• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.135-161
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• 2023

This study analyzed the research trends of COVID-19 research papers published in medical journals of Korea. Data were collected from 25 MEDLINE journals in 'Medicine and Pharmacy' studies and a total of 800 were selected. As a result of the study, authors from domestic affiliations made up 76.96% of the total, and the proportion of authors from foreign institutions decreased without significant change. The authors' majors were 'Internal Medicine' (32.85%), 'Preventive Medicine/Occupational and Environmental Medicine' (16.23%), 'Radiology' (5.74%), and 'Pediatrics' (5.50%), and 435 (54.38%) papers were collaborative research. As for author keywords, 'COVID19' (674), 'SARSCoV2' (245), 'Coronavirus' (81), and 'Vaccine' (80) were derived as top keywords. There were six words that appeared throughout the entire period: 'COVID19,' 'SARSCoV2,' 'Coronavirus,' 'Korea,' 'Pandemic,' and 'Mortality.' Co-occurrence network analysis was conducted on MeSH terms and author keywords, and common keywords such as 'covid-19,' 'sars-cov-2,' and 'public health' were derived. In topic modeling, five topics were identified, including 'Vaccination,' 'COVID-19 outbreak status,' 'Omicron variant,' 'Mental health, control measures,' and 'Transmission and control in Korea.' Through this study, it was possible to identify the research areas and major keywords by year of COVID-19 research papers published during the 'Public Health Emergency of International Concern (PHEIC).'

• Journal of Industrial Convergence
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• v.22 no.6
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• pp.51-59
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• 2024

The purpose of the paper is to suggest government policies that are practically helpful to small business owners in pandemic situations such as COVID-19. To this end, keyword frequency analysis and word cloud analysis of text mining analysis were performed by crawling news articles centered on the keywords "COVID-19 Support for Small Businesses", "The Impact of Small Businesses by Response System to COVID-19 Infectious Diseases", and "COVID-19 Small Business Economic Policy", and major issues were identified through LDA topic modeling analysis. As a result of conducting LDA topic modeling, the support policy for small business owners formed a topic label with government cash and financial support, and the impact of small business owners according to the COVID-19 infectious disease response system formed a topic label with a government-led quarantine system and an individual-led quarantine system, and the COVID-19 economic policy formed a topic label with a policy for small business owners to acquire economic crisis and self-sustainability. Focusing on the organized topic label, it was intended to provide basic data for small business owners to understand the damage reduction policy for small business owners and the policy for enhancing market competitiveness in the future pandemic situation.

• Journal of Korean Academy of Nursing
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• v.50 no.4
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• pp.533-540
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• 2020

Purpose: This study aimed to evaluate nursing students' understanding of the prevention of COVID-19, as well as their anxiety towards the disease and their perception of their professional identity in the wake of the pandemic, in Zhengzhou, China. Methods: A cross-sectional study was designed to investigate 474 nursing students by cluster sampling using a stratified questionnaire from February 15 to March 31, 2020. Multiple linear regression was used to identify the factors affecting professional identity. Binary and multiple logistic regression were used to identify the factors affecting anxiety. Results: Responders with a high level of understanding of COVID-19 and frequent use of behavioral strategies for its prevention comprised 93.2% and 30.0% of the cohort, respectively. Professional identity was significantly associated with gender and anxiety (p < .050). The prevalence of anxiety among nursing students was 12.4%. Male (odds ratio [OR] = 2.39; 95% confidence interval [CI] = 1.26~4.52), sophomores (OR = 5.30; 95% CI = 1.61~7.45), and infrequent use of prevention measures (OR = 3.49; 95% CI = 1.16~5.19) had a significant effect on anxiety. Conclusion: Anxiety during the COVID-19 epidemic gives an adverse effect on the professional identity of nursing in students. Nursing education institutions need to provide psychological counseling services for nursing students, in addition to improving their teaching of COVID-19 prevention strategies.

• Journal of Preventive Medicine and Public Health
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• v.54 no.4
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• pp.221-229
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• 2021

Objectives: The Indonesian government issued large-scale social restrictions (called Pembatasan Sosial Berskala Besar, or PSBB) at the beginning of the coronavirus disease 2019 (COVID-19) pandemic to control the spread of COVID-19 in Jakarta, Bogor, Depok, Tangerang, and Bekasi (Greater Jakarta). Public compliance poses a challenge when implementing large-scale social restrictions, and various factors have contributed to public non-compliance with the regulation. This study aimed to determine the degree of non-compliance and identify the factors that contributed to public non-compliance with the PSBB in Greater Jakarta, Indonesia. Methods: This was a quantitative study with a cross-sectional design. A total of 839 residents of Greater Jakarta participated in this study. Data were collected online using a Google Form, and convenience sampling was undertaken. Univariate and multivariate analyses were performed to explore the relationships between public non-compliance with the PSBB regulation and socio-demographic variables, respondents' opinion of the PSBB, and social capital. Results: A total of 22.6% of subjects reported participating in activities that did not comply with the PSBB. The variables that most affected non-compliance with the PSBB were age, gender, income, opinion of the PSBB, and social capital. Conclusions: Strengthening social capital and providing information about COVID-19 prevention measures, such as washing one's hands with soap, wearing masks properly, and maintaining social distancing, is essential. Robust public understanding will foster trust and cooperation with regard to COVID-19 prevention efforts and provide a basis for mutual agreement regarding rules/penalties.

• Journal of the Korean Society of School Health
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• v.34 no.1
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• pp.76-86
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• 2021

Purpose: This study was conducted to describe the experience of school health teachers in regard to the COVID-19 pandemic in South Korea. Methods: We conducted a qualitative study using content analysis. Ten school health teachers were recruited from 6 elementary schools, 2 middle schools and 2 high schools in Seoul, using purposive sampling. They participated in semi-structured in-depth interviews in person or using an online communication system from January to February, 2021. Interviews were transcribed verbatim and analysed using qualitative content analysis. Results: Three main categories and nine generic categories emerged from the analysis. Firstly, it was discovered that school health teachers had psychological and physical stress to cope with COVID-19 due to the 1) fear of the unexpected infectious disease, 2) burden of having to deal with it alone, 3) breakdown of personal life and physical exhaustion and 4) heavy duty as a health teacher caused by the lack of an organic cooperation system with institutions related to school infectious diseases. Secondly, school health teachers had an increased sense of empowerment in regard to infectious disease management as a result of 1) feeling rewarded and appreciated and 2) gaining confidence and trust in infectious disease management. Finally, school health teachers experienced the urgent need for an effective response strategy for infection control because of the 1) confusion over infectious disease response due to lack of practical manuals applicable to the field as well as training, 2) disappointing response system without an expert response team dedicated to managing infectious diseases in schools, and 3) growing awareness of the need for change. Conclusion: It is expected that school health teachers' experience of COVID-19 will be used as important data for building effective and advanced school infectious disease response systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.306-312
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• 2022

During the unprecedented COVID-19 outbreak, the public's information needs created an environment where they overwhelmingly consume information on the chronic disease. Given that news media affect the public's emotional well-being, the pandemic situation highlights the importance of paying particular attention to how news stories frame their coverage. In this study, COVID-19 news speech emotion from mainstream broadcasters in South Korea and the United States (US) were analyzed using convolutional neural networks. Results showed that neutrality was detected across broadcasters. However, emotions such as sadness and anger were also detected. This was evident in Korean broadcasters, whereas those emotions were not detected in the US broadcasters. This is the first quantitative vocal emotion analysis of COVID-19 news speech. Overall, our findings provide new insight into news emotion analysis and have broad implications for better understanding of the COVID-19 pandemic.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.563-572
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• 2021

Background and objective: The ongoing COVID-19 pandemic restricted daily life, forcing people to spend time indoors. With the growing interest in mental health issues and residential environments, 'pet plants' have been receiving attention during the unprecedented social distancing measures. This study aims to analyze the change in trends of pet plants before and during the COVID-19 pandemic and provide basic data for studies related to pet plants and directions of future development. Methods: A total of 2,016 news articles using the keyword 'pet plants' were collected on Naver News from January 1, 2018 to August 15, 2019 (609 articles) and January 1, 2020 to August 15, 2021 (1,407 articles). The texts were tokenized into words using KoNLPy package, ultimately coming up with 63,597 words. The analyses included frequency of keywords and topic modeling based on Latent Dirichlet Allocation (LDA) to identify the inherent meanings of related words and each topic. Results: Topic modeling generated three topics in each period (before and during the COVID-19), and the results showed that pet plants in daily life have become the object of 'emotional support' and 'healing' during social distancing. In particular, pet plants, which had been distributed as a solution to prevent solitary deaths and depression among seniors living alone, are now expanded to help resolve the social isolation of the general public suffering from COVID-19. The new term 'plant butler' became a new trend, and there was a change in the trend in which people shared their hobbies and information about pet plants and communicated with others in online. Conclusion: Based on these findings, the trend data of pet plants before and after the outbreak of COVID-19 can provide the basis for activating research on pet plants and setting the direction for development of related industries considering the continuous popularity and trend of indoor gardening and green hobby.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.39-44
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• 2022

The advent, course, and possible consequences of the COVID-19 pandemic are now the focus of global attention. From whichever side the geopolitical centers of influence might view it, the problem of the coronavirus concerns all world leaders and the representatives of all branches of science, especially physicians, economists, and politicians - virtually the entire population of the planet. The uniqueness of the COVID-19 phenomenon lies in the uncertainty of the problem itself, the peculiarities and specifics of the course of the biological processes in modern conditions, as well as the sharp confrontation of the main political players on the world stage. Based on an analysis of scientific research, the article describes the profile of the emotional concept of "anxiety" in Russian linguoculture. Through monitoring the headlines of Russian media reports in the "COVID-19" section of Google News and Mail News news aggregators dated August 4-6, 2021, the study establishes the quantitative and qualitative characteristics of the alarm-generating news products on coronavirus in the Russian segment of the Internet and interprets the specifics of media information about COVID-19. The level of mass media criticism in Russia is determined through a phone survey. It is concluded that coronavirus reports in online media conceptualize anxiety about the SARS virus and the COVID-19 disease as a complex cognitive structure. The media abuse the trick of "magic numbers" and emotionally expressive words in news headlines, which are perceived by mass information consumers first and typically uncritically.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Journal of Microbiology and Biotechnology
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• v.32 no.1
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• pp.1-5
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• 2022

Diagnostics, vaccines, and drugs are indispensable tools and control measures employed to overcome infectious diseases such as coronavirus disease 2019 (COVID-19). Diagnostic tools based on RT-PCR were developed early in the COVID-19 pandemic and were urgently required for quarantine (testing, tracing and isolation). Vaccines such as mRNA vaccines and virus-vectored vaccines were also successfully developed using new platform technologies within one year after identifying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19. Drug development has been conducted in various ways including drug repurposing, convalescent plasma therapy, and monoclonal antibody development. Among the above efforts, this review examines COVID-19 drug development along with the related and upcoming challenges.