were significant differences in anxiety symptoms scores between the high and the low stress level groups (42.16+/-9.92, 35.99+/-7.30) (P < 0.01). There were significant differences in scores of stress, psychological resilience, and anxiety symptoms among different grade groups (P < 0.01). The pearson correlation analysis results showed that the stress score was positively correlated with the anxiety symptom score (r=0.417, P < 0.01) and negatively correlated with the psychological resilience score (r=-0.344, P < 0.01);the psychological resilience score was negatively correlated with the anxiety symptom score (r=-0.495, P < 0.01). The hierarchical regression analysis results found that stress had a positive effect on anxiety symptoms (b=0.280, P < 0.01), and a negative effect on psychological resilience (b=-0.344, P < 0.01);psychological resilience negatively affected anxiety symptoms (b=-0.398, P < 0.01), and played a partial mediating role in the relationship between stress and anxiety symptoms (effect value was 0.137) that accounted for 32.8% of the total effect. [Conclusion] In the post COVID-19 period, medical students have a superposition of high stress and high anxiety symptoms. Psychological resilience is a protective factor for anxiety symptoms and plays a partial mediating role in the relationship between stress and anxiety symptoms.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.
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Background With the outbreak of the coronavirus disease 2019 (COVID-19), nurses have won well-deserved recog-nition for their indispensable roles in providing humane and professional healthcare for patients. However, by the nature of their role working at the forefront of patient care, nurses are prone to experiencing mental health conse-quences. Therefore, we pay attention to measuring the magnitude of psychological symptoms and identifying associ-ated factors among nurses in China.Methods We launched a nationwide, cross-sectional survey of nurses who worked in secondary or tertiary hospitals and public or private hospitals from 30 provinces in China. The prevalence and severity of symptoms of burnout, depression, and anxiety were investigated, respectively. Multivariable logistic regression analyses were performed to identify factors associated with each psychological symptom.Findings A total of 138 279 respondents who worked in 243 hospitals completed this survey. A substantial propor-tion of nurses reported symptoms of burnout (34%), depression (55.5%), and anxiety (41.8%). In line with the dispro-portionality of economic development, we noted that the middle or western region was an independent risk factor for depression and anxiety. Compared with those working in the secondary hospital, nurses who worked in tertiary hospitals were associated with a higher likelihood of burnout and depression.Interpretation Nurses are experiencing emotional, physical, and mental exhaustion during the COVID-19 epidemic. Governments and health policymakers need to draw attention to reinforcing prevention and ameliorating counter-measures to safeguard nurses' health.Funding The strategic consulting project of the Chinese Academy of Engineering [2021-32-5]. Advanced Institute of Infomation Technology, Peking University, Zhejiang Province [2020-Z-17]Copyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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Public debt in developing economies rose at a fast clip during 2020–2021, at least partly due to the onset of the global COVID-19 pandemic. Nobel laureate Paul Krugman opined in early 2021 that "fighting covid is like fighting a war.” This paper empirically examines trends in debt and economic growth around the onset of three types of calamities, namely natural disasters, armed conflicts, and external debt distress in developing countries. The estimations provide quantitative estimates of differences in GDP growth and debt trends in economies suffering episodes of calamities relative to the trends observed in economies not experiencing calamities. The paper finds that debt and growth evolve quite differently depending on the type of calamity. With the empirical evidence in hand, the authors argue that debt-financed reconstruction efforts after natural disasters, and thus plausibly in the aftermath of the pandemic, can help accelerate growth after such disasters with lower debt burden than in the aftermath of episodes of armed conflict without necessarily incurring the economic costs associated with episodes of debt restructuring. However, the implied upward trajectory of the debt to GDP ratio in developing economies is not trivial, even after post-disaster growth upticks, which raises concerns about long-term debt sustainability after episodes of reconstruction after natural disasters. If so, the time for orderly preemptive debt restructuring might be approaching quickly since recoveries after debt defaults tend to be more costly. © 2023, International Monetary Fund.
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Background: With the outbreak of the coronavirus disease 2019 (COVID-19), nurses have won well-deserved recognition for their indispensable roles in providing humane and professional healthcare for patients. However, by the nature of their role working at the forefront of patient care, nurses are prone to experiencing mental health consequences. Therefore, we pay attention to measuring the magnitude of psychological symptoms and identifying associated factors among nurses in China. Methods: We launched a nationwide, cross-sectional survey of nurses who worked in secondary or tertiary hospitals and public or private hospitals from 30 provinces in China. The prevalence and severity of symptoms of burnout, depression, and anxiety were investigated, respectively. Multivariable logistic regression analyses were performed to identify factors associated with each psychological symptom. Findings: A total of 138 279 respondents who worked in 243 hospitals completed this survey. A substantial proportion of nurses reported symptoms of burnout (34%), depression (55·5%), and anxiety (41·8%). In line with the disproportionality of economic development, we noted that the middle or western region was an independent risk factor for depression and anxiety. Compared with those working in the secondary hospital, nurses who worked in tertiary hospitals were associated with a higher likelihood of burnout and depression. Interpretation: Nurses are experiencing emotional, physical, and mental exhaustion during the COVID-19 epidemic. Governments and health policymakers need to draw attention to reinforcing prevention and ameliorating countermeasures to safeguard nurses' health. Funding: The strategic consulting project of the Chinese Academy of Engineering [2021-32-5]. Advanced Institute of Infomation Technology, Peking University, Zhejiang Province [2020-Z-17] © 2022 The Author(s)
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The prevalence of misinformation, both online and offline, has prompted a great demand of fact verification. Table-based fact verification aims to check whether a textual claim is supported or refuted based on relational tables. However, most of the existing approaches are in a closed-domain setting, which may not be realistic in practice. To address this problem, in this paper, we introduce OPENTFV, a user-friendly system that supports open domain table-based fact verification. Given a claim input by an end-user, OPENTFV retrieves the relevant tables, and provides a verification result for each table with an intuitive interpretation in natural language. We have implemented OPENTFV and demonstrated OPENTFV in two representative scenarios, COVID-19 claims fact verification based on academic tables and general fact verification on Wiki-tables.
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As patients infected with SARS-CoV-2 are more likely to have trouble breathing, a great demand for ventilators has been generated since the COVID-19 is continuing to spread around the world. However, the research and development of ventilator control suffer from high cost, slow efficiency, and lack of automation, especially regarding the estimation of ventilator pressure. In this paper, to address this challenge and help control the mechanical ventilators better, we develop a Transformer-based deep learning method for the prediction of ventilator pressure. Based on the dataset provided by Google Brain in a Kaggle competition, we connect the Transformer encoders by residual connections to extract features from the time-series ventilator data, and successfully achieve the goal to predict the ventilator pressure with excellent performance. After applying the K-Fold cross validation technique, our Transformer-based model reaches a mean absolute error 0.1311 on the private test set. This result ranks 67/2605 (top 2.6%) in the leaderboard of Google Brain-Ventilator Pressure Prediction competition, and can get a silver medal in this Kaggle competition. This work could accelerate the development of new methods to overcome the cost barrier of ventilator control. © 2022 IEEE.
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Novel biologic therapies have revolutionized the treatment of psoriasis and atopic dermatitis. Although they are generally safe, they are immunomodulatory and therefore unique considerations apply in regards to infections and vaccine administration. This review aims to provide a clear and practical guide for dermatologists or other healthcare providers to reference when caring for psoriasis or atopic dermatitis patients being treated with biologic therapies using currently available guidelines and clinical data. Vaccinations for approved biologics including TNF alpha, IL-12/23, IL-23, IL-17, and IL-4/13 inhibitors will be discussed, with a special note on current COVID-19 vaccination recommendations.
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The COVID-19 pandemic has led to a multiplicity of research publications related to various aspects of coronavirus. Research topics range from COVID-19 transmission mechanisms to the public health response of various countries, and the publications need to be categorized for easier and more efficient access to resources. This paper explores various machine learning-based document classification techniques to categorize COVID-19 related literature. We integrate a novel terminology dictionary with machine learning models to study the dictionary's impact on the effectiveness of various classification techniques. We report a slight boost to F1 scores as a result of our modifications. © 2021 IEEE.
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In this paper, by analyzing the characteristics of the news reports of the COVID-19 epidemic events, we extract the event ancestor pairs from the text, extract the relationship between the events through attention-based bidirectional LSTM, and display them in the form of EEG model, which is conducive to the analysis of the evolution of epidemic events. The method proposed in this paper provides a new idea for the evolution of network events. The constructed event map can clearly show the evolution path of network events, monitor key nodes of network events, assist relevant management departments to formulate corresponding measures, and lead the events forward in a positive way. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Why have some countries done better than others in dealing with the coronavirus crisis so far? One popular answer is in terms of politics: everything depends on state capacity, the level of political trust in society, and the quality of leadership. This paper suggests the need to go beyond politics and turn to ethics. If one does not delve into the ethical spirit and substance that underlie tangible political decisions and activities to combat the coronavirus pandemic in a state, one will fail to see the cultural momentum of the people's responses in that state and miss the moral foundation of the social practices embedded within that state's civilization in comparison with other civilizations. In particular, this paper argues that the spirit and substance of Chinese ethics differ from those of the contemporary mainstream Western ethics characteristic of secular principlism, which, although they possess important advantages and merits, suffer from a series of defects and failures, including untenable reductionism, a type of dogmatism, and even radicalism. In contrast, Confucian civilization provides the Chinese with a virtue ethics that is not principlism. It is rather an exposition of Confucian virtue (de), as a powerful but peaceful moral force, that is entrenched within the fundamental structures of the universe (as portrayed in the images of yin-yang, the eight trigrams, and the 64 hexagrams in the Classic of Change) and within the ritual activities of human beings (as described in the ceremonial and minute rituals in the three Confucian ritual classics) to shape the Confucian moral character. Confucian virtue principles and rules are implicit in such structures and the rituals to be formulated in connection with them, but they cannot be created through pure reason. They play their roles in human practices along with structures and rituals but can never exhaust their richness and profundity. The paper indicates that this virtue ethics contains a Confucian notion of harmonious freedom (that can counter scientific determinism) and a familist ethic (that can be adopted to check and balance runaway contractualism), which can be fruitfully used to direct political decisions and activities to combat the coronavirus pandemic and to accomplish peaceful and fruitful outcomes in society.