• Korean Journal of Clinical Laboratory Science
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• v.52 no.4
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• pp.297-309
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• 2020

Coronaviruses were originally discovered as enzootic infections that limited to their natural animal hosts, but some strains have since crossed the animal-human species barrier and progressed to establish zoonotic diseases. Accordingly, cross-species barrier jumps resulted in the appearance of SARS-CoV, MERS-CoV, and SARS-CoV-2 that manifest as virulent human viruses. Coronaviruses contain four main structural proteins: spike, membrane, envelope, and nucleocapsid protein. The replication cycle is as follows: cell entry, genome translation, replication, assembly, and release. They were not considered highly pathogenic to humans until the outbreaks of SARS-CoV in 2002 in Guangdong province, China. The consequent outbreak of SARS in 2002 led to an epidemic with 8,422 cases, and a reported worldwide mortality rate of 11%. MERS-CoVs is highly related to camel CoVs. In 2019, a cluster of patients infected with 2019-nCoV was identified in an outbreak in Wuhan, China, and soon spread worldwide. 2019-nCoV is transmitted through the respiratory tract and then induced pneumonia. Molecular diagnosis based on upper respiratory region swabs is used for confirmation of this virus. This review examines the structure and genomic makeup of the viruses as well as the life cycle, diagnosis, and potential therapy.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.113-128
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• 2021

In this study, we aimed to understand the public opinion on COVID-19 vaccine. To achieve the goal, we analyzed COVID-19 vaccine-related Twitter posts. 45,413 tweets posted from March 16, 2020 to March 15, 2021 including COVID-19 vaccine names as keywords were collected. The 12 vaccine names used for data collection included 'Pfizer', 'AstraZeneca', 'Modena', 'Jansen', 'NovaVax', 'Sinopharm', 'SinoVac', 'Sputnik V', 'Bharat', 'KhanSino', 'Chumakov', and 'VECTOR' in the order of the number of collected posts. The collected posts were analyzed manually and automatedly through keyword analysis, sentiment analysis, and topic modeling to understand the opinions for the investigated vaccines. According to the results, there were generally more negative posts about vaccines than positive posts. Anxiety about the aftereffects of vaccination and distrust in the efficacy of vaccines were identified as major negative factors for vaccines. On the contrary, the anticipation for the suppression of the spread of coronavirus following vaccination was identified as a positive social factor for vaccines. Different from previous studies that investigated opinions about COVID-19 vaccines through mass media data such as news articles, this study explores opinions of social media users using keyword analysis, sentiment analysis, and topic modeling. In addition, the results of this study can be used by governmental institutions for making policies to promote vaccination reflecting the social atmosphere.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.142-152
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• 2020

This study explores experiences of college nursing students during the COVID-19 pandemic. The participants were 10 college nursing students (junior and senior years) enrolled in a university in K city. Data were collected through in-depth interviews from May 1 to June 30, 2020. The phenomenological methodology proposed by Colaizzi was applied for data analysis. We identified a total of 6 theme clusters: increased fear of an infection, feeling isolated due to the changed way of life, feeling perplexed about unexpected circumstances, inability to adapt to the sudden change in classroom instruction, feeling burdened about clinical practice, and confronting the reality as a preliminary nurse. The analyzed data revealed that subjects had numerous experiences about COVID-19. We believe there were necessity and significance to conduct this study during the ongoing COVID-19 pandemic. This analysis can serve as a useful resource for discussing issues related to nursing education in the post COVID-19 era.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.3
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• pp.208-216
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• 2021

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to investigate the status of renal function in patients with COVID-19. The study surveyed a total of 649 patients hospitalized with COVID-19 at a hospital located in southern Gyeonggi Province, South Korea over a one month period in January 2021. The parameters analyzed were blood urea nitrogen (BUN), creatinine, sodium, potassium, chloride, and estimated glomerular filtration rate (eGFR). The BUN and creatinine of the COVID-19 patients were found to be higher than the normal reference range, specially in males, and in the elderly (60s and 80s or older). The serum electrolyte levels of the patients were observed to be within the reference intervals. Of the subjects, males over 80 years of age had a Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) of 60 mL/min/1.73 m² or less. Recent research suggests that some severe cases of COVID-19 are showing signs of kidney damage, even in those with no prior underlying kidney disease. Thus, assessment of kidney function using multiple indicators could help diagnose abnormal renal function in patients with COVID-19.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.68-68
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• 2022

코로나바이러스는(COVID-19)는 2019년 12일 중국 후베이성 우한시에서 시작된 코로나바이러스감염증으로 2020년 1월부터 전 세계로 퍼져, 일부 국가 및 지역을 제외한 대부분의 나라와 모든 대륙으로 확산되었다. 이에 WHO는 범 유행전염병(Pandemic)을 선언하였다. 2022년 3월 18일 현재 국내 누적 확진환자 8,657,609명과 11,782명의 사망자를 일으켰고 전 세계적으로도 많은 사상자를 내고 있는 실정이고 사회 및 경제적인 피해로도 계속 확대되고 있다. 많은 감염자와 사망자의수에 대한 예측은 코로나바이러스의 전염병을 예방하고 즉각적 조치를 취할 수 있는데 도움이 될 수 있다. 본 연구에서는 문화적 인자를 제외한 국내에서 연구 사례가 많지 않은 기상 요인을 인자로 포함하여 머신러닝 모델을 통해 확진자를 예측하였다. 그리고 여러 가지 모델을 성능 평가 기법인 Root Mean Square Error(RMSE) 및 Mean Absolute Percentage Error(MAPE)를 통해 성능을 평가하고 비교하여 정확도 높은 모델을 제시하였다.

• Journal of Korean Society of Archives and Records Management
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• v.20 no.4
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• pp.229-236
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• 2020

In light of SARS-CoV-2's significant impact, human society has experienced rapid changes in lifestyle that it has not yet experienced before. One way this virus has influenced people's lives is the emergence of the zero-contact society, an initiative for preventing the spread of infectious diseases. As can be seen, the social impact of COVID-19 is widespread. Various issues, such as those about government policy, personal information protection, and health care, are affecting society as a whole. At the same time, factual information is difficult to track and record because of the rapid and transient nature of related events and issues. As such, a method of effectively describing COVID-19 and real-time information is necessary. The "COVID-19: Our Memory" project is an attempt to record the sociocultural impact of the coronavirus infection. This project collects major events and issues classified into several subjects, records those events from a neutral point of view, and develops a digital archive so that all records are accessible. All the data collected and built through the project, the application, including the source code and visualization, are all published to bring about new opportunities for collaboration.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.583-590
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• 2020

Coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. Several articles have so far reported on radiological findings of COVID-19 pneumonia. Herein, we present three cases of COVID-19 pneumonia in South Korea, and provide clinical information as well as chest radiograph and chest CT findings.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.143-144
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• 2022

본 연구에서는 엄격성 지수(Strigency Index)분석에 따른 OECD 가입국들의 코로나19 대응 정책 엄격성의 효과성을 분석하였다. 코로나바이러스감염증-19(이하, 코로나19)발생 이후 전 세계 대다수 국가들은 점진적 일상회복 단계로 접어들어 'with corona' 시대로 가고 있다. 코로나19의 완전 방역을 이루며 이전과 같은 일상으로 돌아가고 있지만, 또다시 대규모 감염병이 발생할 수 있다는 가능성을 가지고 있다. 그리하여 본 연구에서는 옥스퍼드 코로나바이러스 정부대응추적 프로젝트(OxCGRT)에서 제시된 21개 지표 중 OurWorldinData서 엄격성 지수 분석에 활용되는 9개 지표를 분석하여 정책의 엄격성을 분석하여 추후 발생하는 대규모 감염병에 효과적으로 대응하기 위한 기초 자료가 되고자 한다. 엄격성 지수 분석 결과, 9개 지표 중 6개 지표에서 정책의 도입한 시점부터 확진자가 감소하는 추세를 보이는 유사한 변화를 찾을 수 있었다. 하지만, 엄격성 지수 분석을 통해 국가 대응의 적절성·효과성을 입증하기에는 판단 기준이 0~4점 사이의 임의의 기준으로 분류되고 있었으며, 변수설정 또한 제시되어 있지 않아 대응 정책의 엄격성을 평가하는 기준으로 활용하기에는 한계점이 있어 보인다. 본 연구에서 엄격성 지수 분석을 통한 감염병 대응 정책의 효과성을 찾고자 하였다. 지수와 확진자 추세 간 유사한 변화는 찾았지만, 엄격성 지수의 한계점이 존재하는 연구이다. 그러나 본 연구의 결과를 통해 추후 확진자 증감 대비 엄격성 지수 분석을 통한 상관관계 분석, 지표별 평균치와 확진자 추세 분석을 통한 공통적인 효과성 분석 등 다른 연구의 기초 자료가 될 수 있을 것이라 판단된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.31-32
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• 2021

신종 코로나바이러스 감염증(Coronavirus disease 2019; COVID-19)이 빠르게 확산됨에 따라 세계적인 전염병 대유행인 팬데믹(Pandemic)으로 선언되었다. 감염자들은 꾸준히 증가하고 있고 최근에는, 무증상 감염자들이 나타나고 있어 의심 환자를 조기에 판단하고 선별할 수 있는 기술이 필요하다. 본 논문에서는 흉부 방사선 검사(chest Radiography; CXR) 영상을 딥러닝(Deep Learning)하여 정상인, 폐렴 환자, 코로나바이러스 감염자를 분류할 수 있도록 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.