• 셀메드
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• 제10권4호
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• pp.29.1-29.6
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• 2020

COVID-19 (Corona Virus Disease-2019) is an infectious respiratory disease caused by the most recently discovered coronavirus, SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona virus-2). This new viral disease was unknown before the outbreak began in Wuhan, China, in December 2019. As of November 16th 2020, it affects about 54.3 million populations, death troll increased to 1.32 million cases in worldwide. Whereas in India 8.85 cases are infected with COVID-19, of which 1, 30, 112 cases were died. Till now there has been no specific anti-virus drug or vaccines are available for the treatment of this disease, the supportive care and non-specific treatment to the symptoms of the patient are the only options in Biomedicine, the entire world turns its attention towards alternative medicine or Traditional medicine. Siddha medicine is one of the primordial systems of medicine practiced in the southern part of India, it dealt a lot about pandemic, and its management. This review provides an insight into Pandemic in Siddha system and its management in both ancient history and modern history, National and state level Government policies related to current pandemic, World Health Organization (WHO) guidelines on usage of unproven drug during infectious disease outbreak, Preparedness of Siddha system during a pandemic outbreak Challenges and Recommendations.

• 디지털융복합연구
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• 제18권6호
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• pp.479-493
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• 2020

중국 우한 시에서 최초로 발병한 코로나바이러스감염증(이하 COVID-19)으로 인한 인명 피해 및 사회·경제적 손실은 매우 크며 현재 세계 주요 각국에서는 COVID-19와 같은 감염병의 확산을 막기 위해서는 발생 추이를 초기에 정확히 예측하는 것이 중요하다고 보고 감염병 대응 체계 구축 시 인공지능을 적극 활용하고 있다. 이에 본 연구에서는 감염병 확산에 대응하기 위한 해외 각국의 인공지능 활용 현황을 파악, 국내 현황과 비교·분석했으며 몇 가지 시사점을 도출할 수 있었다. 연구 결과 보다 효율적으로 인공지능을 활용해 감염병에 대응하기 위해서는 원 헬스(One Health) 기반의 국가 컨트롤타워 구축이 필요하다는 결론이 도출되었으며 이에 컨트롤타워가 갖춰야 할 요건을 살펴보았다. 또한 국가 안보 차원에서의 감염병 대응을 위해 상위 기관인 의료 빅데이터 거버넌스를 설립할 것을 제안하였다. 향후 본 연구에서 도출된 결론 및 시사점을 정책적으로 활용하기 위한 연구가 필요할 것으로 보이며 본 연구가 제안하는 바를 반영해 제도적 미비점을 보완한다면 감염병 확산 방지 및 의료 빅데이터를 유용하게 활용하는데 긍정적으로 작용할 것으로 전망된다.

• 산업융합연구
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• 제19권3호
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• pp.35-40
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• 2021

본 연구는 신종감염병에 대한 텍스트 데이터를 수집하고, 이를 분석하여 신종감염병에서 중요한 요인을 도출하고자 하였다. 이를 위해 네이버 뉴스 데이터베이스의 기사를 직접 크롤링하고, 이를 전처리 하여, 데이터 분석에 활용하였다. 또한 빅카인즈를 활용하여 추가적인 분석을 실시하였다. 우선순위 분석결과, 코로나, 전염병, 방역, 백신, 발생, 바이러스, 감염, 개발 순으로 그 중요도가 나타났다. 근접중심성 분석 결과 정부, 사망, 계획 순으로 그 중요도가 나타났으며, 빅카인즈 분석결과는 코로나 19, 질병관리 본부 등이 중요한 것으로 나타났다. 본 연구의 결과를 토대로 신종감염병에 대한 대국민 인식 제고 및 방역, 백신 및 치료제 개발 등에 정부의 정책적인 지원이 필요하다고 할 수 있다.

• 동의생리병리학회지
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• 제36권5호
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• pp.147-154
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• 2022

In order to secure practising rapid pattern(證, zheng) differentiation against acute infectious diseases like corona virus disease-19(COVID-19) showing rapid variation and contagion, a simplified classification of stages centering on the exterior-interior pattern identification with 2 step-subdivision by cold, heat, deficiency, excess pattern and pathogens is proposed. Pattern differentiation by compound patterns of 8 principles is made for the non-severe stage of general cold and the early mild stage of epidemic disease. Compound pattern's names of 8 principles about external infectious diseases are composed of three stages, that is disease site-characters-etiology. Based on early stage symptoms of fever or chilling etc., exterior, interior and half exterior and half interior patterns are determined first, and then cold, heat, deficiency, excess patterns of exterior and interior pattern respectively are determined, and then more concrete differentiation on pathogens of wind, dryness, dampness and dearth of qi, blood, yin, yang accompanied with constitutional and personal illness factors. Summarizing above descriptions, 4 patterns of exterior cold, exterior heat, exterior deficiency, exterior excess and their secondary compound patterns of exterior cold deficiency and exterior cold excess and so on are classified together with treatment method and available decoction for a standard measure of eight principle pattern differentiation.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2020년도 추계국제학술대회
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• pp.15-15
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• 2020

In an internal document, CORONA-VIRUS DISEASE 2019 (COVID-19) PLAN, release developed stated that "on January 22, 2020, Palau Ministry of Health activated its emergency operations center, and since then has prepared and put in place measures in response to this global pandemic." The actions eventually led to the closure of most flights coming into Palau as a method to protect its population. The population of is at high risk with COVID19 due to the very elevated rate of NCD's, as well as the limited access to proper testing and treatment facilities. Increased use of traditional medicines in the population has reduced the co-morbidities by reducing risk factors. Furthermore, the expansion of tradtional NCD therapies, especially that of DAK reduce pressure due to obesity and diabetes therefore allowing for unimpaired immune systems to combat deadly infectious diseases such as COVID19.

• International Journal of Advanced Culture Technology
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• 제10권1호
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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.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• 대기
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• 제32권1호
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• pp.51-60
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• 2022

Corona Virus Disease 19 pandemic (COVID-19) causes many deaths worldwide, and has enormous impacts on society and economy. The COVID-19 was caused by a new type of coronavirus (Severe Acute Respiratory Syndrome Cornonavirus 2; SARS-CoV-2), which has been found that these viruses can be effectively inactivated by ultraviolet (UV) radiation of 290~315 nm. In this study, 90% inactivation time of the SARS-CoV-2 virus was analyzed using ground observation data from Brewer spectrophotometer at Yonsei University, Seoul and simulation data from UVSPEC for the period of 2015~2017 and 2020. Based on 12:00-13:00 noon time, the shortest virus inactivation time were estimated as 13.5 minutes in June and 4.8 minutes in July/August, respectively, under all sky and clear sky conditions. In the diurnal and seasonal variations, SARS-CoV-2 could be inactivated by 90% when exposed to UV radiation within 60 minutes from 10:00 to 14:00, for the period of spring to autumn. However, in winter season, the natural prevention effect was meaningless because the intensity of UV radiation weakened, and the time required for virus inactivation increased. The spread of infectious diseases such as COVID-19 is related to various and complex interactions of several variables, but the natural inactivation of viruses by UV radiation presented in this study, especially seasonal differences, need to be considered as major variables.

• 산업융합연구
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• 제21권1호
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• pp.175-185
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• 2023

최근 COVID-19와 같은 신종 바이러스 감염증이 확산하여 심각한 공중 보건 문제를 제기하고 있다. 특히 이러한 질병은 고령자에게 치명적으로 작용하여, 생명을 위협하고 심각한 사회적, 경제적 손실을 초래하였다. 이에 많은 산업분야에서 사물 인터넷(IoT) 및 인공 지능(AI)을 응용한 원격진료, 헬스케어, 질병예방 등의 애플리케이션이 소개되어 질병 감지, 모니터링 및 검역 성능을 향상하고 있다. 하지만 기존기술은 갑작스러운 전염병의 출현에 신속하고 통합적으로 적용되지 않기 때문에, 사회 속에 감염병이 대규모 감염 및 전국적 확산되는 것을 차단하지 못하였다. 따라서 본 논문에서는 바이러스 질병 정보 수집기를 통해 지역적 한계가 있는 다양한 감염 정보를 수집하고, AI 브로커를 통해 AI 분석 및 심각도 매칭을 수행하여 감염의 확산을 예측하고자 한다. 최종에는 질병관리본부를 통해 고령자에게 위험경보 발령, 확산 차단 문자 발송 및 감염지역 대피정보를 신속하게 제공한다. 현실적인 고령자 지원시스템은 감염자 발생지역 정보와 고령자의 위치정보를 비교하여 증강현실 기반의 스마트폰 애플리케이션으로 직관적인 위험지역(감염지역) 회피기능을 제공하고 감염지역 방문이 확인되면 자동으로 방역관리 서비스를 제공한다. 향후 제안시스템은 위치기반의 사용자 밀집도를 파악함으로써 갑작스런 인파 집중으로 인한 압사 사고를 사전에 예방하는 방법으로도 활용 가능할 것이다.