• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.59-70
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• 2024

Background: The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020 [1] . It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. Method: This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers' focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. Result: This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic is sure to cause

• 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.

• Pediatric Infection and Vaccine
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• v.29 no.3
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• pp.125-130
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• 2022

For the extended duration of the coronavirus disease 2019 (COVID-19) pandemic, reports emerged that mother-to-child transmission rates were low. However, the pandemic protocols including strict isolation, testing for severe acute respiratory syndrome coronavirus 2, and negative pressure isolation remained in Korea. Recently, the guideline for the management of neonates born to mothers with COVID-19 have been revised based on guidelines in other countries. Here, we introduce this newly developed guideline and review the foreign guidelines that were used for reference.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.360-370
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• 2022

Objectives: This study evaluated the response in Daegu, Korea to the first wave of the coronavirus disease 2019 (COVID-19) pandemic according to a public health emergency response model. Methods: After an examination of the official data reported by the city of Daegu and the Korea Centers for Disease Control and Prevention, as well as a literature review and advisory meetings, we chose a response model. Daegu's responses were organized into 4 phases and evaluated by applying the response model. Results: In phase 1, efforts were made to block further transmission of the virus through preemptive testing of a religious group. In phase 2, efforts were concentrated on responding to mass infections in high-risk facilities. Phase 3 involved a transition from a high-intensity social distancing campaign to a citizen participation-based quarantine system. The evaluation using the response model revealed insufficient systematic preparation for a medical surge. In addition, an incorporated health-related management system and protection measures for responders were absent. Nevertheless, the city encouraged the participation of private hospitals and developed a severity classification system. Citizens also played active roles in the pandemic response by practicing social distancing. Conclusions: This study employed the response model to evaluate the early response in Daegu to the COVID-19 pandemic and revealed areas in need of improvement or maintenance. Based on the study results, creation of a systematic model is necessary to prepare for and respond to future public health emergencies like the COVID-19 pandemic.

• International Journal of Oral Biology
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• v.46 no.1
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• pp.1-6
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• 2021

Since the outbreak of coronavirus disease 2019 (COVID-2019), the infection has spread worldwide due to the highly contagious nature of severe acute syndrome coronavirus (SARS-CoV-2). To manage SARS-CoV-2, the development of diagnostic assays that can quickly and accurately identify the disease in patients is necessary. Currently, nucleic acid-based testing and serology-based testing are two widely used approaches. Of these, nucleic acid-based testing with quantitative reverse transcription-PCR (RT-qPCR) using nasopharyngeal (NP) and/or oropharyngeal (OP) swabs is considered to be the gold standard. Recently, the use of saliva samples has been considered as an alternative method of sample collection. Compared to the NP and OP swab methods, saliva specimens have several advantages. Saliva specimens are easier to collect. Self-collection of saliva specimens can reduce the risk of infection to healthcare providers and reduce sample collection time and cost. Until recently, the sensitivity and accuracy of the data obtained using saliva specimens for SARS-CoV-2 detection was controversial. However, recent clinical research has found that sensitive and reliable data can be obtained from saliva specimens using RT-qPCR, with approximately 81% to 95% correspondence with the data obtained from NP and OP swabs. These data suggest that self-collected saliva is an alternative option for the diagnosis of COVID-19.

• Journal of Preventive Medicine and Public Health
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• v.54 no.1
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• pp.31-36
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• 2021

Objectives: Non-traditional materials are used for mask construction to address personal protective equipment shortages during the coronavirus disease 2019 (COVID-19) pandemic. Reusable masks made from surgical sterilization wrap represent such an innovative approach with social media frequently referring to them as "N95 alternatives." This material was tested for particle filtration efficiency and breathability to clarify what role they might have in infection prevention and control. Methods: A heavyweight, double layer sterilization wrap was tested when new and after 2, 4, 6, and 10 autoclave sterilizing cycles and compared with an approved N95 respirator and a surgical mask via testing procedures using a sodium chloride aerosol for N95 efficiency testing similar to 42 CFR 84.181. Pressure testing to indicate breathability was also conducted. Results: The particle filtration efficiency for the sterilization wrap ranged between 58% to 66%, with similar performance when new and after sterilizing cycles. The N95 respirator and surgical mask performed at 95% and 68% respectively. Pressure drops for the sterilization wrap, N95 and surgical mask were 10.4 mmH2O, 5.9 mmH2O, and 5.1 mmH2O, respectively, well below the National Institute for Occupational Safety and Health limits of 35 mmH2O during initial inhalation and 25 mmH2O during initial exhalation. Conclusions: The sterilization wrap's particle filtration efficiency is much lower than a N95 respirator, but falls within the range of a surgical mask, with acceptable breathability. Performance testing of non-traditional mask materials is crucial to determine potential protection efficacy and for correcting misinterpretation propagated through popular media.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.32-42
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• 2023

There are serious problems worldwide, such as a pandemic due to an unprecedented infection caused by COVID-19. On previous approaches, they invented medical vaccines and preemptive testing tools for medical engineering. However, it is difficult to access poor medical systems and medical institutions due to disparities between countries and regions. In advanced nations, the damage was even greater due to high medical and examination costs because they did not go to the hospital. Therefore, from a software engineering-based perspective, we propose a learning model for determining coronavirus infection through symptom data-based software prediction models and tools. After a comparative analysis of various models (decision tree, Naive Bayes, KNN, multi-perceptron neural network), we decide to choose an appropriate decision tree model. Due to a lack of data, additional survey data and overseas symptom data are applied and built into the judgment model. To protect from thiswe also adapt human normalization approach with traditional Korean medicin approach. We expect to be possible to determine coronavirus, flu, allergy, and cold without medical examination and diagnosis tools through data collection and analysis by applying decision trees.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.205-213
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• 2024

This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers' preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

• The Journal of Internal Korean Medicine
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• v.42 no.4
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• pp.590-604
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• 2021

Objectives: The purpose of this study is to examine the safety of Korean medicine treatment in patients vaccinated with the AstraZeneca COVID-19 vaccine (ChAdOx1 nCoV-19/AZD1222). Methods: We investigated patients at Kyung Hee University Korean Medicine Hospital who were vaccinated with the AstraZeneca COVID-19 vaccine between June 1, 2021 and June 30, 2021. The safety of Korean medicine treatment was evaluated by examining adverse events that occurred within seven days of vaccination, including liver function and kidney function testing, assessment of the severity of adverse events, and examination of causality to vaccines and Korean medicine treatment. Results: Eleven patients vaccinated with the first dose of the AstraZeneca COVID-19 vaccine were included. A total of 19 adverse events were reported: 15 systemic adverse events, three local adverse events, and one alanine aminotransferase increase. The most commonly reported systemic adverse events were fatigue (4 cases, 36.4%), headache (4 cases, 36.4%), and myalgia (4 cases, 36.4%). All adverse events were rated below moderate (grade 2) in severity. Systemic and local adverse events were evaluated as definitely related to vaccination and unlikely to be related to Korean medicine treatment, while alanine aminotransferase increase was evaluated as unlikely to be related to either the vaccine or Korean medicine treatment. Aspartate transaminase, Blood urea nitrogen, and creatinine were measured within the reference range after vaccination. Conclusion: Our results suggest that the severity and frequency of adverse events in patients vaccinated with the AstraZeneca COVID-19 vaccine did not increase after Korean medicine treatment.

• The Journal of Korean Medicine
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• v.43 no.2
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• pp.124-139
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• 2022

Objectives: This study examined the effectiveness of Korean medical treatment for two patients complaining of discomfort after receiving Pfizer COVID-19 vaccine. Methods: The patients were hospitalized for 50 days and 12 days, respectively. They were treated with herbal medicine, acupuncture, electroacupuncture, and moxibustion. We used the Numerical Rating Scale (NRS) on numbness in extremities and headache, Manual Muscle Testing Grading System (MMT), Criteria for Sweating Categorization, and 36-Item Short Form Health Survey (SF-36) to evaluate the clinical effects of the treatment. Results: In Case 1, headache improved from peak NRS 9 and average NRS 7 on admission day to both NRS 3 on discharge. The SF-36 score was also increased, suggesting that the quality of life was improved. In Case 2, numbness in the extremities improved from NRS 8 on the day before admission to NRS 2 on discharge, and general condition also improved. Conclusions: This study suggests that Korean medicine can be an effective treatment for patients who experience long-lasting discomfort after being vaccinated with COVID-19, but with no abnormal findings in the examination.