Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Sources of Infection Among Confirmed Cases of COVID-19 in Jeju Province, Korea.

ABSTRACT:

Objectives

Jeju Province in Korea reported 627 coronavirus disease 2019 (COVID-19) cases between January 20, 2020, and March 31, 2021. This study analyzed the sources of infection among confirmed cases in Jeju Province, a self-governed island.

Methods

The sources of infection were broadly categorized as follows: (1) infections from overseas (confirmed patients who reported travel overseas or contact with overseas travelers); (2) infections from outside Jeju Province (confirmed patients who had visited other provinces or had contact with individuals who had traveled to other provinces in Korea); and (3) unknown sources of infection (confirmed patients who were infected following contact with an infected person whose source of infection was unknown). The chi-square test was used to analyze the differences in the distributions of related variables for each source of infection.

Results

Of the 627 confirmed cases, 38 (6.1%) were infections from overseas sources, 199 (31.7%) were from outside of Jeju Province, and 390 (62.2%) were from unknown sources. Jeju Province had no cases with an unknown source of infection during the first and second waves of the nationwide outbreak.

Conclusions

Infections from overseas sources could be blocked from spreading to local communities in Jeju Province by conducting screening at the airport, along with the preemptive suspension of visa-free entry. In addition, considering the scale of the nationwide outbreak, measures must be established to delay outbreaks from unknown sources of infection caused by sources outside Jeju Province.

SUBMITTER: Hwang M

PROVIDER: S-EPMC8357538 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Epidemiological and Clinical Characteristics of Confirmed Cases of Severe Fever With Thrombocytopenia Syndrome in Jeju Province, Korea, 2014-2018.

Project description:OBJECTIVES:Jeju Province is well known as the region showing the highest incidence of severe fever with thrombocytopenia syndrome (SFTS) in South Korea. The aim of this study was to evaluate the epidemiological and clinical characteristics of SFTS patients in Jeju Province. METHODS:The primary data for this study were obtained from the Integrated Diseases and Health Control System of the Korea Centers for Disease Control and Prevention (KCDCIS). The selection criteria were confirmed cases of SFTS with a residence listed in Jeju Province at the time of diagnosis, reported to the KCDCIS between July 16, 2014 and November 30, 2018. RESULTS:Of 55 confirmed cases of SFTS, the case fatality rate was 10.9% (95% confidence interval [CI], 4.1 to 22.2). The most common presenting symptoms at diagnosis of severe fever, myalgia, and diarrhea had incidences of 83.6% (95% Cl, 71.2 to 92.2), 45.5% (95% Cl, 32.0 to 59.5), and 40.0% (95% CI, 27.0 to 54.1), respectively. CONCLUSIONS:Compared to SFTS patients nationwide in 2013-2015, the subjects of this study exhibited a lower case fatality rate and had a lower incidence of severe fever, myalgia, and confusion.

| S-EPMC6549013 | biostudies-literature

Association of Cardiac Infection With SARS-CoV-2 in Confirmed COVID-19 Autopsy Cases.

Project description:ImportanceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be documented in various tissues, but the frequency of cardiac involvement as well as possible consequences are unknown.ObjectiveTo evaluate the presence of SARS-CoV-2 in the myocardial tissue from autopsy cases and to document a possible cardiac response to that infection.Design, setting, and participantsThis cohort study used data from consecutive autopsy cases from Germany between April 8 and April 18, 2020. All patients had tested positive for SARS-CoV-2 in pharyngeal swab tests.ExposuresPatients who died of coronavirus disease 2019.Main outcomes and measuresIncidence of SARS-CoV-2 positivity in cardiac tissue as well as CD3+, CD45+, and CD68+ cells in the myocardium and gene expression of tumor necrosis growth factor α, interferon γ, chemokine ligand 5, as well as interleukin-6, -8, and -18.ResultsCardiac tissue from 39 consecutive autopsy cases were included. The median (interquartile range) age of patients was 85 (78-89) years, and 23 (59.0%) were women. SARS-CoV-2 could be documented in 24 of 39 patients (61.5%). Viral load above 1000 copies per μg RNA could be documented in 16 of 39 patients (41.0%). A cytokine response panel consisting of 6 proinflammatory genes was increased in those 16 patients compared with 15 patients without any SARS-CoV-2 in the heart. Comparison of 15 patients without cardiac infection with 16 patients with more than 1000 copies revealed no inflammatory cell infiltrates or differences in leukocyte numbers per high power field.Conclusions and relevanceIn this analysis of autopsy cases, viral presence within the myocardium could be documented. While a response to this infection could be reported in cases with higher virus load vs no virus infection, this was not associated with an influx of inflammatory cells. Future investigations should focus on evaluating the long-term consequences of this cardiac involvement.

| S-EPMC7385672 | biostudies-literature

Twitter conversations predict the daily confirmed COVID-19 cases.

Project description:As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83%-51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

| S-EPMC9444159 | biostudies-literature

Epidemiological characteristics of confirmed COVID-19 in Guizhou province, China.

Project description:OBJECTIVE:To explore the epidemiological characteristics of COVID-19 associated with SARS-Cov-2 in Guizhou province, and to compare the differences in epidemiology with other provinces. METHODS:The data were extracted from National Health Commission of the People's Republic of China, Health Commission of Guizhou province, and Health Commission of Hubei province from January 20 to February 12, 2020. Information included such as general demographic indicators, population data and clinical outcome. RESULTS:A total of 135 cases were analyzed in the study. The average age was 39.46±18.95 years. The ratio of males to females was 0.985:1. Most of COVID-19 patients were 18-45 years old (52.27%). Close contact history was the most common (37.88%), followed by residence history in Hubei (34.85%). There was no difference between males and females in age (P=0.953) and exposure condition (P=0.186). Correlation analysis showed that there was a significant positive correlation between the migration index and the number of confirmed cases (r=0.816, P=0.007). CONCLUSION:Among the cases, most patients were young adults. Most epidemiological characteristics were no difference between males and females. Family-based transmission should not be ignored, as a close contact history was the top reason of exposure. Moreover, population movements also had significant impact on outbreaks.

| S-EPMC7251290 | biostudies-literature

Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.

Project description:We estimate the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020. The mean interval was 3.96 days (95% CI 3.53-4.39 days), SD 4.75 days (95% CI 4.46-5.07 days); 12.6% of case reports indicated presymptomatic transmission.

| S-EPMC7258488 | biostudies-literature

Cohort survey of healthcare workers with COVID-19 infection

Project description: Not available

| S-BSST563 | biostudies-other

Investigation of SARS-CoV-2 Infection among Companion Animals in Households with Confirmed Human COVID-19 Cases.

Project description:We aimed to characterize SARS-CoV-2 infection in companion animals living in households with COVID-19-positive people and understand the dynamics surrounding how these animals become infected. Public health investigators contacted households with at least one confirmed, symptomatic person with COVID-19 for study recruitment. Blood, nasal, and rectal swab specimens were collected from pet dogs and cats and a questionnaire was completed. Specimens were tested for SARS-CoV-2 by RT-PCR, and for neutralizing antibodies; genomic sequencing was performed on viral-positive samples. A total of 36.4% of 110 pets enrolled had evidence of infection with SARS-CoV-2. Pets were more likely to test positive if the pet was immunocompromised, and if more than one person in the home was positive for COVID-19. Among 12 multi-pet households where at least one pet was positive, 10 had at least one other pet test positive. Whole-genome sequencing revealed the genomes of viral lineages circulating in the community during the time of sample collection. Our findings suggest a high likelihood of viral transmission in households with multiple pets and when pets had very close interactions with symptomatic humans. Further surveillance studies are needed to characterize how new variants impact animals and to understand opportunities for infection and spillover in susceptible species.

| S-EPMC11206992 | biostudies-literature

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil.

Project description:The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays. Developing efficient short-term forecasting models allow forecasting the number of future cases. In this context, it is possible to develop strategic planning in the public health system to avoid deaths. In this paper, autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking-ensemble learning are evaluated in the task of time series forecasting with one, three, and six-days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence. In the stacking-ensemble learning approach, the CUBIST regression, RF, RIDGE, and SVR models are adopted as base-learners and Gaussian process (GP) as meta-learner. The models' effectiveness is evaluated based on the improvement index, mean absolute error, and symmetric mean absolute percentage error criteria. In most of the cases, the SVR and stacking-ensemble learning reach a better performance regarding adopted criteria than compared models. In general, the developed models can generate accurate forecasting, achieving errors in a range of 0.87%-3.51%, 1.02%-5.63%, and 0.95%-6.90% in one, three, and six-days-ahead, respectively. The ranking of models, from the best to the worst regarding accuracy, in all scenarios is SVR, stacking-ensemble learning, ARIMA, CUBIST, RIDGE, and RF models. The use of evaluated models is recommended to forecasting and monitor the ongoing growth of COVID-19 cases, once these models can assist the managers in the decision-making support systems.

| S-EPMC7252162 | biostudies-literature

Power-law distribution in the number of confirmed COVID-19 cases

Project description:COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.

| S-EPMC7519452 | biostudies-literature

The serial interval of COVID-19 from publicly reported confirmed cases.

Project description:We estimate the distribution of serial intervals for 468 confirmed cases of COVID-19 reported in 93 Chinese cities by February 8, 2020. The mean and standard deviation are 3.96 (95% CI 3.53-4.39) and 4.75 (95% CI 4.46-5.07) days, respectively, with 12.6% of reports indicating pre-symptomatic transmission.

| S-EPMC7239082 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data