Open access epidemiological data from the COVID-19 outbreak.
Ontology highlight
ABSTRACT:
SUBMITTER: Xu B
PROVIDER: S-EPMC7158984 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
Open access epidemiological data from the COVID-19 outbreak.
Xu Bo B Kraemer Moritz U G MUG
The Lancet. Infectious diseases 20200219 5
Similar Datasets
Project description:Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.
| S-EPMC7093412 | biostudies-literature
Project description:There is an obvious concern globally regarding the fact about the emerging coronavirus 2019 novel coronavirus (2019-nCoV) as a worldwide public health threat. As the outbreak of COVID-19 causes by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progresses within China and beyond, rapidly available epidemiological data are needed to guide strategies for situational awareness and intervention. The recent outbreak of pneumonia in Wuhan, China, caused by the SARS-CoV-2 emphasizes the importance of analyzing the epidemiological data of this novel virus and predicting their risks of infecting people all around the globe. In this study, we present an effort to compile and analyze epidemiological outbreak information on COVID-19 based on the several open datasets on 2019-nCoV provided by the Johns Hopkins University, World Health Organization, Chinese Center for Disease Control and Prevention, National Health Commission, and DXY. An exploratory data analysis with visualizations has been made to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China. Overall, at the outset of an outbreak like this, it is highly important to readily provide information to begin the evaluation necessary to understand the risks and begin containment activities.
| S-EPMC7228278 | biostudies-literature
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research.
| S-EPMC8218199 | biostudies-literature
Project description:ObjectiveIndia reported its first coronavirus disease 2019 (COVID-19) case in the state of Kerala and an outbreak initiated subsequently. The Department of Health Services, Government of Kerala, initially released daily updates through daily textual bulletins for public awareness to control the spread of the disease. However, these unstructured data limit upstream applications, such as visualization, and analysis, thus demanding refinement to generate open and reusable datasets.Materials and methodsThrough a citizen science initiative, we leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins and media outlets to generate reusable datasets. This was further visualized as a dashboard through a front-end Web application and a JSON (JavaScript Object Notation) repository, which serves as an application programming interface for the front end.ResultsFrom the sourced data, we provided real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for nonspecialists. To ensure longevity and reusability, the dataset was deposited in an open-access public repository for future analysis. Finally, we provide outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 138 days of the outbreak.DiscussionWe anticipate that our dataset can form the basis for future studies, supplemented with clinical and epidemiological data from the individuals affected with COVID-19 in Kerala.ConclusionsWe reported a citizen science initiative on the COVID-19 outbreak in Kerala to collect and deposit data in a structured format, which was utilized for visualizing the outbreak trend and describing demographic characteristics of affected individuals.
| S-EPMC7454688 | biostudies-literature
Project description:Control of the novel COronaVIrus Disease-2019 (COVID-19) in a hospital setting is a priority. A COVID-19-infected surgeon performed surgical activities before being tested. An exposure risk classification was applied to the identified exposed subjects and high- and medium-risk contacts underwent active symptom monitoring for 14 days at home. All healthcare professionals (HCPs) were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) at the end of the quarantine and serological tests were performed. Three household contacts and 20 HCPs were identified as high- or medium-risk contacts and underwent a 14-day quarantine. Fourteen HCPs and 19 patients were instead classified as low risk. All the contacts remained asymptomatic and all HCPs tested negative for SARS-CoV-2. About 25-28 days after their last exposure, HCPs underwent serological testing and two of them had positive IgM but negative confirmatory swabs. In a low COVID-19 burden area, the in-hospital transmission of SARS-CoV-2 from an infectious doctor did not occur and, despite multiple and frequent contacts, a hospital outbreak was avoided. This may be linked to the adoption of specific recommendations and to the use of standard personal protective equipment by HCPs.
| S-EPMC8042382 | biostudies-literature
Project description:BackgroundIn March 2020, an outbreak of coronavirus 19 (COVID-19) was detected in the North of Jordan. This retrospective study is the first from Jordan to report the epidemiologic, clinical, laboratory, and radiologic characteristics of COVID-19 infected patients.MethodsAll patients with laboratory-confirmed COVID-19 infection by RT-PCR in the North of Jordan admitted between March 15 and April 2, 2020 were included. The clinical features, radiological, and laboratory findings were reviewed.ResultsOf 81 patients affected, 79 (97.5%) shared a common exposure to four recent travelers from endemic areas. The mean age was 40 years. Although about half (44 [54.3%]) were females, symptomatic patients were mostly females (75%). The most common presenting symptoms were nasal congestion, sore throat and dry cough. Less than one-third (31%) had chronic diseases. Although 84% of patients reported receiving Bacille Calmette-Guérin (BCG) vaccination, more asymptomatic patients had BCG than symptomatic (p = 0.017). Almost all patients (97.5%) had an elevated D-dimer level. Erythrocyte sedimentation rate (ESR) and c-reactive protein were elevated in 50% and 42.7% of patients, respectively. High ESR found to be the predictor of abnormal chest radiograph observed in 13 (16%) patients with OR of 14.26 (95% CI 1.37-147.97, p = 0.026).ConclusionsAn outbreak of COVID-19 infection in northern Jordan affected more females and relatively young individuals and caused mainly mild illnesses. The strict outbreak response measures applied at early stages probably contributed to the lenient nature of this outbreak, but the contribution of other factors to such variability in COVID-19 presentation is yet to be explained.
| S-EPMC7367794 | biostudies-literature
Project description:Latvian SARS-CoV-2 isolate genetic diveristy
| PRJEB40188 | ENA
Project description:ObjectivesIn 2019 Chinese authorities alerted of the appearance of a cluster of cases of unknown pneumonia related to a new type of coronavirus. Spain is among the most affected countries. Our aim is to describe the cases of COVID-19 at Infanta Sofía University Hospital (Madrid), a public secondary hospital that increased its hospital beds to provide assistance during the outbreak.MethodsRetrospective descriptive study of cases that met COVID-19 clinical diagnosis criteria or had a positive PCR test from February 27 to June 29, 2020. A description of demographic variables, hospital stay, mortality and the epidemiological curve was performed.ResultsOf 1,828 confirmed cases, 64.4% were hospitalised, 5.6% were admitted to the ICU. About 52.2% were male. The median age was 63.2 years. About 13.1% were nursing home residents. Nineteen percent were of Latin American origin of which 6.8% were admitted to the ICU. Overall case fatality was 14.6%. We observed a biphasic epidemiological curve.ConclusionsSixty to 79-year-old males were admitted and deceased more often than women. Mortality reached 14.7%. Latin Americans were admitted more often to the ICU. Further studies about epidemiological characteristics of COVID-19 in hospitals are necessary.
| S-EPMC7834034 | biostudies-literature
Project description:Epidemiological models can provide the dynamic evolution of a pandemic but they are based on many assumptions and parameters that have to be adjusted over the time the pandemic lasts. However, often the available data are not sufficient to identify the model parameters and hence infer the unobserved dynamics. Here, we develop a general framework for building a trustworthy data-driven epidemiological model, consisting of a workflow that integrates data acquisition and event timeline, model development, identifiability analysis, sensitivity analysis, model calibration, model robustness analysis, and projection with uncertainties in different scenarios. In particular, we apply this framework to propose a modified susceptible-exposed-infectious-recovered (SEIR) model, including new compartments and model vaccination in order to project the transmission dynamics of COVID-19 in New York City (NYC). We find that we can uniquely estimate the model parameters and accurately project the daily new infection cases, hospitalizations, and deaths, in agreement with the available data from NYC's government's website. In addition, we employ the calibrated data-driven model to study the effects of vaccination and timing of reopening indoor dining in NYC.
| S-EPMC8452065 | biostudies-literature