• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of Preventive Medicine and Public Health
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• v.53 no.3
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• pp.158-163
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• 2020

Objectives: In the current early phase of the coronavirus disease 2019 (COVID-19) outbreak, Bali needs to prepare to face the escalation of cases, with a particular focus on the readiness of healthcare services. We simulated the future trajectory of the epidemic under current conditions, projected the impact of policy interventions, and analyzed the implications for healthcare capacity. Methods: Our study was based on the first month of publicly accessible data on new confirmed daily cases. A susceptible, exposed, infected, recovered (SEIR) model for COVID-19 was employed to compare the current dynamics of the disease with those predicted under various scenarios. Results: The fitted model for the cumulative number of confirmed cases in Bali indicated an effective reproduction number of 1.4. Interventions have decreased the possible maximum number of cases from 71 125 on day 86 to 22 340 on day 119, and have prolonged the doubling time from about 9 days to 21 days. This corresponds to an approximately 30% reduction in transmissions from cases of mild infections. There will be 2780 available hospital beds, and at the peak (on day 132), the number of severe cases is estimated to be roughly 6105. Of these cases, 1831 will need intensive care unit (ICU) beds, whereas the number of currently available ICU beds is roughly 446. Conclusions: The healthcare system in Bali is in danger of collapse; thus, serious efforts are needed to improve COVID-19 interventions and to prepare the healthcare system in Bali to the greatest extent possible.

• Journal of Preventive Medicine and Public Health
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• v.55 no.6
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• pp.568-577
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• 2022

Objectives: Many studies have shown that social distancing, as a non-pharmaceutical intervention (NPI) that is one of the various measures against coronavirus disease 2019 (COVID-19), is an effective preventive measure to suppress the spread of infectious diseases. This study explored the relationships between traditional health-related behaviors in Korea and social distancing practices during the COVID-19 pandemic. Methods: Data were obtained from the 2020 Community Health Survey conducted by the Korea Disease Control and Prevention Agency (n=98 149). The dependent variable was the degree of social distancing practice to cope with the COVID-19 epidemic. Independent variables included health-risk behaviors and health-promoting behaviors. The moderators were vaccination and unmet medical needs. Predictors affecting the practice of social distancing were identified through hierarchical multiple logistic regression analysis. Results: Smokers (adjusted odds ratio [aOR], 0.924) and frequent drinkers (aOR, 0.933) were more likely not to practice social distancing. A greater degree of physical activity was associated with a higher likelihood of practicing social distancing (aOR, 1.029). People who were vaccinated against influenza were more likely to practice social distancing than those who were not (aOR, 1.150). However, people with unmet medical needs were less likely to practice social distancing than those who did not experience unmet medical needs (aOR, 0.757). Conclusions: Social distancing practices were related to traditional health behaviors such as smoking, drinking, and physical activity. Their patterns showed a clustering effect of health inequality. Therefore, when establishing a strategy to strengthen social distancing, a strategy to protect the vulnerable should be considered concomitantly.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.499-512
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• 2022

Intervention measures have been implemented worldwide to reduce the spread of the COVID-19 outbreak. The COVID-19 outbreak has occured in several waves of infection, so this paper is divided into three groups, namely those countries who have passed the pandemic period, those countries who are still experiencing a single-wave pandemic, and those countries who are experiencing a multi-wave pandemic. The purpose of this study is to develop a multi-wave Richards model with several changepoint detection methods so as to obtain more accurate prediction results, especially for the multi-wave case. We investigated epidemiological trends in different countries from January 2020 to October 2021 to determine the temporal changes during the epidemic with respect to the intervention strategy used. In this article, we adjust the daily cumulative epidemiological data for COVID-19 using the logistic growth model and the multi-wave Richards curve development model. The changepoint detection methods used include the interpolation method, the Pruned Exact Linear Time (PELT) method, and the Binary Segmentation (BS) method. The results of the analysis using 9 countries show that the Richards model development can be used to analyze multi-wave data using changepoint detection so that the initial data used for prediction on the last wave can be determined precisely. The changepoint used is the coincident changepoint generated by the PELT and BS methods. The interpolation method is only used to find out how many pandemic waves have occurred in given a country. Several waves have been identified and can better describe the data. Our results can find the peak of the pandemic and when it will end in each country, both for a single-wave pandemic and a multi-wave pandemic.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.761-770
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• 2023

This study was conducted to identify factors affecting depression in the elderly using three-year Community Health Survey data from 2020, when COVID-19 was declared an epidemic. Differences in depression according to general characteristics, health behavior, subjective health level, and medical use among 220,921 elderly were analyzed using complex samples t-test and ANOVA, and multiple regression analysis was performed to identify factors affecting depression it was carried out. As a result of the study, the level of depression among elderly women was found to be high, with an average of 1.21±0.01 for elderly men and 1.74±0.02 for elderly women, and there was a difference in generation type, with depression being higher in the first generation for elderly men and the third generation for elderly women. Variables that had a great influence on depression were the experience of depression and perceived stress.

• Journal of Preventive Medicine and Public Health
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• v.56 no.5
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• pp.431-439
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• 2023

Objectives: Numerous studies have explored the causes and spread of outbreaks, yet there is a lack of research on post-coronavirus disease 2019 condition (PCC) in Korea. The goal of this study was to identify the various types of PCC and associated factors in discharged patients and to provide directions for the ongoing health management of confirmed patients. Methods: A telephone survey was conducted among 680 coronavirus disease 2019 (COVID-19) patients diagnosed between July 7, 2021 and August 26, 2021, in Dangjin, Chungnam, Korea. A descriptive analysis of characteristics, univariate analysis, and regression were performed using data from basic epidemiological surveys conducted at the time of diagnosis and post-discharge questionnaires. Results: Of the 585 patients who responded, 159 (27.2%) developed PCC. Of the 211 patients with no initial symptoms, 27 (12.8%) developed PCC, versus 132 (35.3%) of the 374 patients with initial symptoms. Among the initial symptoms, fever or chills, cough or sputum, loss of smell, and sore throat were associated with PCC. Compared to patients with less than 10 days of hospitalization, those with a hospitalization period of 21 days to 30 days (odds ratio [OR], 2.3; 95% confidence interval [CI], 1.0 to 5.2) and 31 days or more (OR, 5.8; 95% CI, 1.9 to 18.1) had a higher risk of PCC. Conclusions: More than a quarter of COVID-19 patients, including those who had no initial symptoms, experienced PCC in Korea. People with the initial symptoms of fever, chills, and respiratory symptoms and those who had prolonged hospital stays had a high risk of PCC.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.515-521
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• 2021

The popularity of Islamic financial instruments among Muslims is not surprising. The Islamic capital market is where sharia-compliant financial assets are transacted. It works parallel to the conventional market and helps investors find sharia-compliant investment opportunities. At a time of collective confusion when the COVID-19 epidemic is contributing to unprecedented change, this paper is keen to understand how attractive conventional and Islamic stock markets have been to investors recently. Second, this paper takes advantage of the time-scale decomposition property of the wavelet to simultaneously capture risk exposure and distinguish the risks faced by short- and long-term investors. To this end, this research conducted a two-step investigation of the daily closing equity market price indices for three Islamic stock markets and their conventional counterparts. Given that different financial decisions occur with greater or less frequency, the paper examines the connectedness of stock markets operating at heterogeneous rates and identifies the timescales using wavelet-DCC-GARCH analysis to take account of both the time and the frequency domains of stock market connectedness. The paper findings highlight the strong evidence of contagion that can be seen in nearly all conventional stock markets in the COVID-19 pandemic; they reach a high level of dependency in such health crises. Furthermore, Islamic stock markets prove to be a rich ground for global diversification.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.249-257
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• 2022

COVID-19 struck without warning, and by the first quarter of 2020, the world had plunged into a state of total closure as a means of containing the pandemic's devastating effect. Certainly, the pandemic shook many economies; some countries were able to cope, while third-world countries lost their invulnerability. Based on this, the current study looked at financial reports from Kuwaiti conventional and Islamic banks from 2019 to 2020 (before and after the pandemic) and compared the findings to see how much of an impact Kuwaiti conventional and Islamic banks had during the COVID-19 epidemic. Financial analysis of financial reports was used as a quantitative methodology, and variables were compared and analyzed, including (the liquidity ratio, profitability ratio, and financial leverage) within (14) Kuwaiti conventional and Islamic banks. The study found that the pandemic had a detrimental impact on both conventional and Islamic banks in Kuwait, as they were the first line of defense for the Kuwaiti economy during lockdowns and quarantines. Furthermore, there were significant implications on the Rate of Return on Investment, Debt, Financial Leverage, and Return on Equity.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.12a
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• pp.14-14
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• 2020

In December 2019, the COVID-19 epidemic was discovered in Wuhan, China, and since has disseminated around the world impacting human health for millions. Herein, in-silico drug discovery approaches were utilized to identify potential candidates as Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) main protease (M^pro) inhibitors. We investigated several databases including natural and natural-like products (>100,000 molecules), DrugBank database (10,036 drugs), major metabolites isolated from daily used spices (32 molecules), and current clinical drug candidates for the treatment of COVID-19 (18 drugs). All tested compounds were prepared and screened using molecular docking techniques. Based on the calculated docking scores, the top ones from each project under investigation were selected and subjected to molecular dynamics (MD) simulations followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. Combined long MD simulations and MM-GBSA calculations revealed the potent compounds with prospective binding affinities against M^pro. Structural and energetic analyses over the simulated time demonstrated the high stabilities of the selected compounds. Our results showed that 4-bis([1,3]dioxolo)pyran-5-carboxamide derivatives (natural and natural-like products database), DB02388 and Cobicistat (DB09065) (DrugBank database), salvianolic acid A (spices secondary metabolites) and TMC-310911 (clinical-trial drugs database) exhibited high binding affinities with SARS-CoV-2 M^pro. In conclusion, these compounds are up-and-coming anti-COVID-19 drug candidates that warrant further detailed in vitro and in vivo experimental estimations.

• Journal of Distribution Science
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• v.22 no.1
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• pp.47-59
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• 2024

Purposes: The study aimed at examining the impact of the COVID-19 pandemic on the shift of online consumer purchasing behavior and whether the new behaviors would be maintained after the epidemic season. The study also aims to investigate how online customers change based on perceived risks. Research design and Methodology: The study investigated purchasing behavior of the same 377 online Vietnamese consumers during two periods: (1) during the period of social distancing and (2) one and half year after that, allowing data to be collected in real time, so that consumers do not have to recall their behavior. Results: Purchasing behavior appeared to be more influenced by gender, age and household size. Aged consumers are more concerned about risks than those in the younger group, who only worry about the risks during the pandemic. Consumers in households with two or more people are more concerned about the risks than those living alone. Female appeared to be more influential in both during and after pandemic than male. Conclusions: The findings contribute to clarify shift of online consumer purchasing behavior, which helps business to develop effective marketing strategies and enhance their presence in the e-commerce sector.