• 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.

• Microbiology and Biotechnology Letters
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• v.48 no.3
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• pp.252-266
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• 2020

The coronavirus disease 2019 (COVID-19) is a highly contagious pneumonia that has spread throughout the world. It is caused by a novel, single stranded RNA virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Genetic analysis revealed that, phylogenetically, the SARS-CoV-2 is related to severe acute respiratory syndrome-like viruses seen in bats. Because of this, bats are considered as a possible primary reservoir. The World Health Organization has declared the COVID-19 outbreak as a pandemic. As of May 27, 2020, more than 5,406,282 confirmed cases, and 343,562 confirmed deaths have been reported worldwide. Currently, there are no approved vaccines or antiviral drugs available against COVID-19. Newly developed vaccines are in the first stage of clinical trials, and it may take a few months to a few years for their commercialization. At present, remdesivir and chloroquine are the promising drugs for treating COVID-19 patients. In this review, we summarize the diversity, genetic variations, primary reservoirs, epidemiology, clinical manifestations, pathogenesis, diagnosis, treatment strategies, and future prospects with respect to controlling the spread of COVID-19.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.265-283
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• 2022

To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic un-hospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.155-162
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• 2022

Even after testing negative for COVID-19, some patients continue to struggle with a variety of symptoms such as fatigue, shortness of breath, gastrointestinal problems and neurological problems. The World Health Organization (WHO) defined long COVID (Post COVID-19 conditions) as "A disease occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months, that cannot be explained by an alternative diagnosis." As a possible pathological mechanism of long COVID, three hypotheses are proposed: the persistence of the infectious state due to the residual virus, the persistent inflammatory response, and the autoimmune response. The main symptoms of long COVID are shortness of breath (dyspnea), abdominal pain and dyspepsia, fatigue, cognitive problems (brain fog), anosmia and dysgeusia, and chest pain, palpitations and tachycardia. In the Chinese guidelines, COVID-19 patients were divided into mild, moderate, severe, and recovery, and prescriptions with effective therapeutic effects were summarized to encourage combined treatment of chinese and western medicine. Globally, only symptomatic therapy is recommended for long COVID, but a specific treatment has not yet been proposed. Recently, morbidity code for post COVID-19 conditions was created, and it is planned to announce guidelines for long COVID treatment and management in the first half of 2023. In line with this trend, the Korean medical community needs to make efforts to prepare treatment guidelines for patients with long COVID.

• BMB Reports
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• v.55 no.9
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• pp.465-471
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• 2022

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of large-scale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.154-168
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• 2021

COVID-19 has been spreading all around the world, and threatening global health. In this situation, identifying and isolating infected individuals rapidly has been one of the most important measures to contain the epidemic. However, the standard diagnosis procedure with RT-PCR (Reverse Transcriptase Polymerase Chain Reaction) is costly and time-consuming. For this reason, pooled testing for COVID-19 has been proposed from the early stage of the COVID-19 pandemic to reduce the cost and time of identifying the COVID-19 infection. For pooled testing, how many samples are tested in group is the most significant factor to the performance of the test system. When the arrivals of test requirements and the test time are stochastic, batch-service queueing models have been utilized for the analysis of pooled-testing systems. However, most of them do not consider the false-negative test results of pooled testing in their performance analysis. For the COVID-19 RT-PCR test, there is a small but certain possibility of false-negative test results, and the group-test size affects not only the time and cost of pooled testing, but also the false-negative rate of pooled testing, which is a significant concern to public health authorities. In this study, we analyze the performance of COVID-19 pooled-testing systems with false-negative test results. To do this, we first formulate the COVID-19 pooled-testing systems with false negatives as a batch-service queuing model, and then obtain the performance measures such as the expected number of test requirements in the system, the expected number of RP-PCR tests for a test sample, the false-negative group-test rate, and the total cost per unit time, using the queueing analysis. We also present a numerical example to demonstrate the applicability of our analysis, and draw a couple of implications for COVID-19 pooled testing.

• Journal of Preventive Medicine and Public Health
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• v.56 no.2
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• pp.180-189
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• 2023

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of medical resources; therefore, we investigated whether COVID-19 impacted the quality of non-COVID-19 hospital care in Korea by comparing hospital standardized mortality rates (HSMRs) before and during the pandemic. Methods: This retrospective cohort study analyzed Korean National Health Insurance discharge claim data obtained from January to June in 2017, 2018, 2019, and 2020. Patients' in-hospital deaths were classified according to the most responsible diagnosis categories. The HSMR is calculated as the ratio of expected deaths to actual deaths. The time trend in the overall HSMR was analyzed by region and hospital type. Results: The final analysis included 2 252 824 patients. In 2020, the HSMR increased nationwide (HSMR, 99.3; 95% confidence interval [CI], 97.7 to 101.0) in comparison to 2019 (HSMR, 97.3; 95% CI, 95.8 to 98.8). In the COVID-19 pandemic zone, the HSMR increased significantly in 2020 (HSMR, 112.7; 95% CI, 107.0 to 118.7) compared to 2019 (HSMR, 101.7; 95% CI, 96.9 to 106.6). The HSMR in all general hospitals increased significantly in 2020 (HSMR, 106.4; 95% CI, 104.3 to 108.5) compared to 2019 (HSMR, 100.3; 95% CI, 98.4 to 102.2). Hospitals participating in the COVID-19 response had a lower HSMR (HSMR, 95.6; 95% CI, 93.9 to 97.4) than hospitals not participating in the COVID-19 response (HSMR, 124.3; 95% CI, 119.3 to 129.4). Conclusions: This study suggests that the COVID-19 pandemic may have negatively impacted the quality of care in hospitals, especially general hospitals with relatively few beds. In light of the COVID-19 pandemic, it is necessary to prevent excessive workloads in hospitals and to properly employ and coordinate the workforce.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.163-168
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• 2022

After the outbreak of COVID-19 in China, the national health commission of the people's republic of China distributed guidelines for the diagnosis and treatment of COVID-19. Based on that, each region of China made guidelines for traditional Chinese medical treatment of COVID-19 applicable to clinical field. Under the hypothesis that each region's guideline contains regional characteristics, a comparison was made on pattern identification among each region's guidelines and central guidelines. At the beginning of the analysis of the cases, opinions on pattern identification vary from region to region, and the diversity is mainly reflected in the early stage of the disease. When the guideline is organized to a certain level due to the accumulation of clinical cases, there is a strong tendency to enumerate various types of pattern identification. It means that as a specific infectious disease progresses, it can appear in various cases due to variables. In some guidelines, disease stages were analyzed by only a limited pathological mechanism, but no regional characteristics were found here. Rather, it may mean that unique characteristics for disease can be derived.

• The Journal of Korean Medicine
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• v.44 no.3
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• pp.47-58
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• 2023

Objectives: This study aimed to investigate the epidemiological characteristics of COVID-19 in Chungju City from July to December 2021. Methods: The authors processed and analyzed the epidemiological analysis report written by researcher. The estimated reproduction rate was analyzed using web-based software that calculates time-varying reproduction numbers. The results were analyzed through univariate multiple regression analysis, with a maximum significance level set at 0.05. Results: During the study period, a total of 1,188 patients were identified, with 7.9% of them progressing to a severe status. The maximum reproduction rate recorded was 3.48. Factors associated with the transition to a severe status included the presence of symptoms at the time of diagnosis, lack of vaccination, and belonging to the age group over 40. Conclusion: Based on the findings of this study, it can be strongly supported that the measures implemented in Chungju City, such as social distancing, vaccination, and preemptive diagnostic tests, were appropriate. Furthermore, it demonstrates that Chungju City effectively managed the impact of COVID-19. Korean Medicine Doctors made significant contributions to the epidemiological investigations of COVID-19. To comprehensively manage infectious diseases, it is crucial to provide administrative and legal support and encourage active research to expand the role of Korean Medicine Doctors in this area.