6 datasets found
  1. Coronavirus (COVID-19) death numbers by gender and age Germany 2024

    • statista.com
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    Statista, Coronavirus (COVID-19) death numbers by gender and age Germany 2024 [Dataset]. https://www.statista.com/statistics/1105512/coronavirus-covid-19-deaths-by-gender-germany/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Germany
    Description

    The coronavirus (COVID-19) has led to over 183,000 deaths in Germany, as of 2024. When looking at the distribution of deaths by age, based on the figures currently available, most death occurred in the age group 80 years and older at approximately 118,938 deaths.

  2. H

    National and Subnational Estimates of the Covid 19 Reproduction Number (R)...

    • dataverse.harvard.edu
    Updated Dec 7, 2021
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    Sam Abbott; Christopher Bennett; Joe Hickson; Jamie Allen; Katharine Sherratt; Sebastian Funk (2021). National and Subnational Estimates of the Covid 19 Reproduction Number (R) for Germany Based on Test Results [Dataset]. http://doi.org/10.7910/DVN/314SD7
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 7, 2021
    Dataset provided by
    Harvard Dataverse
    Authors
    Sam Abbott; Christopher Bennett; Joe Hickson; Jamie Allen; Katharine Sherratt; Sebastian Funk
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Germany
    Description

    Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in Germany. These results are impacted by changes in testing effort, increases and decreases in testing effort will increase and decrease reproduction number estimates respectively.

  3. Number of coronavirus (COVID-19) vaccinations in Germany since 2020

    • statista.com
    Updated Dec 15, 2022
    + more versions
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    Statista (2022). Number of coronavirus (COVID-19) vaccinations in Germany since 2020 [Dataset]. https://www.statista.com/statistics/1195560/coronavirus-covid-19-vaccinations-number-germany/
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    Dataset updated
    Dec 15, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Germany
    Description

    Currently around 127.7 million coronavirus (COVID-19) vaccinations have taken place in Germany since the beginning of the campaign at the end of December 2020. The vaccination rollout followed the approval of the BioNTech-Pfizer vaccine in the EU. Other vaccines have since been allowed. Vaccinations are free and voluntary in Germany. The total number of vaccinations mentioned here includes both first and second doses. Most vaccines need to be administered in two doses to be effective against the virus.

  4. f

    Table_1_Case Report: Return to Sport Following the COVID-19 Lockdown and Its...

    • datasetcatalog.nlm.nih.gov
    Updated Feb 18, 2021
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    Thom, Mitchell L.; Voos, James E.; Seshadri, Dhruv R.; Harlow, Ethan R.; Drummond, Colin K. (2021). Table_1_Case Report: Return to Sport Following the COVID-19 Lockdown and Its Impact on Injury Rates in the German Soccer League.docx [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000897538
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    Dataset updated
    Feb 18, 2021
    Authors
    Thom, Mitchell L.; Voos, James E.; Seshadri, Dhruv R.; Harlow, Ethan R.; Drummond, Colin K.
    Description

    The Bundesliga made headlines for becoming the first major sports league to return to sport worldwide following COVID-19 lockdown. To-date, there lacks retrospective studies on longitudinal injury rates to elucidate the effect isolation measures had on the health and safety of professional athletes. This study sought to compare injury rates experienced by Bundesliga athletes before and after the COVID-19 lockdown. Data was collected from public injury and player reports regarding the Bundesliga, with injury defined as trauma resulting in loss of game time. Descriptive statistics were used to present differences in injury incidence between all Bundesliga Match days pre- and post-lockdown. Between the league's resumption and completion on May 16 and June 27, 2020, injuries occurred in 21 forwards (FW), 11 central midfielders (CM), 12 wide midfielders (WM), 16 central defenders (CD), 6 fullbacks (FB), and 2 goalkeepers. Players had 1.13 (95% CI 0.78, 1.64) times the odds of being injured following the COVID-19 lockdown, with a 3.12 times higher rate of injury when controlling for games played compared to injury rates pre-lockdown (0.84 injuries per game vs. 0.27 injuries per game). The most frequent injury group was muscular injuries with 23 injuries total, with 17% of athletes experiencing injury during their first competitive match following lockdown. Injury rate increased over 3-fold following COVID-19 lockdown. Athletes did not experience an increased rate of injury with more cumulative competitive matches played. High injury incidence for players yet to complete their first competitive match may imply suboptimal sport readiness following home confinement.

  5. Data_Sheet_1_Tackling the Waves of COVID-19: A Planning Model for...

    • frontiersin.figshare.com
    pdf
    Updated May 30, 2023
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    Felicitas Schmidt; Christian Hauptmann; Walter Kohlenz; Philipp Gasser; Sascha Hartmann; Michael Daunderer; Thomas Weiler; Lorenz Nowak (2023). Data_Sheet_1_Tackling the Waves of COVID-19: A Planning Model for Intrahospital Resource Allocation.PDF [Dataset]. http://doi.org/10.3389/frhs.2021.718668.s001
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Felicitas Schmidt; Christian Hauptmann; Walter Kohlenz; Philipp Gasser; Sascha Hartmann; Michael Daunderer; Thomas Weiler; Lorenz Nowak
    License

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

    Description

    Background: The current pandemic requires hospitals to ensure care not only for the growing number of COVID-19 patients but also regular patients. Hospital resources must be allocated accordingly.Objective: To provide hospitals with a planning model to optimally allocate resources to intensive care units given a certain incidence of COVID-19 cases.Methods: The analysis included 334 cases from four adjacent counties south-west of Munich. From length of stay and type of ward [general ward (NOR), intensive care unit (ICU)] probabilities of case numbers within a hospital at a certain time point were derived. The epidemiological situation was simulated by the effective reproduction number R, the infection rates in mid-August 2020 in the counties, and the German hospitalization rate. Simulation results are compared with real data from 2nd and 3rd wave (September 2020–May 2021).Results: With R = 2, a hospitalization rate of 17%, mitigation measures implemented on day 9 (i.e., 7-day incidence surpassing 50/100,000), the peak occupancy was reached on day 22 (155.1 beds) for the normal ward and on day 25 (44.9 beds) for the intensive care unit. A higher R led to higher occupancy rates. Simulated number of infections and intensive care unit occupancy was concordant in validation with real data obtained from the 2nd and 3rd waves in Germany.Conclusion: Hospitals could expect a peak occupancy of normal ward and intensive care unit within ~5–11 days after infections reached their peak and critical resources could be allocated accordingly. This delay (in particular for the peak of intensive care unit occupancy) might give options for timely preparation of additional intensive care unit resources.

  6. DataSheet1_Genetic Drift Versus Climate Region Spreading Dynamics of...

    • frontiersin.figshare.com
    pdf
    Updated May 30, 2023
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    R. Di Pietro; M. Basile; L. Antolini; S. Alberti (2023). DataSheet1_Genetic Drift Versus Climate Region Spreading Dynamics of COVID-19.pdf [Dataset]. http://doi.org/10.3389/fgene.2021.663371.s001
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    R. Di Pietro; M. Basile; L. Antolini; S. Alberti
    License

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

    Description

    Background: The current propagation models of COVID-19 are poorly consistent with existing epidemiological data and with evidence that the SARS-CoV-2 genome is mutating, for potential aggressive evolution of the disease.Objectives: We looked for fundamental variables that were missing from current analyses. Among them were regional climate heterogeneity, viral evolution processes versus founder effects, and large-scale virus containment measures.Methods: We challenged regional versus genetic evolution models of COVID-19 at a whole-population level, over 168,089 laboratory-confirmed SARS-CoV-2 infection cases in Italy, Spain, and Scandinavia at early time-points of the pandemic. Diffusion data in Germany, France, and the United Kingdom provided a validation dataset of 210,239 additional cases.Results: Mean doubling time of COVID-19 cases was 6.63 days in Northern versus 5.38 days in Southern Italy. Spain extended this trend of faster diffusion in Southern Europe, with a doubling time of 4.2 days. Slower doubling times were observed in Sweden (9.4 days), Finland (10.8 days), and Norway (12.95 days). COVID-19 doubling time in Germany (7.0 days), France (7.5 days), and the United Kingdom (7.2 days) supported the North/South gradient model. Clusters of SARS-CoV-2 mutations upon sequential diffusion were not found to clearly correlate with regional distribution dynamics.Conclusion: Acquisition of mutations upon SARS-CoV-2 spreading failed to explain regional diffusion heterogeneity at early pandemic times. Our findings indicate that COVID-19 transmission rates are rather associated with a sharp North/South climate gradient, with faster spreading in Southern regions. Thus, warmer climate conditions may not limit SARS-CoV-2 infectivity. Very cold regions may be better spared by recurrent courses of SARS-CoV-2 infection.

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Statista, Coronavirus (COVID-19) death numbers by gender and age Germany 2024 [Dataset]. https://www.statista.com/statistics/1105512/coronavirus-covid-19-deaths-by-gender-germany/
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Coronavirus (COVID-19) death numbers by gender and age Germany 2024

Explore at:
17 scholarly articles cite this dataset (View in Google Scholar)
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Germany
Description

The coronavirus (COVID-19) has led to over 183,000 deaths in Germany, as of 2024. When looking at the distribution of deaths by age, based on the figures currently available, most death occurred in the age group 80 years and older at approximately 118,938 deaths.

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