• International journal of advanced smart convergence
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• v.8 no.1
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• pp.1-8
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• 2019

Coronaviruses are known respiratory pathogens. In the past, most human coronaviruses were thought to cause mild symptoms such as cold. However recently, as seen in the Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS), infectious diseases with severe pulmonary disease and respiratory symptoms are caused by coronaviruses, making research on coronaviruses become important. Considering previous studies, we constructed 'HCoV-IMDB (Human Corona Virus Immune Database)' to systematically provide genetic information on human coronavirus and host immune information, which can be used to analyze the interaction between human coronavirus and host immune proteins. The 'HCoV-IMDB' constructed in the study can be used to search for genetic information on human coronavirus and host immune protein and to download data. A BLAST search specific to the human coronavirus, one of the database functions, can be used to infer genetic information and evolutionary relationship about the query sequence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.204-210
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• 2022

From the 1970s to the present, the number of new infectious diseases such as SARS, Ebola virus, and MERS has steadily increased. The new infectious disease, COVID-19, which began in Wuhan, Hubei Province, China, has pushed the world into a pandemic era. As a result, Countries imposed restrictions on entry to foreign countries due to concerns over the spread of COVID-19, which led to a decrease in the movement of tourists. Due to the restriction of travel, keywords such as "Corona blue" have soared and depression has increased. Therefore, this study aims to analyze the stress meaning network of the COVID-19 era to derive keywords and come up with a plan for a travel-related platform of the Post-COVID 19 era. This study conducted analysis of travel and stress caused by COVID-19 using TEXTOM, a big data analysis tool, and conducted semantic network analysis using UCINET6. We also conducted a CONCOR analysis to classify keywords for clustering of words with similarities. However, since we have collected travel and stress-oriented data from the start to the present, we need to increase the number of analysis data and analyze more data in the future.

• The Journal of Industrial Distribution & Business
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• v.14 no.5
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• pp.31-39
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• 2023

Purpose: As SARS-CoV-2, which was the main cause of the global pandemic, has repeatedly mutated in various forms, the threat of the virus has decreased considerably, and the spread has also subsided. Therefore, the purpose of this study was to explore the change in the role of masks and sustainable mask consumption according to the change in perception of wearing masks during the pandemic. Research design, data and methodology: This study used a descriptive review method as a literature review, and utilized the literature search method in PubMed, Riss, Scopus, and Google Scholar databases. Among them, a total of 46 papers were selected in the final stage. Results: As a result, it can be seen that during the pandemic, masks changed their roles according to social trends as their perceptions changed from general perceptions of protecting from external environments or diseases to fashion items with quarantine functions. Conclusions: Masks will be continuously consumed as one of the fashion items with the function of quarantine that protects the respiratory tract from the external environment that is indispensable in our daily lives. Therefore, measures should be taken on sustainable consumption measures according to consumer demand for disposable masks.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.50-58
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• 2024

Body temperature plays an important role in medicine, some diseases are characterized by changes in human body temperature. Monitoring body temperature also allows doctors to monitor the effectiveness of medical treatments. Accurate body temperature measurement is key to detecting fevers, especially fevers related to infection with the SARS-CoV-2 virus that caused the recent Covid-19 pandemic in the world. The solution of measuring body temperature using a thermal camera is fast but has a high cost and is not suitable for some organizations with difficult economic conditions today. Use a medical thermometer to measure body temperature directly for a slow rate, making it easier to spread disease from person to person. In this paper, we propose a completely automatic body temperature measurement system that can adjust the height according to the person taking the measurement, has a measurement logging system and is monitored via the internet. Experimental results show that the proposed method has successfully created a fully automatic human body measurement system. Furthermore, this research also helps the school's scientists and students gain more knowledge and experience to apply Internet of Things technology in real life.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.23.1-23.12
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• 2022

Natural infection with severe acute respiratory syndrome-coronavirus-2 or vaccination induces virus-specific immunity protecting hosts from infection and severe disease. While the infection-preventing immunity gradually declines, the severity-reducing immunity is relatively well preserved. Here, based on the different longevity of these distinct immunities, we develop a mathematical model to estimate courses of endemic transition of coronavirus disease 2019 (COVID-19). Our analysis demonstrates that high viral transmission unexpectedly reduces the rates of progression to severe COVID-19 during the course of endemic transition despite increased numbers of infection cases. Our study also shows that high viral transmission amongst populations with high vaccination coverages paradoxically accelerates the endemic transition of COVID-19 with reduced numbers of severe cases. These results provide critical insights for driving public health policies in the era of 'living with COVID-19.'

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.1-9
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• 2024

Predicting the spread of COVID-19 remains a challenge due to the complexity of the disease and its evolving nature. This study presents an integrated approach using the classic SIR model for infectious diseases, enhanced by the chemical master equation (CME). We employ a Monte Carlo method (SSA) to solve the model, revealing unique aspects of the SARS-CoV-2 virus transmission. The study, a first of its kind in Korea, adopts a step-by-step and complementary approach to model prediction. It starts by analyzing the epidemic's trajectory at local government levels using both basic and stochastic SIR models. These models capture the impact of public health policies on the epidemic's dynamics. Further, the study extends its scope from a single-infected individual model to a more comprehensive model that accounts for multiple infections using the jump SIR prediction model. The practical application of this approach involves applying these layered and complementary SIR models to forecast the course of the COVID-19 epidemic in small to medium-sized local governments, particularly in Gangnam-gu, Seoul. The results from these models are then compared and analyzed.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.661-672
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• 2022

After the outbreak of the SARS-CoV2 virus that causes COVID-19, it spreads around the world with the number of infections and deaths rising rapidly caused a shortage of medical resources. As a way to solve this problem, chest X-ray diagnosis using Artificial Intelligence(AI) received attention as a primary diagnostic method. The purpose of this study is to comprehensively analyze the detection of COVID-19 via AI. To achieve this purpose, 292 studies were collected through a series of Classification methods. Based on these data, performance measurement information including Accuracy, Precision, Area Under Cover(AUC), Sensitivity, Specificity, F1-score, Recall, K-fold, Architecture and Class were analyzed. As a result, the average Accuracy, Precision, AUC, Sensitivity and Specificity were achieved as 95.2%, 94.81%, 94.01%, 93.5%, and 93.92%, respectively. Although the performance measurement information on a year-on-year basis gradually increased, furthermore, we conducted a study on the rate of change according to the number of Class and image data, the ratio of use of Architecture and about the K-fold. Currently, diagnosis of COVID-19 using AI has several problems to be used independently, however, it is expected that it will be sufficient to be used as a doctor's assistant.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.2
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• pp.143-150
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• 2021

The recent spread of novel and highly variant pathogenic viruses, including the coronavirus (SARS-CoV-2), has increased the demand for diagnostic testing for rapid confirmation. This has resulted in investigating the functional capability of each space, and preparing facility guidelines to secure the safety of medical technologists. During viral evaluations, there is a requirement of negative pressure facilities along with thread separation, during pre-treatment of samples and before nucleic acid amplification. Space composition therefore needs to be planned by considering unidirectional air flow. This classification of safety management facilities is designated as biosafety level 2, and personal protective equipment is placed accordingly. In case of handling dangerous materials, they need to be carried out of the biosafety cabinet, and sterilizers are required for suitable disposal of infectious agents. A common feature of domestic laboratories is maintenance of the sample pre-treatment space at a negative pressure of -2.5 Pa or less, and arranging separate pre-treatment and reagent preparation spaces during the test process. We believe that the data generated in this study is meaningful, and offers an efficient direction and detailed flow for separation of the inspection process and space functions. Moreover, this study introduces construction of the laboratory by applying the safety management standards.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.741-750
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• 2022

Objective : In March 2020, World Health Organization declared a global pandemic caused by a novel coronavirus (SARS-CoV-2). The disease caused by this virus is called COVID-19. Due to its high contagiousness, many changes have occurred in overall areas of our daily life including hospital use by patients. The aim of this study was to investigate the impact of COVID-19 on volume of spine surgery in South Korea using the National Health Insurance database and compare it with the volume of a homologous period before the pandemic. Methods : Data of related to spine surgery from January 2019 to April 2021 were obtained from the National Health Insurance and Health Insurance Review and Assessment Service database. Primary outcomes were total number of patients, rate of patients per 100000 population, and total number of procedures. The number of patients by hospital size was also analyzed. Results : COVID-19 outbreaks occurred in South Korea in March, August, and December of 2020. Compared to the previous year, the total number of patients who underwent spinal surgery showed a decrease for 2-3 months after the first and second outbreaks. However, it showed an increasing trend after the third outbreak. The same pattern was observed in terms of the ratio of the number of patients per 100000 population. Between 2019 and 2021, the mean number of spine surgeries per month tended to increase. Mean annual medical expenses increased over the years (p=0.001). When the number of spine surgeries was analyzed by hospital size, proportion of tertiary general hospital in 2021 increased compared to those in 2019 and 2020 (vs. 2019, p=0.012; vs. 2020, p=0.016). The proportion of general hospital was significantly decreased in 2020 compared to that in 2019 (p=0.037). Conclusion : After the COVID-19 outbreak, patients tended to postpone spinal surgery temporarily. The number of spinal surgeries decreased for 2-3 months after the first and second outbreaks. However, as the ability to respond to the COVID-19 pandemic at the hospital and society-wide level gradually increased, the number of spine surgeries did not decrease after the third outbreak in December 2020. In addition, the annual number of spine surgeries continued to increase. However, it should be noted that patients tend to be increasingly concentrated in tertiary hospitals for spinal surgery.