37 datasets found
  1. g

    Coronavirus (Covid-19) Data in the United States

    • github.com
    • openicpsr.org
    • +4more
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data
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    csvAvailable download formats
    Dataset provided by
    New York Times
    License

    https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

    Description

    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

    Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

    We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

    The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

  2. COVID-19 Daily Data Tracker - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Sep 9, 2025
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    ckan.publishing.service.gov.uk (2025). COVID-19 Daily Data Tracker - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/covid-19-daily-data-tracker
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    Dataset updated
    Sep 9, 2025
    Dataset provided by
    CKANhttps://ckan.org/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    This dataset contains daily data trackers for the COVID-19 pandemic, aggregated by month and starting 18.3.20. The first release of COVID-19 data on this platform was on 1.6.20. Updates have been provided on a quarterly basis throughout 2023/24. No updates are currently scheduled for 2024/25 as case rates remain low. The data is accurate as at 8.00 a.m. on 8.4.24. Some narrative for the data covering the latest period is provided here below: Diagnosed cases / episodes • As at 3.4.24 CYC residents have had a total 75,556 covid episodes since the start of the pandemic, a rate of 37,465 per 100,000 of population (using 2021 Mid-Year Population estimates). The cumulative rate in York is similar to the national (37,305) and regional (37,059) averages. • The latest rate of new Covid cases per 100,000 of population for the period 28.3.24 to 3.4.24 in York was 1.49 (3 cases). The national and regional averages at this date were 1.67 and 2.19 respectively (using data published on Gov.uk on 5.4.24).

  3. New York State Statewide COVID-19 Fatalities by Age Group - 7cdh-f4d3 -...

    • healthdata.gov
    csv, xlsx, xml
    Updated Oct 7, 2023
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    (2023). New York State Statewide COVID-19 Fatalities by Age Group - 7cdh-f4d3 - Archive Repository [Dataset]. https://healthdata.gov/dataset/New-York-State-Statewide-COVID-19-Fatalities-by-Ag/t2tt-2xgk
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    xml, xlsx, csvAvailable download formats
    Dataset updated
    Oct 7, 2023
    Area covered
    New York
    Description

    This dataset tracks the updates made on the dataset "New York State Statewide COVID-19 Fatalities by Age Group" as a repository for previous versions of the data and metadata.

  4. COVID-19 death rates in the United States as of March 10, 2023, by state

    • statista.com
    Updated May 15, 2024
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    Statista (2024). COVID-19 death rates in the United States as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1109011/coronavirus-covid19-death-rates-us-by-state/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

  5. Number of coronavirus (COVID-19) deaths in New York as of Dec. 16, 2022, by...

    • statista.com
    Updated Sep 15, 2020
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    Statista (2020). Number of coronavirus (COVID-19) deaths in New York as of Dec. 16, 2022, by county [Dataset]. https://www.statista.com/statistics/1109403/coronavirus-covid19-death-number-new-york-by-county/
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    Dataset updated
    Sep 15, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    New York
    Description

    There have been almost 60 thousand COVID-19 deaths in New York State as of December 16, 2022. A majority of those deaths have been recorded in New York City: Staten Island, Queens, Brooklyn, Bronx, and Manhattan.

    Pandemic takes hold in U.S. Across the United States, over one million COVID-19 deaths had been confirmed by the middle of December 2022. New York has been hit particularly hard throughout the pandemic and is among the states with the highest number of deaths from the coronavirus. The neighboring state of New Jersey was also at the heart of the initial outbreak in March 2020, and the two states continue to have some of the highest death rates from the coronavirus in the United States.

    Deaths in New York City The number of new daily deaths from COVID-19 in New York City peaked early in the pandemic. Since then there have been waves in which the number of daily deaths rose, but they have not gotten close to the levels seen early in the pandemic. The impact of the coronavirus has been thoroughly analyzed, and the fatality rates by age in New York City support the evidence that the risk of developing more severe COVID-19 symptoms increases with age.

  6. N

    Confirmed COVID-19 Case and Hospitalization Counts

    • data.cityofnewyork.us
    csv, xlsx, xml
    Updated Dec 1, 2025
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    Department of Health and Mental Hygiene (DOHMH) (2025). Confirmed COVID-19 Case and Hospitalization Counts [Dataset]. https://data.cityofnewyork.us/Health/Confirmed-COVID-19-Case-and-Hospitalization-Counts/3w37-3kr9
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    xml, csv, xlsxAvailable download formats
    Dataset updated
    Dec 1, 2025
    Authors
    Department of Health and Mental Hygiene (DOHMH)
    Description

    Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients.

    Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/case-hosp-death.csv on a daily basis.

  7. Table1_Diagnostics and treatments of COVID-19: two-year update to a living...

    • frontiersin.figshare.com
    docx
    Updated Nov 16, 2023
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    Jamie Elvidge; Gareth Hopkin; Nithin Narayanan; David Nicholls; Dalia Dawoud (2023). Table1_Diagnostics and treatments of COVID-19: two-year update to a living systematic review of economic evaluations.docx [Dataset]. http://doi.org/10.3389/fphar.2023.1291164.s002
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    docxAvailable download formats
    Dataset updated
    Nov 16, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Jamie Elvidge; Gareth Hopkin; Nithin Narayanan; David Nicholls; Dalia Dawoud
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Objectives: As the initial crisis of the COVID-19 pandemic recedes, healthcare decision makers are likely to want to make rational evidence-guided choices between the many interventions now available. We sought to update a systematic review to provide an up-to-date summary of the cost-effectiveness evidence regarding tests for SARS-CoV-2 and treatments for COVID-19.Methods: Key databases, including MEDLINE, EconLit and Embase, were searched on 3 July 2023, 2 years on from the first iteration of this review in July 2021. We also examined health technology assessment (HTA) reports and the citations of included studies and reviews. Peer-reviewed studies reporting full health economic evaluations of tests or treatments in English were included. Studies were quality assessed using an established checklist, and those with very serious limitations were excluded. Data from included studies were extracted into predefined tables.Results: The database search identified 8,287 unique records, of which 54 full texts were reviewed, 28 proceeded for quality assessment, and 15 were included. Three further studies were included through HTA sources and citation checking. Of the 18 studies ultimately included, 17 evaluated treatments including corticosteroids, antivirals and immunotherapies. In most studies, the comparator was standard care. Two studies in lower-income settings evaluated the cost effectiveness of rapid antigen tests and critical care provision. There were 17 modelling analyses and 1 trial-based evaluation.Conclusion: A large number of economic evaluations of interventions for COVID-19 have been published since July 2021. Their findings can help decision makers to prioritise between competing interventions, such as the repurposed antivirals and immunotherapies now available to treat COVID-19. However, some evidence gaps remain present, including head-to-head analyses, disease-specific utility values, and consideration of different disease variants.Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021272219], identifier [PROSPERO 2021 CRD42021272219].

  8. New York Forward COVID-19 Daily Hospitalization Summary by Region (Archived)...

    • health.data.ny.gov
    • healthdata.gov
    • +1more
    csv, xlsx, xml
    Updated Oct 6, 2023
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    New York State Department of Health (2023). New York Forward COVID-19 Daily Hospitalization Summary by Region (Archived) [Dataset]. https://health.data.ny.gov/Health/New-York-Forward-COVID-19-Daily-Hospitalization-Su/qutr-irdf
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    xlsx, xml, csvAvailable download formats
    Dataset updated
    Oct 6, 2023
    Dataset authored and provided by
    New York State Department of Health
    Area covered
    New York
    Description

    Note: This dataset was archived on 10/6/23. Statewide hospitalization data is available in the New York State Statewide COVID-19 Hospitalizations and Beds dataset.

    This dataset includes the number of patients hospitalized, and number of patients in the intensive care unit (ICU) among patients with lab-confirmed COVID-19 disease by hospital region and reporting date. The primary goal of publishing this dataset is to provide users with timely information about hospitalizations among patients with lab-confirmed COVID-19 disease.

    The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals are required to complete this survey daily and data reflects the number of patients hospitalized and number of patients in the ICU reported by hospitals through the survey each day. These data include NYS resident and non-NYS resident hospitalizations. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in mid-March 2020.

    To calculate regional totals, the number of patients hospitalized and number of patients in the ICU are each summed by hospital region and reporting date.

    The information in this dataset is updated daily on NY Forward; New York State’s resource for COVID-19 testing, early warning monitoring, and regional daily hospitalization dashboards. More information can be found at forward.ny.gov.

  9. f

    Data_Sheet_1_High-income ZIP codes in New York City demonstrate higher case...

    • frontiersin.figshare.com
    txt
    Updated Jun 20, 2024
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    Steven T. L. Tung; Mosammat M. Perveen; Kirsten N. Wohlars; Robert A. Promisloff; Mary F. Lee-Wong; Anthony M. Szema (2024). Data_Sheet_1_High-income ZIP codes in New York City demonstrate higher case rates during off-peak COVID-19 waves.CSV [Dataset]. http://doi.org/10.3389/fpubh.2024.1384156.s001
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    txtAvailable download formats
    Dataset updated
    Jun 20, 2024
    Dataset provided by
    Frontiers
    Authors
    Steven T. L. Tung; Mosammat M. Perveen; Kirsten N. Wohlars; Robert A. Promisloff; Mary F. Lee-Wong; Anthony M. Szema
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New York
    Description

    IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

  10. Data_Sheet_1_T-Cell Subsets and Interleukin-10 Levels Are Predictors of...

    • frontiersin.figshare.com
    pdf
    Updated Jun 15, 2023
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    Amal F. Alshammary; Jawaher M. Alsughayyir; Khalid K. Alharbi; Abdulrahman M. Al-Sulaiman; Haifa F. Alshammary; Heba F. Alshammary (2023). Data_Sheet_1_T-Cell Subsets and Interleukin-10 Levels Are Predictors of Severity and Mortality in COVID-19: A Systematic Review and Meta-Analysis.pdf [Dataset]. http://doi.org/10.3389/fmed.2022.852749.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Amal F. Alshammary; Jawaher M. Alsughayyir; Khalid K. Alharbi; Abdulrahman M. Al-Sulaiman; Haifa F. Alshammary; Heba F. Alshammary
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundMany COVID-19 patients reveal a marked decrease in their lymphocyte counts, a condition that translates clinically into immunodepression and is common among these patients. Outcomes for infected patients vary depending on their lymphocytopenia status, especially their T-cell counts. Patients are more likely to recover when lymphocytopenia is resolved. When lymphocytopenia persists, severe complications can develop and often lead to death. Similarly, IL-10 concentration is elevated in severe COVID-19 cases and may be associated with the depression observed in T-cell counts. Accordingly, this systematic review and meta-analysis aims to analyze T-cell subsets and IL-10 levels among COVID-19 patients. Understanding the underlying mechanisms of the immunodepression observed in COVID-19, and its consequences, may enable early identification of disease severity and reduction of overall morbidity and mortality.MethodsA systematic search was conducted covering PubMed MEDLINE, Scopus, Web of Science, and EBSCO databases for journal articles published from December 1, 2019 to March 14, 2021. In addition, we reviewed bibliographies of relevant reviews and the medRxiv preprint server for eligible studies. Our search covered published studies reporting laboratory parameters for T-cell subsets (CD4/CD8) and IL-10 among confirmed COVID-19 patients. Six authors carried out the process of data screening, extraction, and quality assessment independently. The DerSimonian-Laird random-effect model was performed for this meta-analysis, and the standardized mean difference (SMD) and 95% confidence interval (CI) were calculated for each parameter.ResultsA total of 52 studies from 11 countries across 3 continents were included in this study. Compared with mild and survivor COVID-19 cases, severe and non-survivor cases had lower counts of CD4/CD8 T-cells and higher levels of IL-10.ConclusionOur findings reveal that the level of CD4/CD8 T-cells and IL-10 are reliable predictors of severity and mortality in COVID-19 patients. The study protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO); registration number CRD42020218918.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218918, identifier: CRD42020218918.

  11. f

    Data_Sheet_2_Liver injury associated with the severity of COVID-19: A...

    • frontiersin.figshare.com
    pdf
    Updated Jun 6, 2023
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    Ruiqi Yang; Jihua Feng; Huan Wan; Xiaona Zeng; Pan Ji; Jianfeng Zhang (2023). Data_Sheet_2_Liver injury associated with the severity of COVID-19: A meta-analysis.pdf [Dataset]. http://doi.org/10.3389/fpubh.2023.1003352.s002
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    pdfAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    Frontiers
    Authors
    Ruiqi Yang; Jihua Feng; Huan Wan; Xiaona Zeng; Pan Ji; Jianfeng Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundThe current 2019 novel coronavirus disease (COVID-19) pandemic is a major threat to global health. It is currently uncertain whether and how liver injury affects the severity of COVID-19. Therefore, we conducted a meta-analysis to determine the association between liver injury and the severity of COVID-19.MethodsA systematic search of the PubMed, Embase, and Cochrane Library databases from inception to August 12, 2022, was performed to analyse the reported liver chemistry data for patients diagnosed with COVID-19. The pooled odds ratio (OR), weighted mean difference (WMD) and 95% confidence interval (95% CI) were assessed using a random-effects model. Furthermore, publication bias and sensitivity were analyzed.ResultsForty-six studies with 28,663 patients were included. The pooled WMDs of alanine aminotransferase (WMD = 12.87 U/L, 95% CI: 10.52–15.23, I2 = 99.2%), aspartate aminotransferase (WMD = 13.98 U/L, 95% CI: 12.13–15.83, I2 = 98.2%), gamma-glutamyl transpeptidase (WMD = 20.67 U/L, 95% CI: 14.24–27.10, I2 = 98.8%), total bilirubin (WMD = 2.98 μmol/L, 95% CI: 1.98–3.99, I2 = 99.4%), and prothrombin time (WMD = 0.84 s, 95% CI: 0.46–1.23, I2 = 99.4%) were significantly higher and that of albumin was lower (WMD = −4.52 g/L, 95% CI: −6.28 to −2.75, I2 = 99.9%) in severe cases. Moreover, the pooled OR of mortality was higher in patients with liver injury (OR = 2.72, 95% CI: 1.18–6.27, I2 = 71.6%).ConclusionsHepatocellular injury, liver metabolic, and synthetic function abnormality were observed in severe COVID-19. From a clinical perspective, liver injury has potential as a prognostic biomarker for screening severely affected patients at early disease stages.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, Identifier: CRD42022325206.

  12. New York State Statewide COVID-19 Vaccination Data by County (Archived,...

    • health.data.ny.gov
    • healthdata.gov
    • +1more
    csv, xlsx, xml
    Updated Nov 3, 2023
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    New York State Department of Health (2023). New York State Statewide COVID-19 Vaccination Data by County (Archived, Initial) [Dataset]. https://health.data.ny.gov/Health/New-York-State-Statewide-COVID-19-Vaccination-Data/duk7-xrni
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    csv, xml, xlsxAvailable download formats
    Dataset updated
    Nov 3, 2023
    Dataset authored and provided by
    New York State Department of Health
    Area covered
    New York
    Description

    Note: As of November 10, 2023, this dataset has been archived. For the current version of this data, please visit: https://health.data.ny.gov/d/gikn-znjh

    This dataset reports daily on the number of people vaccinated by New York providers with at least one dose and with a complete COVID-19 vaccination series overall since December 14, 2020. New York providers include hospitals, mass vaccination sites operated by the State or local governments, pharmacies, and other providers registered with the State to serve as points of distribution.

    This dataset is created by the New York State Department of Health from data reported to the New York State Immunization Information System (NYSIIS) and the New York City Citywide Immunization Registry (NYC CIR). County-level vaccination data is based on data reported to NYSIIS and NYC CIR by the providers administering vaccines. Residency is self-reported by the individual being vaccinated. This data does not include vaccine administered through Federal entities or performed outside of New York State to New York residents. NYSIIS and CIR data is used for county-level statistics. New York State Department of Health requires all New York State vaccination providers to report all COVID-19 vaccination administration data to NYSIIS and NYC CIR within 24 hours of administration.

  13. f

    Data_Sheet_1_The Predictive Value of Myoglobin for COVID-19-Related Adverse...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Nov 18, 2021
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    Xu, Qiang; Hou, Pan; Tu, Dingyuan; Zhao, Xianxian; Wu, Hong; Ma, Chaoqun; Bai, Yuan; Li, Pan; Guo, Zhifu; Gu, Jiawei (2021). Data_Sheet_1_The Predictive Value of Myoglobin for COVID-19-Related Adverse Outcomes: A Systematic Review and Meta-Analysis.PDF [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000868469
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    Dataset updated
    Nov 18, 2021
    Authors
    Xu, Qiang; Hou, Pan; Tu, Dingyuan; Zhao, Xianxian; Wu, Hong; Ma, Chaoqun; Bai, Yuan; Li, Pan; Guo, Zhifu; Gu, Jiawei
    Description

    Objective: Cardiac injury is detected in numerous patients with coronavirus disease 2019 (COVID-19) and has been demonstrated to be closely related to poor outcomes. However, an optimal cardiac biomarker for predicting COVID-19 prognosis has not been identified.Methods: The PubMed, Web of Science, and Embase databases were searched for published articles between December 1, 2019 and September 8, 2021. Eligible studies that examined the anomalies of different cardiac biomarkers in patients with COVID-19 were included. The prevalence and odds ratios (ORs) were extracted. Summary estimates and the corresponding 95% confidence intervals (95% CIs) were obtained through meta-analyses.Results: A total of 63 studies, with 64,319 patients with COVID-19, were enrolled in this meta-analysis. The prevalence of elevated cardiac troponin I (cTnI) and myoglobin (Mb) in the general population with COVID-19 was 22.9 (19–27%) and 13.5% (10.6–16.4%), respectively. However, the presence of elevated Mb was more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3–52.1%) vs.30.7% (24.7–37.1%)]. Moreover, compared with cTnI, the elevation of Mb also demonstrated tendency of higher correlation with case-severity rate (Mb, r = 13.9 vs. cTnI, r = 3.93) and case-fatality rate (Mb, r = 15.42 vs. cTnI, r = 3.04). Notably, elevated Mb level was also associated with higher odds of severe illness [Mb, OR = 13.75 (10.2–18.54) vs. cTnI, OR = 7.06 (3.94–12.65)] and mortality [Mb, OR = 13.49 (9.3–19.58) vs. cTnI, OR = 7.75 (4.4–13.66)] than cTnI.Conclusions: Patients with COVID-19 and elevated Mb levels are at significantly higher risk of severe disease and mortality. Elevation of Mb may serve as a marker for predicting COVID-19-related adverse outcomes.Prospero Registration Number:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175133, CRD42020175133.

  14. f

    Table_1_The prevalence of sensory changes in post-COVID syndrome: A...

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Aug 25, 2022
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    Pardhan, Shahina; Trott, Mike; Driscoll, Robin (2022). Table_1_The prevalence of sensory changes in post-COVID syndrome: A systematic review and meta-analysis.DOCX [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000431396
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    Dataset updated
    Aug 25, 2022
    Authors
    Pardhan, Shahina; Trott, Mike; Driscoll, Robin
    Description

    Post-COVID syndrome can be defined as symptoms of COVID-19 that persist for longer than 12 weeks, with several studies reporting persistent symptoms relating to the sensory organs (eyes, ears, and nose). The aim of this systematic review was to examine the prevalence of persistent anosmia, hyposmia, ageusia, and hypogeusia, as well as eye/vision and ear/hearing related long-COVID symptoms. Authors searched the electronic databases from inception to November 2021. Search terms included words related to long-COVID, smell, taste, eyes/vision, and ears/hearing, with all observational study designs being included. A random effects meta-analysis was undertaken, calculating the prevalence proportions of anosmia, hyposmia, ageusia, and hypogeusia, respectively. From the initial pool, 21 studies met the inclusion criteria (total n 4,707; median n per study 125; median age = 49.8; median percentage female = 59.2%) and 14 were included in the meta-analysis The prevalence of anosmia was 12.2% (95% CI 7.7–16.6%), hyposmia 29.9% (95% CI 19.9–40%), ageusia 11.7% (95% CI 6.1–17.3%), and hypogeusia 31.2% (95% 16.4–46.1%). Several eye/vision and ear/hearing symptoms were also reported. Considering that changes in the sensory organs are associated with decreases in quality of life, future research should examine the etiology behind the persistent symptoms.Systematic review registration[www.crd.york.ac.uk/prospero], identifier [CRD42021292804].

  15. f

    DataSheet_2_The efficiency of convalescent plasma in COVID-19 patients: A...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Jul 28, 2022
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    Zhang, Zhijin; Qian, Zhenbei; Shao, Shuai; Tong, Zhaohui; Ma, Haomiao; Kang, Hanyujie (2022). DataSheet_2_The efficiency of convalescent plasma in COVID-19 patients: A systematic review and meta-analysis of randomized controlled clinical trials.doc [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000448221
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    Dataset updated
    Jul 28, 2022
    Authors
    Zhang, Zhijin; Qian, Zhenbei; Shao, Shuai; Tong, Zhaohui; Ma, Haomiao; Kang, Hanyujie
    Description

    The objective of this study was to assess whether convalescent plasma therapy could offer survival advantages for patients with novel coronavirus disease 2019 (COVID-19). An electronic search of Pubmed, Web of Science, Embase, Cochrane library and MedRxiv was performed from January 1st, 2020 to April 1st, 2022. We included studies containing patients with COVID-19 and treated with CCP. Data were independently extracted by two reviewers and synthesized with a random-effect analysis model. The primary outcome was 28-d mortality. Secondary outcomes included length of hospital stay, ventilation-free days, 14-d mortality, improvements of symptoms, progression of diseases and requirements of mechanical ventilation. Safety outcomes included the incidence of all adverse events (AEs) and serious adverse events (SAEs). The Cochrane risk-of-bias assessment tool 2.0 was used to assess the potential risk of bias in eligible studies. The heterogeneity of results was assessed by I^2 test and Q statistic test. The possibility of publication bias was assessed by conducting Begg and Egger test. GRADE (Grading of Recommendations Assessment, Development and Evaluation) method were used for quality of evidence. This study had been registered on PROSPERO, CRD42021273608. 32 RCTs comprising 21478 patients with Covid-19 were included. Compared to the control group, COVID-19 patients receiving CCP were not associated with significantly reduced 28-d mortality (CCP 20.0% vs control 20.8%; risk ratio 0.94; 95% CI 0.87-1.02; p = 0.16; I² = 8%). For all secondary outcomes, there were no significant differences between CCP group and control group. The incidence of AEs (26.9% vs 19.4%,; risk ratio 1.14; 95% CI 0.99-01.31; p = 0.06; I² = 38%) and SAEs (16.3% vs 13.5%; risk ratio 1.03; 95% CI 0.87-1.20; p = 0.76; I² = 42%) tended to be higher in the CCP group compared to the control group, while the differences did not reach statistical significance. In all, CCP therapy was not related to significantly improved 28-d mortality or symptoms recovery, and should not be viewed as a routine treatment for COVID-19 patients.Trial registration numberCRD42021273608. Registration on February 28, 2022Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, Identifier CRD42022313265.

  16. f

    Supplementary file 1_Factors influencing adolescents’ decision-making about...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    • +1more
    Updated May 14, 2025
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    Gobbo, Elisa; da Cunha, Nayara Moreira; Tzirita, Sofia; van Wees, Sibylle Herzig (2025). Supplementary file 1_Factors influencing adolescents’ decision-making about COVID-19 vaccination: a systematic review with qualitative synthesis.docx [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0002075603
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    Dataset updated
    May 14, 2025
    Authors
    Gobbo, Elisa; da Cunha, Nayara Moreira; Tzirita, Sofia; van Wees, Sibylle Herzig
    Description

    IntroductionAttitudes towards vaccination are influenced by a broad range of factors, yet little is known about the drivers shaping adolescents’ vaccination beliefs. The aim of this study was to qualitatively explore the factors influencing adolescents’ individual decision-making towards COVID-19 vaccination.MethodsA systematic review was conducted using Medline, Web of Science, Sociological Abstracts, and Publicly Available Content Database. Studies on attitudes, beliefs, and perceptions of adolescents regarding COVID-19 vaccines were included. The JBI Critical Appraisal Checklist was used for quality assessment, followed by thematic synthesis of the included studies.ResultsIn total, 13 studies were included, revealing 5 key themes: (1) Limited vaccine literacy influences adolescents’ attitudes towards COVID-19 vaccines; (2) Family, peers, and community strongly influence adolescents’ COVID-19 vaccine decision-making; (3) Different levels of trust in vaccine providers and governments influence adolescents’ attitudes towards COVID-19 vaccines; (4) Desire to go back to normality influences adolescents’ COVID-19 vaccine attitudes towards vaccine acceptancy; (5) Autonomy influences adolescents’ COVID-19 vaccine decision-making.DiscussionThe review findings suggest that vaccine acceptance among adolescents could be improved through tailored and accessible vaccine literacy messaging, addressing structural mistrust, and empowering adolescents to make autonomous health decisions that take into account diverse contexts and populations.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024512197, identifier CRD42024512197.

  17. York shop covid closed signs

    • kaggle.com
    zip
    Updated Jun 1, 2020
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    ali king (2020). York shop covid closed signs [Dataset]. https://www.kaggle.com/blimp10/york-shop-covid-closed-signs
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    zip(7140 bytes)Available download formats
    Dataset updated
    Jun 1, 2020
    Authors
    ali king
    Area covered
    York
    Description

    The Project involved getting photos of closed due to COVID signs in shops and businesses in York UK.

    One column of "text" includes all the transcripts of the signs with phone numbers removed.

  18. f

    Data_Sheet_1_Safety, Immunogenicity, and Efficacy of COVID-19 Vaccines in...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Apr 14, 2022
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    Chen, Long; Du, Yuxuan; Shi, Yuan (2022). Data_Sheet_1_Safety, Immunogenicity, and Efficacy of COVID-19 Vaccines in Adolescents, Children, and Infants: A Systematic Review and Meta-Analysis.ZIP [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000221525
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    Dataset updated
    Apr 14, 2022
    Authors
    Chen, Long; Du, Yuxuan; Shi, Yuan
    Description

    BackgroundAs the epidemic progresses, universal vaccination against COVID-19 has been the trend, but there are still some doubts about the efficacy and safety of COVID-19 vaccines in adolescents, children, and even infants.PurposeTo evaluate the safety, immunogenicity, and efficacy of COVID-19 vaccines in the population aged 0–17 years.MethodA comprehensive search for relevant randomized controlled trials (RCTs) was conducted in PubMed, Embase, and the Cochrane Library from inception to November 9, 2021. All data were pooled by RevMan 5.3 statistical software, with risk ratio (RR) and its 95% confidence interval as the effect measure. This study protocol was registered on PROSPERO (CRD42021290205).ResultsThere was a total of six randomized controlled trials included in this systematic review and meta-analysis, enrolling participants in the age range of 3–17 years, and containing three types of COVID-19 vaccines. Compared with mRNA vaccines and adenovirus vector vaccines, inactivated vaccines have a more satisfactory safety profile, both after initial (RR 1.40, 95% CI 1.04–1.90, P = 0.03) and booster (RR 1.84, 95% CI 1.20–2.81, P = 0.005) vaccination. The risk of adverse reactions was significantly increased after the first and second doses, but there was no significant difference between the first two doses (RR 1.00, 95%CI 0.99–1.02, P = 0.60). Nevertheless, the two-dose regimen is obviously superior to the single-dose schedule for immunogenicity and efficacy. After booster vaccination, both neutralizing antibodies (RR 144.80, 95%CI 44.97–466.24, P < 0.00001) and RBD-binding antibodies (RR 101.50, 95%CI 6.44–1,600.76, P = 0.001) reach optimal levels, but the cellular immune response seemed not to be further enhanced. In addition, compared with younger children, older children and adolescents were at significantly increased risk of adverse reactions after vaccination, with either mRNA or inactivated vaccines, accompanied by a stronger immune response.ConclusionThe available evidence suggests that the safety, immunogenicity and efficacy of COVID-19 vaccines are acceptable in people aged 3–17 years. However, there is an urgent need for additional multicenter, large-sample studies, especially in younger children under 3 years of age and even in infants, with long-term follow-up data.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021290205, identifier: CRD42021290205.

  19. f

    Data_Sheet_1_COVID-19 and Hemoglobinopathies: A Systematic Review of...

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    • +1more
    docx
    Updated May 30, 2023
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    Jun Xin Lee; Wei Keong Chieng; Sie Chong Doris Lau; Chai Eng Tan (2023). Data_Sheet_1_COVID-19 and Hemoglobinopathies: A Systematic Review of Clinical Presentations, Investigations, and Outcomes.docx [Dataset]. http://doi.org/10.3389/fmed.2021.757510.s001
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Jun Xin Lee; Wei Keong Chieng; Sie Chong Doris Lau; Chai Eng Tan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This systematic review aimed to provide an overview of the clinical profile and outcome of COVID-19 infection in patients with hemoglobinopathy. The rate of COVID-19 mortality and its predictors were also identified. A systematic search was conducted in accordance with PRISMA guidelines in five electronic databases (PubMed, Scopus, Web of Science, Embase, WHO COVID-19 database) for articles published between 1st December 2019 to 31st October 2020. All articles with laboratory-confirmed COVID-19 cases with underlying hemoglobinopathy were included. Methodological quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Thirty-one articles with data on 246 patients with hemoglobinopathy were included in this review. In general, clinical manifestations of COVID-19 infection among patients with hemoglobinopathy were similar to the general population. Vaso-occlusive crisis occurred in 55.6% of sickle cell disease patients with COVID-19 infection. Mortality from COVID-19 infection among patients with hemoglobinopathy was 6.9%. After adjusting for age, gender, types of hemoglobinopathy and oxygen supplementation, respiratory (adj OR = 89.63, 95% CI 2.514–3195.537, p = 0.014) and cardiovascular (adj OR = 35.20, 95% CI 1.291–959.526, p = 0.035) comorbidities were significant predictors of mortality. Patients with hemoglobinopathy had a higher mortality rate from COVID-19 infection compared to the general population. Those with coexisting cardiovascular or respiratory comorbidities require closer monitoring during the course of illness. More data are needed to allow a better understanding on the clinical impact of COVID-19 infections among patients with hemoglobinopathy.Clinical Trial Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218200.

  20. d

    New York Forward Industry Reopening Status by Phase

    • catalog.data.gov
    • datasets.ai
    • +1more
    Updated Sep 15, 2023
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    data.ny.gov (2023). New York Forward Industry Reopening Status by Phase [Dataset]. https://catalog.data.gov/dataset/new-york-forward-industry-reopening-status-by-phase
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    Dataset updated
    Sep 15, 2023
    Dataset provided by
    data.ny.gov
    Area covered
    New York
    Description

    This dataset includes the different reopening statuses and health and safety guidelines that were assigned to individual industries during the State of New York’s COVID-19 declared state of emergency, which began on March 7, 2020, and ended on June 24, 2021.

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New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://github.com/nytimes/covid-19-data

Coronavirus (Covid-19) Data in the United States

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csvAvailable download formats
Dataset provided by
New York Times
License

https://github.com/nytimes/covid-19-data/blob/master/LICENSEhttps://github.com/nytimes/covid-19-data/blob/master/LICENSE

Description

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

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