• Journal of Nutrition and Health
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• v.54 no.1
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• pp.1-9
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• 2021

This review describes the risk factors of the nutrition crisis in coronavirus disease 2019 (COVID-19) infections and suggests precision nutrition against long-term psychological and physiological stress. The mandatory quarantine and the social distancing are associated with an interruption of the lifestyle routine, resulting in psychological (i.e., boredom) and physiological stress. The stress with multiple causes and forms induces over-compensation of energy-dense food, such as sugary comfort food, and is defined as "food craving" because carbohydrates positively affect the psychological stability with serotonin secretion. The consumption of foods that promote an immune response against viral infections (vitamins & minerals; Cu, folate, Fe, Se, Zn, and Vit A, B6, B12, C, and D), reduce inflammatory cytokines (w-3 fatty acids, Vit D, fibers, and Mg), contain antioxidants (beta-carotene, Vit E, C, Se, and phenolics), and sleep-inducing proteins (serotonin, melatonin, and milk products) is essential. In addition, a reduced Vit D deficiency in winter due to less time spent outdoors under quarantine has been reported to be associated with viral infections. The case fatality rate of COVID-19 was significantly dependent on age, sex, race, and underlying health condition. To prevent malnutrition and cachexia in elderly people, weight loss and muscle wasting should be monitored and controlled. Inadequate protein intake, sedentary lifestyle, and inflammation are significant risk factors for sarcopenia. Moreover, relatively high intakes of fat or carbohydrate compared to low protein intake result in abdominal obesity, which is defined as "sarcopenic obesity." Keeping the food-safety guidelines of COVID-19, this study recommends the consumption of fresh and healthy foods and avoiding sugar, fat, salt, alcohol, and commercially frozen foods.

• Journal of Preventive Medicine and Public Health
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• v.54 no.2
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• pp.86-95
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• 2021

Objectives: This study investigated associations between perceptions of coronavirus disease 2019 (COVID-19) and the prevalence of posttraumatic stress disorder (PTSD) in workers at hospitals designated to treat COVID-19, as well as the difference in the magnitude of these associations by occupational type and previous Middle East respiratory syndrome coronavirus (MERS-CoV) experience. Methods: The participants were workers at hospitals designated to treat COVID-19 who completed a questionnaire about their perceptions related to COVID-19, work experience during the previous MERS-CoV outbreak, and symptoms of PTSD ascertained by the PTSD Checklist for the Diagnostic and Statistical Manual of Mental Disorders. Participants' characteristics were compared using the chi-square test. Multivariable logistic regression was performed to evaluate the associations between perceptions and the prevalence of PTSD, stratified by occupational type and previous MERS-CoV experience. Results: Non-medical personnel showed stronger associations with PTSD than medical personnel according to general fear (odds ratio [OR], 6.67; 95% confidence interval [CI], 1.92 to 23.20), shortages of supplies (OR, 1.29; 95% CI, 1.07 to 1.56), and issue-specific fear (OR, 1.29; 95% CI, 1.05 to 1.59). Those with prior MERS-CoV quarantine experience were more prone to PTSD than those without such experience in terms of general fear (OR, 1.70; 95% CI, 1.22 to 2.37), shortages of supplies (OR, 1.24; 95% CI, 1.10 to 1.40), and issue-specific fear (OR, 1.21; 95% CI, 1.06 to 1.38). Conclusions: During the COVID-19 pandemic, non-medical personnel tended to have higher odds of being categorized as having PTSD. Workers with prior MERS-CoV experience were more susceptible than those without such experience. These findings suggest the need for timely interventions to manage human resources for a sustainable quarantine system.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.787-793
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• 2022

After COVID-19, with the emergence of social distancing, non-face-to-face services, and home economics, visiting dining out is rapidly being replaced by non-face-to-face dining out. The purpose of this study is to find ways to create a safe dining culture centered on living quarantine in line with the changing trend of the restaurant industry after the outbreak of COVID-19, establish the direction of food culture improvement projects, and enhance the effectiveness of the project. This study used TEXTOM to collect and refine search frequency, perform TF-IDF analysis, and Ucinet6 programs to implement visualization using NetDraw from January 1, 2018 to October 31, 2019 and December 31, 2021, and identified the network between nodes of key keywords. Finally, clustering between them was performed through Concor analysis. As a result of the study, if you check the frequency of searches before and after COVID-19, it can be seen that the COVID-19 pandemic greatly affects the changes in the restaurant industry.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.97-107
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• 2022

This study was performed to understand the meaning and essence of class experience of nursing students in the post-COVID 19 age. The participants in this study were 10 enrolled students from freshman to senior students who experienced the face-to-face and contactless classes during four semesters after the COVID-19 pandemic. The data collection period was from December 9 to December 30, 2021. The collected data was analyzed by applying the Colaizzi method. According to the study result, 25 themes, 13 collections of the themes, and six categories were drawn. The six categories were as follows: vagueness of the future, Lack of confidence in nursing practice, Class system stabilization, Acceptance and adaptation of situations, Have a sense of vocational calling as a pre-registration nurses, Finding the direction to improvement of contactless classes. The nursing students' understanding of class experience at present two years after the outbreak of COVID-19 can be used as basic data to seek future educational direction strategy and enhance future education quality.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.207-218
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• 2023

Jeju Island is a major area generates origin-destination trips, accounting for about 90 % of domestic air transportation, and popular tourist destination visited by more than 10 million domestic and foreign tourists annually. Travel behavior patterns of tourists in Jeju Island have great meaning for not only Jeju Island, but also the inland aviation, tourism, mobility industry. This study presented passenger travel behavior in Jeju Island based on a survey including foreign visitors and residents as well as domestic visitors. In particular, the survey was conducted in early 2020 prior to the COVID-19 pandemic, it is expected to be a major preliminary study for changes in tourist travel and air travel in Jeju Island before and after COVID-19.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.793-802
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• 2023

The aim of this study analyzed changes in dietary habits and lifestyles before and after COVID-19 targeting adolescents, using the food consumption behavior survey (2019 vs 2021). In the change in health-related factors, height decreased overall, and a significant difference was especially evident in males. Awareness that functional foods and eco-friendly foods contribute to health has increased. Among the results of dietary behavior, the frequency of skipping breakfast showed that the rate of not skipping breakfast and the rate of skipping breakfast more than 5 times increased at the same time(p=0.019). The rate of eating out decreased significantly after COVID-19, and it was analyzed that schools and school cafeteria, as well as Street carts or restaurants and academy, all increased significantly as places where snacks were not consumed. In order to analyze changes in food-related lifestyle, it was grouped into convenience-seeking, quality/safety-seeking, taste-seeking, and health/safety-seeking. 'Small packaged or pre-processed products' decreased. On the other hand, items such as 'Safety rather than price when choosing food' and 'Don't eat food that could go bad' improved. 'Tend to eat regularly' was higher than 2021 compared to 2019. Also 'Tend to purchase HACCP and GAP-certified products' are increased. Because of COVID-19 changes in lifestyle have affected the diet of adolescents. The results of this study suggest that it can be used as a guideline establishment and nutrition counseling material for the formation of correct eating habits for adolescents in the future pandemic era.

• Health Policy and Management
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• v.33 no.4
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• pp.421-431
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• 2023

Background: The coronavirus disease 2019 (COVID-19) pandemic has led to socio-economic issues, highlighting the importance of strengthening health systems for future infectious diseases. This study aims to analyze the relationship between health system preparedness, response levels, and COVID-19 fatality rates across 194 countries. Methods: This study examined various indicators of national health system preparedness and response, including health service delivery, health workforce, health information systems, essential medicines and health products, health financing, and leadership and governance. Results: A correlation was found between the health system and the COVID-19 case fatality rate (CFR). Further examination of specific indicators within health service delivery, health workforce, health information systems, health financing, and leadership/governance showed significant correlations with the CFR. Multiple regression analysis, considering aging and urbanization rates, identified reproductive/maternal/newborn and child health, infectious diseases, nursing and midwifery personnel density, birth registration coverage, and out-of-pocket health expenditure as significant factors affecting the CFR. Conclusion: Countries with strong health system indicators experience lower case fatality rate from COVID-19. Strengthening access to essential health services, increasing healthcare personnel and resources, ensuring reliable health information, and bolstering overall health systems are crucial for preparedness against future infectious diseases.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.255-263
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• 2024

GDP represents the total market value of goods and services produced by all economic entities, including households, businesses, and governments in a country, during a specific time period. It is a representative economic indicator that helps identify the size of a country's economy and influences government policies, so various studies are being conducted on it. This paper presents a GDP growth rate forecasting model based on a dynamic factor model using key macroeconomic indicators of G20 countries. The extracted factors are combined with various regression analysis methodologies to compare results. Additionally, traditional time series forecasting methods such as the ARIMA model and forecasting using common components are also evaluated. Considering the significant volatility of indicators following the COVID-19 pandemic, the forecast period is divided into pre-COVID and post-COVID periods. The findings reveal that the dynamic factor model, incorporating ridge regression and lasso regression, demonstrates the best performance both before and after COVID.