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Objective: To investigate the wearing of masks and the knowledge of masks among high-risk positions for overseas import and pollution transmission. Methods: From May 14 to 17, 2022, a convenient sampling method was used to conduct an online survey among 963 workers in high-risk positions for overseas import and pollution transmission in Beijing. The behaviors of individual use and wearing masks, the distribution and supervision of the unit, the knowledge of personal mask protection and the subjective feelings of wearing masks were analyzed. The χ(2) test and logistic regression model were used to analyze the influencing factors of the correct selection of masks. Results: The majority of the workers in high-risk positions for overseas import and pollution transmission were male (86.0%, 828/963), age concentration in 18-44 years old (68.2%, 657/963), and the majority of them had college or bachelor degrees (49.4%, 476/963). 79.4%(765/963) of the workers chose the right type of masks, female, 45-59 years old and high school education or above were the risk factors for correct selection of masks (P <0.05). Workers had good behaviors such as wearing/removing masks, but only 10.5% (101/963) could correctly rank the protective effect of different masks. 98.4% (948/963) of the workers believed that their work units had provided masks to their employees, and 99.1% (954/963) and 98.2%(946/963) of them had organized training and supervision on the use of masks, respectively. 47.4%(456/963) of the workers were uncomfortable while wearing masks. Conclusion: The overall selection and use of masks among occupational groups in high-risk positions for overseas import and pollution transmission in China need to be further standardized. It is necessary to strengthen supervision and inspection on the use of masks among occupational groups, and take improvement measures to improve the comfort of wearing masks.
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Masks , Humans , Male , Female , Adolescent , Young Adult , Adult , Middle Aged , Cross-Sectional Studies , China , Surveys and Questionnaires , BeijingABSTRACT
Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.
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This study highlights the major challenges faced by hotel interns in their career development and the human resource management of hotels in the current macroeconomic environment, particularly during the COVID-19 pandemic. The paper developed a conceptual model for organizational identification, turnover intention, and perceived alternative job opportunities in the context of hotel internships. A total of 350 samples were collected from hotel internships in Macau. The presented results indicate that organizational identification has a significant negative impact on turnover intention. In addition, alternative job opportunities do not moderate the relationship between organizational identification and turnover intention. The results also showed that females had a higher level of evaluative identification for hotel internships compared to males. In addition, interns from high-income families had a higher level of evaluative identification compared to those from low- and middle-income families. The theoretical contribution extends the concept of organizational socialization to include internship stages in the field of hospitality management. Finally, this paper proposes measures for managing hotel internships during the COVID-19 pandemic.
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Coronavirus disease 2019(COVID-19) poses a challenge to hospitals for the prevention and control of public health emergencies. As the main battlefield of preventing and controlling COVID-19, large public hospitals should develop service protocols of diagnosis and treatment for outpatient, emergency, hospitalization, surgery, and discharge. The construction of medical protocols should be based on the risk factors of key points and focused on pre-inspection triage and screening, to establish a rapid response mechanism to deal with exogenous and endogenous risk factors. Implementation of all-staff training and assessment, strengthening the information system, and use of medical internet service are important. This study explores the construction of medical protocols in large public hospitals during the pandemic, and provides a reference for the orderly diagnosis and treatment in hospitals during the pandemic.Copyright © 2021, Peking Union Medical College Hospital. All rights reserved.
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Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.
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In this article, we analyze a second-order stochastic SEIR epidemic model with latent infectious and susceptible populations isolated at home. Firstly, by putting forward a novel inequality, we provide a criterion for the presence of an ergodic stationary distribution of the model. Secondly, we establish sufficient conditions for extinction. Thirdly, by solving the corresponding Fokker–Plank equation, we derive the probability density function around the quasi-endemic equilibrium of the stochastic model. Finally, by using the epidemic data of the corresponding deterministic model, two numerical tests are presented to illustrate the validity of the theoretical results. Our conclusions demonstrate that nations should persevere in their quarantine policies to curb viral transmission when the COVID-19 pandemic proceeds to spread internationally. © 2023 by the authors.
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The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies.
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The outbreak of multiple disaster sites during the coronavirus disease 2019 (COVID-19) pandemic has presented challenges due to varying access time intensity, population density, and medical resources at each site. To address these issues, this study focuses on 13 districts and counties in Wuhan, China. The importance of each research area is analyzed using the improved PageRank and TOPSIS algorithms to determine the optimal site selection plan. Additionally, a particle swarm algorithm is used to construct an emergency material dispatching model that targets both distribution and site selection costs to solve the multi-distribution center dispatching problem. The results suggest that constructing 10 distribution centers can satisfy the demand for epidemic prevention and control in Wuhan city while saving costs associated with site selection and material distribution. Compared to the previous optimal solution, the distribution and site selection costs under the optimal solution decreased by 27.9% and 17.82%, respectively. This approach can serve as a basis for dispatching emergency materials during public health emergencies. © 2023 by the authors.
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Food is a necessity of people's life, with its unique characteristics and irreplaceability. Due to the sudden, unpredictable and destructive nature of the epidemic, countries need to take particularly strict epidemic prevention measures to manage and control the epidemic in areas with severe development of the epidemic, which affects the trans-regional transportation of food and other agricultural products, and makes food supply become mainly local supply and become a limited resource. Through the integration of food supply chain in response to the outbreak of COVID-19, the service efficiency and cost can be improved, so that the community residents affected by the disaster can get high-quality food more quickly. © 2023 SPIE.
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Separation membranes play a crucial role in the functioning of artificial organs, such as hemodialysis machines, membrane oxygenators, and artificial liver models. The current COVID-19 pandemic has highlighted the importance of these technologies in the medical community. However, membrane technology in artificial organs faces significant challenges, such as the clearance of low-middle-molecule and protein-bound toxins and limited blood compatibility. In this review, we will discuss the separation mechanisms, separation performance, and biocompatibility of different types of separation membranes used in artificial organs. We will also highlight the opportunities and challenges for next-generation membrane technology in this field, including the need for improved clearance of toxins and increased blood compatibility, as well as the potential for microfluidic devices.
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Background The coronavirus disease 2019 (COVID-19) cases continue to rise, and the demand for medical treatment and resources in healthcare systems surges. Assessing the viral shedding time (VST) of patients with COVID-19 can facilitate clinical decision making. Although some studies have been conducted on the factors affecting the VST of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), few prediction models are currently available. Methods This retrospective study included the consecutive patients with COVID-19 admitted to Xi'an Chest Hospital in Shaanxi, China, for treatment between December 19, 2021 and February 5, 2022. The clinical data of the patients were extracted from their electronic medical records. Combining significant factors affecting the VST, a nomogram was developed to predict the VST of the SARS-CoV-2 Delta variant in patients with COVID-19. Results We included 332 patients in this study. The average VST was 21 d. VST was significantly prolonged in patients with severe clinical symptoms, sore throat, old age, long time from onset to diagnosis, and an abnormal white blood cell count. Consequently, we developed a nomogram prediction model using these 5 variables. The concordance index (C-index) of this nomogram was 0.762, and after internal validation using bootstrapping (1000 resamples), the adjusted C-index was 0.762. The area under the nomogram's receiver operator characteristic curve showed good discriminative ability (0.965). The calibration curve showed high consistency. The VST was prolonged in the group with lower model fitting scores according to the Kaplan-Meier curve (χ2=286, log-rank P < 0.001). Conclusions We developed a nomogram for predicting VST based on 5 easily accessible factors. It can effectively estimate the appropriate isolation period, control viral transmission, and optimize clinical strategies. © Wolters Kluwer Health, Inc. All rights reserved.
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In the context of coronavirus disease 2019 (COVID-19), the leverage of specific codes of international classification of diseases(ICD) will substantially help standardize the process of data collection, classification and statistics of COVID-19-related conditions, thus facilitating the rapid research and development of diagnosis and treatment, dynamic monitoring of epidemic trend, as well as effectiveness evaluation of preventive and therapeutic measures taken in the fight against COVID-19. This review summarized and interpreted the latest ICD-10 and ICD-11 classification standards of COVID-19 related conditions and aimed to provide reference to improve and enrich the localized application of ICD coding standards for COVID-19 related conditions in China.Copyright © 2021, Peking Union Medical College Hospital. 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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Objective Many mutant strains of SARS-CoV-2 have stronger infectivity and immune escape ability. The situation of epidemic evaluation, prevention and control is serious. The aim of the present paper is to track and predict the infectious transmission of COVID-19 through a theoretical model. Methods Based on the grid epidemic model, this paper discussed the relationship between the duration of infection and the effect of group immunity, and on this basis, established the theoretical model of infection transmission of COVID-19. The infectivity parameter A and the immune effect parameter B are introduced to predict the daily variation curve of infection. The parameter (Equation presented)can be used to quantitatively compare the comprehensive infectivity of each mutant, and we also test the conjecture that the infection parameters A and B are not related to regional factors. Results Through the theoretical model of infection transmission of COVID-19, the infectious time was accurately predicted. By analyzing the infectivity and electrical changes of mutant strains, the internal relationship between the infectivity of mutant strains and the electrical changes of mutant residues was pointed out. The parameter changes of mutants were analyzed, and the comprehensive infectivity of each mutant was quantitatively compared. We also verified the conjecture that parameters A and B are only related to the nature of the virus itself and the coexistence of the virus and the human body, but not related to the region where the disease occurs and evaluated and compared the epidemic prevention level of each outbreak region. Conclusion This paper established a theoretical model of infection transmission of COVID-19, which can predict the duration of the epidemic, the number of new infections per day, and evaluate the infectivity of the virus, immune escape ability, comprehensive infectivity, and regional epidemic prevention level. It can also give some suggestions on epidemic prevention countermeasures according to the possible parameter changes caused by virus variation. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.
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Visual detection of nucleic acids is important to diagnose the serious acute infectious diseases such as coronavirus disease 2019 (COVID-19). During this pandemic, reliable visual detection kits have been in high demand for screening and prevention of the virus. While developing these visual detection kits, a real-time monitoring platform is usually applied to study the amplification and detection processes of nucleic acids and optimize the detecting conditions. Herein, we developed a real-time monitoring platform of colorimetric loop-mediated isothermal amplification (LAMP) to investigate the amplification and detection processes of nucleic acids. Using this platform, we could obtain the real-time amplification curves, and optimize the reaction temperature, color change, and detection time. Based on the optimized conditions, a visual detection kit for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was successfully developed with a sensitivity of 102 copies µL−1 in 12 min. This real-time monitoring platform has advantages of simple construction, steady performance, high sensitivity, and outstanding anti-pollution capability, and could replace the traditional colorimetric methods by photographing and reading values. This platform would accelerate the development of visual detection kits for colorimetric LAMP, help to explore the amplification and transcription of nucleic acids, and provide support for the prevention of emerging biological threats. © 2023
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Elevated medical waste has urged the improvement of sustainable medical waste treatments. A bibliometric analysis is initially conducted to investigate scientific development of medical waste management to pinpoint the publication trends, influential articles, journals and countries and study hotspots. Publications on medical waste and its management sharply increased since 2020. The most influential article was written by Klemeš et al., and "Waste Management and Research” is the most productive journal. India, China, the United Kingdom, Iran and Italy have published the most works. The research spotlights have switched from "human” and "sustainable development” in 2019 to "COVID-19” and "circular economy” in 2021. Since government acts essentially in handling medical waste and controlling disease transmission, rule implementations among the abovementioned countries are summarized to seek gaps between scientific advancement and regulatory frameworks. For accomplishing a circular economy, waste-to-energy technologies (incineration, gasification, pyrolysis, plasma-based treatments, carbonization, hydrogenation, liquefaction, biomethanation, fermentation and esterification) are comprehensively reviewed. Incineration, gasification, pyrolysis and carbonization are relatively feasible methods, their characteristics and limitations are further compared. By holistically reviewing current status of medical waste research, the focal points involved in management at the policy and technical level have been highlighted to find proper routes for medical waste valorization. © 2023 by the authors.