Following reports of patients with unexplained pneumonia at the end of December 2019 in Wuhan, China, the causative agent was identified as coronavirus (SARS-CoV-2), and the 2019 novel coronavirus disease was named COVID-19 by the World Health Organization. Putative patients with COVID-19 have been identified in South Korea, and attempts have been made to isolate the pathogen from these patients.

Upper and lower respiratory tract secretion samples from putative patients with COVID-19 were inoculated onto cells to isolate the virus. Full genome sequencing and electron microscopy were used to identify the virus.

The virus replicated in Vero cells and cytopathic effects were observed. Full genome sequencing showed that the virus genome exhibited sequence homology of more than 99.9% with SARS-CoV-2 which was isolated from patients from other countries, for instance China. Sequence homology of SARS-CoV-2 with SARS-CoV, and MERS-CoV was 77.5% and 50%, respectively. Coronavirus-specific morphology was observed by electron microscopy in virus-infected Vero cells.

SARS-CoV-2 was isolated from putative patients with unexplained pneumonia and intermittent coughing and fever. The isolated virus was named BetaCoV/Korea/KCDC03/2020.

Case fatality rates (CFR) and recovery rates are important readouts during epidemics and pandemics. In this article, an international analysis was performed on the ongoing coronavirus disease 2019 (COVID-19) pandemic.

Data were retrieved from accurate databases according to the user’s guide of data sources for patient registries, CFR and recovery rates were calculated for each country. A comparison of CFR between countries with total cases ≥ 1,000 was observed for 12^th and 23^rd March.

Italy’s CFR was the highest of all countries studied for both time points (12^th March, 6.22% versus 23^rd March, 9.26%). The data showed that even though Italy was the only European country reported on 12^rd March, Spain and France had the highest CFR of 6.16 and 4.21%, respectively, on 23^rd March, which was strikingly higher than the overall CFR of 3.61%.

Obtaining detailed and accurate medical history from COVID-19 patients, and analyzing CFR alongside the recovery rate, may enable the identification of the highest risk areas so that efficient medical care may be provided. This may lead to the development of point-of-care tools to help clinicians in stratifying patients based on possible requirements in the level of care, to increase the probabilities of survival from COVID-19 disease.

The first confirmed case of coronavirus disease 2019 (COVID-19) in South Korea was reported in January 2020, with 28 confirmed cases reported as of February 14^th, 2020. The epidemiological and clinical characteristics of all 28 cases were analyzed in response to this disease.

The epidemiological characteristics and early clinical features of the 28 patients from Korea with confirmed COVID-19 were analyzed using COVID-19 reporting and surveillance data and the epidemiological investigation reports prepared by the rapid response team.

There were 16 patients that entered Korea from foreign countries: Wuhan, China (11 patients), Zhuhai, China, (1 patient), Singapore (2 patients), Japan (1 patient), and Thailand (1 patient). The early symptoms were fever, sore throat, cough or sputum production, chills, and muscle ache. Three patients were asymptomatic, however, 18 developed pneumonia. Of the 28 cases, 16 were index cases imported from abroad, with 10 cases of secondary infection originating in Korea, and the route of transmission still under investigation for 2 patients. The 10 patients with secondary infection were infected from contact with family members or acquaintances of primary patients, and the suspected sites of transmission were mostly at home.

COVID-19 in Korea was spread by 16 infected individuals traveling from other countries, leading to second-generation cases. The initial symptoms were mostly minor, but the disease was infectious at this stage, resulting from close contact, particularly at home. Establishing an early detection strategy for COVID-19 is crucial for managing the transmission of the disease.

Coronavirus Disease-19 (COVID-19) is a respiratory infection characterized by the main symptoms of pneumonia and fever. It is caused by the novel coronavirus severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), which is known to spread via respiratory droplets. We aimed to determine the rate and likelihood of SARS-CoV-2 transmission from COVID-19 patients through non-respiratory routes.

Serum, urine, and stool samples were collected from 74 hospitalized patients diagnosed with COVID-19 based on the detection of SARS-CoV-2 in respiratory samples. The SARS-CoV-2 RNA genome was extracted from each specimen and real-time reverse transcription polymerase chain reaction performed. CaCo-2 cells were inoculated with the specimens containing the SARS-COV-2 genome, and subcultured for virus isolation. After culturing, viral replication in the cell supernatant was assessed.

Of the samples collected from 74 COVID-19 patients, SARS-CoV-2 was detected in 15 serum, urine, or stool samples. The virus detection rate in the serum, urine, and stool samples were 2.8% (9/323), 0.8% (2/247), and 10.1% (13/129), and the mean viral load was 1,210 ± 1,861, 79 ± 30, and 3,176 ± 7,208 copy/µL, respectively. However, the SARS-CoV-2 was not isolated by the culture method from the samples that tested positive for the SARS-CoV-2 gene.

While the virus remained detectable in the respiratory samples of COVID-19 patients for several days after hospitalization, its detection in the serum, urine, and stool samples was intermittent. Since the virus could not be isolated from the SARS-COV-2-positive samples, the risk of viral transmission via stool and urine is expected to be low.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, in December 2019 and has been rapidly spreading worldwide. Although the causal relationship among mutations and the features of SARS-CoV-2 such as rapid transmission, pathogenicity, and tropism, remains unclear, our results of genomic mutations in SARS-CoV-2 may help to interpret the interaction between genomic characterization in SARS-CoV-2 and infectivity with the host.

A total of 4,254 genomic sequences of SARS-CoV-2 were collected from the Global Initiative on Sharing all Influenza Data (GISAID). Multiple sequence alignment for phylogenetic analysis and comparative genomic approach for mutation analysis were conducted using Molecular Evolutionary Genetics Analysis (MEGA), and an in-house program based on Perl language, respectively.

Phylogenetic analysis of SARS-CoV-2 strains indicated that there were 3 major clades including S, V, and G, and 2 subclades (G.1 and G.2). There were 767 types of synonymous and 1,352 types of non-synonymous mutation. ORF1a, ORF1b, S, and N genes were detected at high frequency, whereas ORF7b and E genes exhibited low frequency. In the receptor-binding domain (RBD) of the S gene, 11 non-synonymous mutations were observed in the region adjacent to the angiotensin converting enzyme 2 (ACE2) binding site.

It has been reported that the rapid infectivity and transmission of SARS-CoV-2 associated with host receptor affinity are derived from several mutations in its genes. Without these genetic mutations to enhance evolutionary adaptation, species recognition, host receptor affinity, and pathogenicity, it would not survive. It is expected that our results could provide an important clue in understanding the genomic characteristics of SARS-CoV-2.

Health-related quality of life (HRQoL) is one of the most important outcome measures for patients. The purpose of this study was to evaluate HRQoL and related factors in Coronavirus disease 2019 (COVID-19) patients.

A total of 420 COVID-19 patients who had been discharged from hospital were selected using a systematic sampling. The EuroQol 5-dimensional-5 levels (EQ-5D-5L) questionnaire along with medical records of the patients were used to gather the data. The t test and analysis of variance were employed to test the difference between mean EQ-5D-5L scores, and the BetaMix model was used to investigate factors associated with EQ-5D-5L scores.

The mean score for the patients who completed the EQ-5D-5L questionnaire (n = 409) was 0.6125. The EQ-5D-5L scores were significantly higher in males, patients with younger age, those with a low level of education, the employed, patients who worked in uncrowded workplaces, patients without diabetes, and those who were not admitted to intensive care unit. The BetaMix model showed that gender, age, education, employment status, having diabetes, heart failure, and admission to the intensive care unit were significant independent predictors of the EQ-5D-5L index values.

The mean score for EQ-5D-5L in COVID-19 patients was low in this study. Some of the factors, especially aging and having diabetes, should be considered in the aftercare of patients to improve their HRQoL.

This study aims to evaluate the risk assessments of coronavirus 2019 (COVID-19) in the Korea Centers for Disease Control and Prevention (KCDC), from the point of detection to the provision of basic information to the relevant public health authorities.

To estimate the overall risk of specific public health events, probability, and impact at the country-level were evaluated using available information. To determine the probability of particular public health events, the risk of importation and risk of transmission were taken into consideration. KCDC used 5 levels (“very low,” “low,” “moderate,” “high,” and “very high”) for each category and overall risk was eventually decided.

A total of 8 risk assessments were performed on 8 separate occasions between January 8^th to February 28^th, 2020, depending on the detection and report of COVID-19 cases in other countries. The overall risk of the situation in each assessment increased in severity over this period: “low” (first), “moderate” (second), “high” (third), “high” (fourth), “high” (fifth), “high” (sixth), “high” (seventh), and “very high” (eighth).

The KCDC’s 8 risk assessments were utilized to activate national emergency response mechanisms and eventually prepare for the pandemic to ensure the containment and mitigation of COVID-19 with non-pharmaceutical public health measures.

In this pandemic situation caused by a novel coronavirus disease in 2019 (COVID-19), an electronic support system that can rapidly and accurately perform epidemic investigations, is needed. It would systematically secure and analyze patients’ data (who have been confirmed to have the infection), location information, and credit card usage.

The “Infectious Disease Prevention and Control Act” in South Korea, established a legal basis for the securement, handling procedure, and disclosure of information required for epidemic investigations. The Epidemic Investigation Support System (EISS) was developed as an application platform on the Smart City data platform.

The EISS performed the function of inter-institutional communication which reduced the processing period of patients’ data in comparison to other methods. This system automatically marked confirmed cases’ tracking data on a map and hot-spot analysis which lead to the prediction of areas where people may be vulnerable to infection.

The EISS was designed and implemented for use during an epidemic investigation to prevent the spread of an infectious disease, by specifically tracking confirmed cases of infection.