• Safety and Health at Work
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• v.11 no.3
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• pp.275-283
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• 2020

Background: In the absence of a vaccine or treatment, the most pragmatic strategies against an infectious disease pandemic are extensive early detection testing and social distancing. This study aimed to summarize public and workplace responses to Coronavirus Disease-19 (COVID-19) and show how the Korean system has operated during the COVID-19 pandemic. Method: Daily briefings from the Korean Center for Disease Control and the Central Disaster Management Headquarters were assembled from January 20 to May 15, 2020. Results: By May 15, 2020, 11,018 COVID-19 cases were identified, of which 15.7% occurred in workplaces such as health-care facilities, call centers, sports clubs, coin karaoke, and nightlife destinations. When the first confirmed case was diagnosed, the Korean Center for Disease Control and Central Disaster Management Headquarters responded quickly, emphasizing early detection with numerous tests and a social distancing policy. This slowed the spread of infection without intensive containment, shut down, or mitigation interventions. After entering the public health blue alert level, a business continuity plan was distributed. After entering the orange level, the Ministry of Employment and Labor developed workplace guidelines for COVID-19 consisting of social distancing, flexible working schedules, early identification of workers with suspected infections, and disinfection of workplaces. Owing to the intensive workplace social distancing policy, workplaces remained safe with only small sporadic group infections. Conclusion: The workplace social distancing policy with timely implementation of specific guidelines was a key to preventing a large outbreak of COVID-19 in Korean workplaces. However, sporadic incidents of COVID-19 are still ongoing, and risk assessment in vulnerable workplaces should be continued.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.1
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• pp.49-60
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• 2022

This study attempted to provide the basic data for developing a system to identify the role of medical technologists and ensure an efficient response for quick and accurate diagnostic tests in the COVID-19 era. The research method involved using focus group interviews for a survey and analysis of 15 medical institutions. Eleven sample collection institutions, 10.4 medical technologists, 2.1 minutes of collection time, 5.4 hours of test time, 9,670 tests, 6.2 member test workforce size, and 7 screening center operating institutions were surveyed. The results of the focus group interview analysis revealed that there were no standardized guidelines covering working hours, area, and environment to protect sample collectors and testers in relation to the COVID-19 tests. Also, legal protection measures were insufficient in the event of accidental infections and there were no personnel regulations related to COVID-19. In addition, the professional training of sample collectors and molecular diagnostic testers was required for reliable COVID-19 testing. In conclusion, it is necessary to provide professional education through special test short-term training institutions to cope with emergency infectious diseases such as COVID-19. Legal systems should be put in place to protect the workforce and ensure stability.

• Safety and Health at Work
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• v.13 no.2
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• pp.255-260
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• 2022

Background: The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the importance of implementing strategic management that prioritizes the safety of frontline nurse professionals. In this sense, this research was aimed at identifying factors associated with the illness of nursing professionals caused by COVID-19 according to socio-demographic, clinical, and labor variables. Methods: A cross-sectional study was conducted in three Brazilian university hospitals with 859 nursing professionals, which include nurses, technicians, and nursing assistants, between November 2020 and February 2021. We present data using absolute and relative frequency. We used Chi-square test for hypothesis testing and multiple logistic regression for predictive analysis and chances of occurrence. Results: The rate of nursing professionals affected by COVID-19 was 41.8%, and the factors associated with contamination were the number of people in the same household with COVID-19 and obesity. Being a nurse was a protective factor when the entire nursing team was considered. The model is significant, and its variables represent 56.61% of the occurrence of COVID-19 in nursing professionals. Conclusion: Obesity and living in the same household as other people affected by COVID-19 increases the risk of contamination by this new coronavirus.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.447-455
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• 2023

COVID-19 is an emerging infectious disease that is hard to predict in terms of fatality rate, treatments, and the timing of its end. World is developing treatments and vaccines for COVID-19. Several treatments and vaccines currently have emergency use authorization, but the treatments are only allowed for critically ill patients with COVID-19. Therefore, the aim of this study is to analyze the confirmed cases of COVID-19, including mortality and testing, in OECD countries and to assess the effect of vaccination on mortality. Looking at the confirmed cases, mortality, and vaccination rates of COVID-19, the number of confirmed cases was lower than previously reported cases after full vaccination. In early 2022, with Omicron, the confirmed cases increased sharply, while mortality dropped, and the mortality showed a gentle curve as the cumulative fully vaccinated exceeded 50%. This indicates that COVID-19 vaccines have an effect on reducing mortality. However, the duration of effectiveness of vaccines was considerably short, which decreased the initial inoculation effect and increased the monthly mortality. As this study was carried out during the COVID-19 pandemic, there was not enough data to analyze comprehensively. However, it is meaningful to compare and analyze the impact of COVID-19 by country.

• 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.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.3
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• pp.257-265
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• 2021

The COVID-19 virus is a β-genus virus that causes infection by mediating the angiotensin convertible enzyme 2 (ACE2) receptor, which is distributed in large numbers in the human respiratory tract. The disease requires effective post-management of antibody production by complete healers and vaccinators because there is no perfect remedy for the virus infection. This study aimed to develop recombinant proteins specifically responsive to neutralizing antibodies in clinical specimens and use them to develop a rapid diagnostic kit to diagnose neutralizing antibodies quickly and conveniently against the COVID-19 virus and confirm the possibility of commercialization through a performance evaluation. Rapid diagnostic kits using COVID-19 S1 RBD recombinant proteins can be applied to rapid diagnostic kits, with positive percentage agreement (PPA) and negative percentage agreement (NPA) of 100% and 98.3%, respectively, compared to the U.S. FDA-approved ELISA kits. If the performance of the rapid diagnostic kit is improved and neutralizing antibodies can be analyzed quantitatively using quantitative analysis equipment, it can be used as important data to predict immunity to the COVID-19 virus and determine additional vaccinations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.53-60
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• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.951-959
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• 2021

The study investigated the effect of food safety knowledge, food safety trust and the factors influencing organic food purchase intention in the 'new normal' of the COVID-19 pandemic. The study employed non-contrived and cross-sectional methods. The data was collected in Malaysia using convenience sampling. A total of 330 valid questionnaires were analyzed using Structural Equation Modelling (SEM) and PROCESS for hypothesis testing. The study revealed a significant relationship involving food safety knowledge on personal attitude, perceived social pressure, and perceived autonomy. Moreover, organic food purchase intention was found to be influenced by personal attitude, perceived social pressure, and perceived autonomy. Interestingly, trust in organic food safety moderated the relationship between perceived autonomy and organic food purchase intention. The study proved valuable for stakeholders and organic food producers to understand the 'new normal' COVID-19 market scenario for a sound understanding of the market and the sustainability of the organic food industry. A new research framework is proposed and validated, related to individual purchase decision in global health issues which is limited in current literature. Hence, the study contributed to a better comprehension of green consumerism mainly in the Asian market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.737-745
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• 2021

This study aims to determine whether there are differences in the average abnormal return, trading volume activity, and trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of the coronavirus (COVID-19) in Indonesia. The sample was selected using a purposive sampling method and collected as many as nine pharmaceutical companies listed on the Indonesia Stock Exchange during 2019-2020. The data used in this study were secondary data in the form of daily data on stock closing prices, Composite Stock Price Index (IHSG), stock volume trading, number of shares outstanding, and stock trading frequency. This study was an event study with an observation period of 14 days, namely seven days before and seven days after the announcement of the coronavirus's first positive case in Indonesia. Hypothesis testing employed the paired sample t-test method. Based on the results, it was found that there was no difference in the average abnormal return of pharmaceutical stocks before and after the announcement of the first case of COVID-19. However, there was a difference in the average trading volume activity and the average trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of COVID-19.

• Journal of Preventive Medicine and Public Health
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• v.56 no.6
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• pp.515-522
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• 2023

Objectives: The pandemic caused by coronavirus disease 2019 (COVID-19) has exerted an unprecedented impact on the health of populations worldwide. However, the adverse health consequences of the pandemic in terms of infection and mortality rates have varied across countries. In this study, we investigate whether COVID-19 mortality rates across a group of developed nations are associated with characteristics of their healthcare systems, beyond the differential policy responses in those countries. Methods: To achieve the study objective, we distinguished healthcare systems based on the extent of healthcare decommodification. Using available daily data from 2020, 2021, and 2022, we applied quantile regression with non-additive fixed effects to estimate mortality rates across quantiles. Our analysis began prior to vaccine development (in 2020) and continued after the vaccines were introduced (throughout 2021 and part of 2022). Results: The findings indicate that higher testing rates, coupled with more stringent containment and public health measures, had a significant negative impact on the death rate in both pre-vaccination and post-vaccination models. The data from the post-vaccination model demonstrate that higher vaccination rates were associated with significant decreases in fatalities. Additionally, our research indicates that countries with healthcare systems characterized by high and medium levels of decommodification experienced lower mortality rates than those with healthcare systems involving low decommodification. Conclusions: The results of this study indicate that stronger public health infrastructure and more inclusive social protections have mitigated the severity of the pandemic's adverse health impacts, more so than emergency containment measures and social restrictions.