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9,674 results for “COVID-19”
Socioeconomic disparities in subway use and COVID-19 outcomes in New York City
Open the record for dataset details and reuse information.
The Situation of South Korea regarding the early stage of COVID-19 outbreak
<p>Drive-Through Screening Centers for COVID-19</p>
COVID-19 Global Recovered cases
<p>COVID-19 Global Recovered cases</p>
Data of correlation of COVID-19 cases/deaths and the Gold price
<p>The data was automatically computed by the tool https://doi.org/10.5281/zenodo.3741812 and shows the correlation between COVID-19 cases/deaths in a given time interval and the gold price in the same interval.</p> <p>The folder input_data contains the raw data that was used to produce this dataset.<br> The file TOOL_VERSION contains the version of the above reference tool that was used.</p>
dataset Covid-19
<p>Este dataset contiene datos de número de personas afectadas por el Covid-19 a nivel mundial. También incluye datos a nivel regional de los países más afectados actualmente: España, Itala y Estados Unidos</p>
Uso de medicamentos del Covid-19 en pacientes asmáticos
<p>Video explicativo del artículo del mismo nombre, en el que se trata la problemática del uso de medicamentos del Covid-19 en pacientes asmáticos. </p>
Video instructivo. Uso de fármacos del Covid-19 en pacientes asmáticos
<p>Versión audiovisual del artículo "Uso de fármacos del Covid-19 en pacientes asmáticos". </p>
Stable psychological traits predict perceived stress related to the COVID-19 outbreak
<p>This repository contains the raw dataset associated to the scientific article "Stable psychological traits predict psychological perceived stress to COVID-19 outbreak”, by L. Flesia, V. Fietta, B. Segatto, M. Monaro. Data are contained in the excel file and organized as follows:</p> <p>- the entire dataset used by the authors to perform statistical analysis</p> <p>- the training set used by the authors to train and validate ML models</p> <p>- the test set used by the authors to test the ML models</p> <p>The "Legend" file contains the description of each variable in the excel file.</p> <p>The step by step instructions to replicate the results of ML classification models, which are reported in the paper, including two .arff files containing the training and test set od data that can be directly run in WEKA software 3.9.</p> <p>The "COVID-19 QUESTIONNAIRE" file contains the English version of the questions administered to participants.</p>
COVID-19 case-fatality–derived age-adjusted risk of death
<p><strong>This is a version of the plot without split into gender-adjusted risks. For the newer version with gender-adjusted curves, please visit </strong><a href="https://zenodo.org/record/3829175">https://zenodo.org/record/3829175</a><br> doi:10.5281/zenodo.3787931</p> <p>This is a plot of the risk of death in COVID-19, which was adjusted by the age and derived from the case fatality data kindly collected by the team of data scientists and data engineers participating in the Kaggle project Data Science for COVID-19 in South Korea (DS4C), which is hosted at https://www.kaggle.com/kimjihoo/coronavirusdataset.</p> <p>The model for the plot was created with the release 2.1 of the Python & C library initially published on April 3, 2020 on GitHub at https://github.com/yuryatin/covid19_age_adjusted_mortality.</p> <p>Please, visit https://github.com/yuryatin/covid19_age_adjusted_mortality for more detailed description of the model and the source code.</p>
Not only pulmonary rehabilitation for critically ill patients with COVID-19
<p><strong>Background</strong></p> <p>Prolonged immobility in COVID-19 patients in mechanical ventilation combined with the infection-mediated harmful immune response can be responsible of peripheral nervous system complications, such as Intensive Care Unit Acquired Weakness (ICU-AW). We report preliminary findings of our monocentric prospective study.</p> <p><strong>Method</strong></p> <p>Prospective observational study of PCR-confirmed COVID-19 patients in mechanical ventilation in the Intensive Care Unit of Azienda Ospedaliero Universitaria Careggi (Firenze). We evaluated: 1. muscle power grading on motor response to nociceptive stimuli by means of MRC; 2. nerve conduction velocities, compound muscle action potentials (CMAP) and sensory nerve action potentials (SNAP) of six motor and four sensitive nerves in bilateral upper and lower limbs; 3. spontaneous muscle activity in bilateral tibialis anterior and biceps by needle. The presence of combined critical illness polyneuropathy and myopathy (CIPNM) was defined by very low amplitude of CMAP and/or SNAP on neurophysiological evaluation with normal or mildly reduced nerve conduction velocities, combined with myopathic features on needle electromyography. Data about pre-existing comorbidities such as diabetes and hypertension were also collected.</p> <p><strong>Findings</strong></p> <p>Between March 23 March 2020 to 10 April 2020, we performed ENG studies in 9 patients. Based on clinical examination ICU-AW (MRC score <48/60) was present in all 9 patients. Four of them had diagnosis of CIPNM at neurophysiological evaluation, and 3 of the remaining 5 patients presented neurophysiological findings consistent with common peroneal nerve compression</p> <p><strong>Interpretation</strong></p> <p>The present findings suggest that in critically ill patients affected by COVID-19 functional motor deficits are highly prevalent and should be searched for. ICU-AW have been found to negatively affect weaning from mechanical ventilation long-term outcome and increase in-hospital mortality of critical patients. Early recognition of these complications could help clinicians to plan an appropriate neuromotor rehabilitation (e.g. by early passive limbs mobilization and posture changes) for improving respiratory function and clinical outcome.</p> <p><strong>Funding</strong></p> <p>None</p> <p> </p>
Molecular docking COVID-19
<p>Coronavirus disease 2019 drug discovery through molecular docking</p>
Analysis of the different approaches adopted in the Italian regions to care for patients affected by COVID-19
<p>The dataset reports the daily cumulative number of patients affected by COVID-19 from 24th of February to 29th of April 2020 gathered from the following website <a href="https://github.com/pcm-dpc/COVID-19">https://github.com/pcm-dpc/COVID-19</a>. Data are produced and published by the Italian Civil Protection Department. Excel was used to build a time-series sheet. Data were subsequently fitted on the basis of a logistic function to capture the onset of the different epidemic phases in each Italian region.</p>
Data from Mehra et al. (2020-05-29), "Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis"
<p>Recent <a href="http://doi.org/dw8t">reports</a> have identified errors and implausible results in the <a href="http://doi.org/ggwzsb">title paper</a>, few of which have been corrected to date. To facilitate analysis, I provide a spreadsheet containing all data from tables in the corrected version (2020-05-29), from both the main text (html) and supporting information (PDF).</p> <p><br> The data is uploaded to this publicly-funded repository with the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7255293/">permission</a> of the copyright holder: <em>"Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories ... with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source."</em> Text and data mining are also <a href="http://web.archive.org/web/20200602210529/https://www.elsevier.com/connect/coronavirus-information-center">permitted</a>: <em>"These articles are also available to download with rights for full text and data mining, re-use and analyses for as long as needed."</em></p>
Aligning repository networks to support international sharing of COVID-19 resources and other current issues
<p>Recording of a panel "Aligning repository networks to support international sharing of COVID-19 resources", organized by COAR. This panel presented several national approaches to managing and sharing COVID-19 resources from around the world (Africa, Canada, Europe, Latin America, Asia), followed by a discussion about how we can work more closely across countries and regions to ensure that content can be integrated internationally. Participants: Kathleen Shearer - COAR, Paolo Manghi - OpenAIRE, Europe, Kazuhiro Hayashi, National Institution of Science and Technology Policy, Japan, Ansie Van de Wasthuizen, UNISA, South Africa, Cesar Olivares, CONCYTEC, Peru and LA Referencia, Latin America, Geoff Harder, Canadian Association of Research Libraries<br> <br> Organization Identifiers and Open Repositories: ROR-ing Together – Maria Gould, California Digital Library, University of California Office of the President.<br> <br> Research Data Management: a Stellenbosch University Journey – Samuel Simango, Stellenbosch University Library.<br> <br> Towards Open Data Metrics: enabling data repositories to demonstrate reuse – Kristian Garza, DataCite.</p> <p>DSpace-CRIS 7: What is Coming? – Susanna Mornati, 4Science</p>
Evolution of COVID-19 by country until 28th May
<p>This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time.</p> <p>We analyse how a type of data behave along the time in the different countries.</p> <p>In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/28).</p> <p>So we have 5 time series by country: one for each kind of data.</p> <p>The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date. </p> <p> </p> <p>We obtained this dataset scrapping <a href="http://worldometers.info/coronavirus">Worldometers</a>.</p>
HADDOCK screening against the COVID-19 RNA dependent RNA polymerase (RdRp)
<p>The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.</p> <p>One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.</p> <p>Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 RNA dependent RNA polymerase (RdRp) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein.</p> <p>This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:</p> <ul> <li><a href="https://zenodo.org/record/3929438">Mpro: Shape-based assay</a></li> <li><a href="https://zenodo.org/record/3929446">Mpro: Pharmacophore-based assay</a></li> <li><a href="https://zenodo.org/record/3929463">ACE2</a></li> </ul> <p>More information about this screen along with interactive visualisations of the top compounds can be found on our website <a href="https://bonvinlab.org/covid/">bonvinlab.org</a>.</p>
HADDOCK pharmacophore-based screening against the COVID-19 main protease (Mpro)
<p>The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.</p> <p>One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.</p> <p>Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 main protease (3CLpro) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein using a pharmacophore-based assay.</p> <p>This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:</p> <ul> <li><a href="https://zenodo.org/record/3929438">Mpro: Shape-based assay</a></li> <li><a href="https://zenodo.org/record/3929463">ACE2</a></li> <li><a href="https://zenodo.org/record/3929449">RdRp</a></li> </ul> <p>More information about this screen along with interactive visualisations of the top compounds can be found on our website <a href="https://bonvinlab.org/covid/">bonvinlab.org</a>.</p>
HADDOCK shape-based screening against the COVID-19 main protease (Mpro)
<p>The novel coronavirus (SARS-CoV-2) that has emerged from Wuhan, China in December 2019 has spread to almost all countries in the world causing a dramatic number of deaths. The current absence of antiviral treatment against the SARS-CoV-2 urges the scientific community to accelerate the drug discovery research process.</p> <p>One way to identify potential treatments and to be able to administer it swiftly is to focus on drug repurposing studies, i.e. to investigate the SARS-CoV-2 antiviral potential of drugs that have already been approved for human use.</p> <p>Proteins that are crucial for the survival and replication of the virus are the most attractive targets for such studies. Here we have focused on the SARS-CoV-2 main protease (3CLpro) that plays an essential role in the virus replication process by screening ~2000 approved drugs (and 6 experimental ones) against this particular protein using a shape-based assay.</p> <p>This is one part of a multi-target screen emphasising the main protease (Mrpo), the RNA-dependent-RNA-polymerase (RdRp) and human ACE2. The other datasets can found at the following locations:</p> <ul> <li><a href="https://zenodo.org/record/3929446">Mpro: Pharmacophore-based assay</a></li> <li><a href="https://zenodo.org/record/3929463">ACE2</a></li> <li><a href="https://zenodo.org/record/3929449">RdRp</a></li> </ul> <p>More information about this screen along with interactive visualisations of the top compounds can be found on our website <a href="https://bonvinlab.org/covid/">bonvinlab.org</a>.</p>
Hipathia version of the COVID-19 map
<p><strong>Hipathia version of the COVID-19 Disease Map provided by http://doi.org/10.17881/covid19-disease-map</strong></p>
Suitability Map of COVID-19 Virus Spread
<p><strong>This dataset is associated with the publication "G.Coro, (2020), A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate, Ecological Modelling, Volume 431, 109187, https://doi.org/10.1016/j.ecolmodel.2020.109187"</strong></p> <p> </p> <p>This image reports a Maximum Entropy model that estimates <em>suitable </em>locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.</p> <p>The model was trained just on locations in <em>Italy </em>that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:</p> <ul> <li>Average Annual Surface Air Temperature in 2018 (NASA)</li> <li>Average Annual Precipitation in 2018 (NASA)</li> <li>CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)</li> <li>Elevation (NOAA ETOPO2)</li> <li>Population per 0.5° cell (NASA Gridded Population of the World)</li> </ul> <p>A higher resolution map, the model file (in ASC format) and all parameters used are also attached.</p> <p>The model indicates highest correlation with <em>infection rate</em> for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with <em>infection rate</em>.</p> <p><strong>One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location</strong>, <strong>and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.</strong></p> <p><strong>Evaluation: </strong></p> <p>A <em>risk score</em> was calculated for each country/region reported by the JHU monitoring system (<a href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6">https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6</a>). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.</p> <p>We assessed the reliability of this score, by selecting the country/regions that reported the <em>highest rates of infection</em>. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.</p> <p>The agreement between the two maps (<a href="https://zenodo.org/api/files/23b09ea2-e5eb-415d-9f6c-fd3b5abfe6c9/covid_high_rate_vs_high_risk_24_03_2020.png">covid_high_rate_vs_high_risk.png</a>, where violet dots indicate <em>high infection rates </em>and countries' colours indicate estimated <em>high risk score</em>) is the following:</p> <p><strong>Accuracy </strong>(overall percentage of correctly predicted high-rate zones): <strong>77.25%</strong><br> <strong>Kappa </strong>(agreement between the two maps): <strong>0.46</strong> (Good, according to Fleiss' intepretation of the score) </p> <p><strong>This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.</strong></p> <p> </p>
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OpenNeuro
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