• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.17-21
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• 2021

In 2020, the coronavirus disease 2019 (COVID-19) pandemic has caused unprecedented disruptions to global health systems. The Korea has taken full-fledged actions against this novel infectious disease, swiftly implementing a testing-tracing-treatment strategy. New obligations have therefore been given to the Health Insurance Review and Assessment Service (HIRA) to devote the utmost effort towards tackling this global health crisis. Thanks to the universal national health insurance and state-of-the-art information communications technology (ICT) of the Korea, HIRA has conducted far-reaching countermeasures to detect and treat cases early, prevent the spread of COVID-19, respond quickly to surging demand for the healthcare services, and translate evidence into policy. Three main factors have enabled HIRA to undertake pandemic control preemptively and systematically: nationwide data aggregated from all healthcare providers and patients, pre-existing ICT network systems, and real-time data exchanges. HIRA has maximized the use of data and pre-existing network systems to conduct rapid and responsive measures in a centralized way, both of which have been the most critical tactics and strategies used by the Korean healthcare system. In the face of new obligations, our promise is to strive for a more responsive and resilient health system during this prolonged crisis.

• Archives of Plastic Surgery
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• v.49 no.3
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• pp.397-404
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• 2022

Background Excessive use of corticosteroids therapy along with gross immunocompromised conditions in the novel coronavirus disease 2019 (COVID-19) pandemic has raised the risks of contracting opportunistic fungal infections. Here, we describe our experience with the implementation of a surgical protocol to treat and reconstruct rhino-orbital-cerebral mucormycosis. Methods A retrospective review of our prospectively maintained database was conducted on consecutive patients diagnosed with mucormycosis undergoing immediate reconstruction utilizing our "Mucormycosis Management Protocol." All patients included in this study underwent reconstruction after recovering from COVID-19. Wide local excision was performed in all cases removing all suspected and edematous tissue. Reconstruction was done primarily after clear margins were achieved on clinical assessment under a cover of injectable liposomal amphotericin B. Results Fourteen patients were included. The average age was 43.6 years and follow-up was 24.3 days. Thirteen patients had been admitted for inpatient care of COVID-19. Steroid therapy was implemented for 2 weeks in 11 patients and for 3 weeks in 3 patients. Eight patients (57.1%) had a maxillectomy and mucosal lining resection with/without skin excision, and six patients (42.8%) underwent maxillectomy and wide tissue excision (maxillectomy and partial zygomatic resection, orbital exenteration, orbital floor resection, nose debridement, or skull base debridement). Anterolateral thigh (ALT) flaps were used to cover defects in all patients. All flaps survived. No major or minor complications occurred. No recurrence of mucormycosis was noted. Conclusion The approach presented in this study indicates that immediate reconstruction is safe and reliable in cases when appropriate tissue resection is accomplished. Further studies are required to verify the external validity of these findings.

• Journal of Fashion Business
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• v.25 no.3
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• pp.1-16
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• 2021

In December 2019, when the novel coronavirus (nCoV) was identified in Wuhan, Hubei, China, the number of people belonging to post-90s generation among about 42,000 medical staffs personnel supporting Hubei was 12,000 or more, accounting for about 33.3% of the total number of personnel. The term "post-90s generation" generally indicates young people born from 1990 to 1999. The study scope is the 1990-2020 period between the birth of post-90s generation and present. Literature and empirical studies are performed. Generational characteristics and fashion trends shown only by post-90s generation through precedent studies and reports are as follows: First, generational characteristics of post-90s generation can be categorized into the following three characteristics: "sang wenhua", "collective loneliness", and "diversified identity". Second, fashion trends of the post-90s generation can be categorized into the following three characteristics: "new Chinese style fashion", "masstige fashion", and "de-labeling fashion". The above results show that the post-90s generation uses "culture" and "me" as keywords. Further, the above trend is consequently divided into the following two characteristics: "diversification" and "individualization". This is because the post-90s generation is directly affected by the reform and opening and the 9-year compulsory education policy of China compared to the previous generations; hence, these people are greatly influenced by Western culture and fashion as well as their own culture and fashion. It refers having a tendency to express one's individuality with a variety of tastes and styles.

• Journal of Korean Academy of Nursing
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• v.52 no.3
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• pp.291-307
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• 2022

Purpose: The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports. Methods: Data were media articles related to the topic "nurse" reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. Results: The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are "a nurse who committed suicide because she could not withstand the Taewoom at work" and "a nurse as a perpetrator of a newborn abuse case," while post-COVID-19 examples are "a nurse as a victim of COVID-19," "a nurse working with the support of the people," and "a nurse as a top contributor and a warrior to protect from COVID-19." Conclusion: Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses' image.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.230-238
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• 2022

Myocarditis was previously attributed to an epidemic viral infection. Additional harmful reagents, in addition to viruses, play a role in its etiology. Coronavirus disease 2019 (COVID-19) vaccine-induced myocarditis has recently been described, drawing attention to vaccine-induced myocarditis in children and adolescents. Its pathology is based on a series of complex immune responses, including initial innate immune responses in response to viral entry, adaptive immune responses leading to the development of antigen-specific antibodies, and autoimmune responses to cellular injury caused by cardiomyocyte rupture that releases antigens. Chronic inflammation and fibrosis in the myocardium eventually result in cardiac failure. Recent advancements in molecular biology have remarkably increased our understanding of myocarditis. In particular, microRNAs (miRNAs) are a hot topic in terms of the role of new biomarkers and the pathophysiology of myocarditis. Myocarditis has been linked with microRNA-221/222 (miR-221/222), miR-155, miR-10a^*, and miR-590. Despite the lack of clinical trials of miRNA intervention in myocarditis yet, multiple clinical trials of miRNAs in other cardiac diseases have been aggressively conducted to help pave the way for future research, which is bolstered by the success of recently U.S. Food and Drug Administration-approved small-RNA medications. This review presents basic information and recent research that focuses on myocarditis and related miRNAs as a potential novel biomarker and the therapeutics.

• Asian Journal for Public Opinion Research
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• v.12 no.1
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• pp.53-74
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• 2024

The proliferation of health misinformation gained momentum amidst the outbreak of the novel coronavirus disease 2019 (COVID-19). People stuck in their homes, without work pressure, regardless of health concerns towards personal, family, or peer groups, consistently demanded information. People became engaged with misinformation while attempting to find health information content. This study used the content analysis method and analyzed 1,154 misinformation stories from four prominent signatories of the International Fact-Checking Network during the pandemic. The study finds the five main categories of misinformation related to the COVID-19 pandemic. These are 1) the severity of the virus, 2) cure, prevention, and treatment, 3) myths and rumors about vaccines, 4) health authorities' guidelines, and 5) personal and social impacts. Various sub-categories supported the content characteristics of these categories. The study also analyzed the emotional valence of health misinformation. It was found that misinformation containing negative sentiments got higher engagement during the pandemic. Positive and neutral sentiment misinformation has less reach. Surprise, fear, and anger/aggressive emotions highly affected people during the pandemic; in general, people and social media users warning people to safeguard themselves from COVID-19 and creating a confusing state were found as the primary motivation behind the propagation of misinformation. The present study offers valuable perspectives on the mechanisms underlying the spread of health-related misinformation amidst the COVID-19 outbreak. It highlights the significance of discerning the accuracy of information and the feelings it conveys in minimizing the adverse effects on the well-being of public health.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.