• CELLMED
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• v.10 no.4
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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.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.479-493
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• 2020

Outbreak of COVID-19 originated from China resulted significantly high casualties and social and economic damages. Currently the major countries see importance of accurate prediction of originating trend to prevent the spread of infectious disease and AI is actively utilized when establishing the system. Therefore this study has comprehended the status of utilizing the AI in overseas and made comparison and analysis with domestic status. It derived the necessity to establish national control tower based on One Health to respond to infectious disease to effectively utilize AI and suggested to establish higher organization, Medical Big Data Governance, to respond to the infectious disease. It is necessary to conduct further study to utilize the results and suggestions derived from this study into the policy and if the suggestions are reflected to improve institutional imperfection, it will be positively used for prevention of the spreading infectious disease and utilizing medical Big Data.

• Journal of Industrial Convergence
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• v.19 no.3
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• pp.35-40
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• 2021

This study attempted to derive important factors of emerging infectious diseases by collecting and analyzing text data onto emerging infectious diseases. For this purpose, articles in the Naver News database were directly crawled, pre-processed, and used for data analysis. In addition, additional analysis was performed using Big Kinds. As a result of the priority analysis, the importance was shown in the order of corona, infectious disease, quarantine, vaccine, outbreak, virus, infection, and development. As a result of the proximity centrality analysis, the importance was shown in the order of government, death, and plan, and the analysis result of Big Kinds showed that Covid-19 and the Korea Centers for Disease Control and Prevention were important. Based on the results of this study, it can be said that the government's policy support is needed to raise public awareness of new infectious diseases, prevent disease, and develop vaccines and treatments.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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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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• v.22 no.1
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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.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.12a
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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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• 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.

• International journal of advanced smart convergence
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• v.9 no.4
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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%.

• Atmosphere
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• v.32 no.1
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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.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.