11 datasets found
  1. COVID-19 cases and deaths per million in 210 countries as of July 13, 2022

    • statista.com
    • ai-chatbox.pro
    Updated Nov 25, 2024
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    Statista (2024). COVID-19 cases and deaths per million in 210 countries as of July 13, 2022 [Dataset]. https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/
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    Dataset updated
    Nov 25, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

    The difficulties of death figures

    This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

    Where are these numbers coming from?

    The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

  2. f

    Data_Sheet_1_The impact of COVID-19 certification mandates on the number of...

    • frontiersin.figshare.com
    pdf
    Updated Jun 21, 2023
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    Kim López-Güell; Albert Prats-Uribe; Martí Català; Clara Prats; Jotun Hein; Daniel Prieto-Alhambra (2023). Data_Sheet_1_The impact of COVID-19 certification mandates on the number of cases of and hospitalizations with COVID-19 in the UK: A difference-in-differences analysis.PDF [Dataset]. http://doi.org/10.3389/fpubh.2023.1019223.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers
    Authors
    Kim López-Güell; Albert Prats-Uribe; Martí Català; Clara Prats; Jotun Hein; Daniel Prieto-Alhambra
    License

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

    Area covered
    United Kingdom
    Description

    BackgroundMandatory COVID-19 certification, showing proof of vaccination, negative test, or recent infection to access to public venues, was introduced at different times in the four countries of the UK. We aim to study its effects on the incidence of cases and hospital admissions.MethodsWe performed Negative binomial segmented regression and ARIMA analyses for four countries (England, Northern Ireland, Scotland and Wales), and fitted Difference-in-Differences models to compare the latter three to England, as a negative control group, since it was the last country where COVID-19 certification was introduced. The main outcome was the weekly averaged incidence of COVID-19 cases and hospital admissions.ResultsCOVID-19 certification led to a decrease in the incidence of cases and hospital admissions in Northern Ireland, as well as in Wales during the second half of November. The same was seen for hospital admissions in Wales and Scotland during October. In Wales the incidence rate of cases in October already had a decreasing tendency, as well as in England, hence a particular impact of COVID-19 certification was less obvious. Method assumptions for the Difference-in-Differences analysis did not hold for Scotland. Additional NBSR and ARIMA models suggest similar results, while also accounting for correlation in the latter. The assessment of the effect in England itself leads one to believe that this intervention might not be strong enough for the Omicron variant, which was prevalent at the time of introduction of COVID-19 certification in the country.ConclusionsMandatory COVID-19 certification reduced COVID-19 transmission and hospitalizations when Delta predominated in the UK, but lost efficacy when Omicron became the most common variant.

  3. COVID-19 death rates in 2020 countries worldwide as of April 26, 2022

    • statista.com
    Updated Apr 15, 2022
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    Statista (2022). COVID-19 death rates in 2020 countries worldwide as of April 26, 2022 [Dataset]. https://www.statista.com/statistics/1105914/coronavirus-death-rates-worldwide/
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    Dataset updated
    Apr 15, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

    Where are these numbers coming from?

    The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

    A word on the flaws of numbers like this

    People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

  4. Europe Coronavirus Test Kits Market Analysis - Size and Forecast 2024-2028

    • technavio.com
    pdf
    Updated Sep 14, 2024
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    Technavio (2024). Europe Coronavirus Test Kits Market Analysis - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/corona-virus-test-kits-market-in-europe-industry-analysis
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    pdfAvailable download formats
    Dataset updated
    Sep 14, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2024 - 2028
    Area covered
    Europe
    Description

    Snapshot img

    Europe Coronavirus Test Kits Market Size 2024-2028

    The coronavirus test kits market in Europe size is forecast to decrease by USD 1.89 billion at a CAGR of -72.5% between 2023 and 2028.

    The European coronavirus test kits market is experiencing significant growth due to the increasing demand for rapid diagnostic solutions. The emergence of SARS-CoV-2 variants, such as the Delta variant, has highlighted the importance of accurate and timely testing. Oropharyngeal swabs, nasal swabs, and sputum samples are commonly used for diagnosing COVID-19 infections. Point-of-Care (PoC) kits have gained popularity due to their convenience and quick results. However, the accuracy of diagnostic tests remains a challenge, with the Indian Council of Medical Research (ICMR) and the Health Ministry reporting false positives and negatives. The market is expected to continue its expansion as the world navigates the ongoing pandemic.
    

    What will be the Size of the Market During the Forecast Period?

    Request Free Sample

    The European coronavirus test kits market is witnessing significant growth due to the ongoing pandemic caused by SARS-CoV-2. The demand for test kits is driven by the need for early detection and rapid screening of infected individuals to prevent the spread of the virus within communities. According to the medical device database from GlobalData, RT-PCR tests remain the gold standard for diagnosing SARS-CoV-2 infection. These tests detect viral genetic material from human nasal samples, providing accurate results. However, the time-consuming nature of these tests and the requirement for specialized equipment have led to the emergence of alternative solutions, such as SARS-CoV-2 antigen tests.
    Moreover, rapid antigen tests, also known as point-of-care (PoC) kits, offer user-friendly solutions for healthcare systems. These tests provide results within minutes, making them ideal for mass screening in various settings, including schools, workplaces, and airports. The Delta variant and the emerging Omicron variant of SARS-CoV-2 have added to the urgency for effective testing solutions. The European Union has been proactive in addressing this need, with initiatives such as the European Health Union and the EU Digital COVID Certificate system. The European coronavirus test kits market is expected to continue its growth trajectory, driven by the ongoing pandemic and the need for regular testing to ensure public health and safety
    

    How is this market segmented and which is the largest segment?

    The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

    End-user
    
      Government
      Non government
    
    
    Type
    
      Rapid test kit
      RT-PCR
      Others
    
    
    Geography
    
      Europe
    
        Germany
        UK
        France
    

    By End-user Insights

    The government segment is estimated to witness significant growth during the forecast period.
    

    In Europe, various diagnostic techniques are utilized to identify COVID-19 cases, with WHO recommending that countries with limited testing capacity or inexperienced national laboratories send their initial positive and negative samples to five referral laboratories in Europe for confirmatory testing. These laboratories include the German coronavirus diagnostic working group at Charite and Robert Koch Institute in Berlin, Erasmus Medical Center in Rotterdam, the Institute Pasteur in Paris, and the Respiratory Virus Unit at Public Health England. Additionally, several other laboratories in Belgium, Luxembourg, the Netherlands, and Spain offer diagnostic testing support. In the UK, Public Health England (PHE) regional laboratories provide testing facilities alongside WHO referral laboratories. As the world awaits vaccinations and booster doses, public awareness remains crucial. During the flu season, mask mandates and social distancing measures continue to be essential preventative measures.

    Get a glance at the market report of share of various segments Request Free Sample

    Market Dynamics

    Our Europe Coronavirus Test Kits Market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

    What are the key market drivers leading to the rise in the adoption of the European coronavirus Test Kits Market?

    Rising adoption of rapid coronavirus test kits is the key driver of the market.

    In Europe, the coronavirus pandemic has put immense pressure on healthcare systems, particularly in terms of diagnostic capabilities. To mitigate this challenge, European governments have prioritized expanding their testing capacity through various means. In 2022, there was a significant push to distribute coronavirus test kits acro
    
  5. f

    Supporting data for Fig 6.

    • plos.figshare.com
    xlsx
    Updated Jun 2, 2023
    + more versions
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    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley (2023). Supporting data for Fig 6. [Dataset]. http://doi.org/10.1371/journal.pbio.3002118.s009
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    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley
    License

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

    Description

    The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England’s mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta’s emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

  6. e

    Data from: Integrating Machine Learning-Enhanced Immunopeptidomics and...

    • ebi.ac.uk
    Updated Oct 14, 2024
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    Saketh Kapoor (2024). Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines [Dataset]. https://www.ebi.ac.uk/pride/archive/projects/PXD052187
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    Dataset updated
    Oct 14, 2024
    Authors
    Saketh Kapoor
    Variables measured
    Proteomics
    Description

    Next-generation T-cell-directed vaccines for COVID-19 aim to induce durable T-cell immunity against circulating and future hypermutated SARS-CoV-2 variants. Mass Spectrometry (MS)based immunopeptidomics holds promise for guiding vaccine design, but computational challenges impede the precise and unbiased identification of conserved T-cell epitopes crucial for vaccines against rapidly mutating viruses. We introduce a computational framework and analysis platform integrating a novel machine learning algorithm, immunopeptidomics, intra-host data, epitope immunogenicity, and geo-temporal CD8+ T-cell epitope conservation analyses. Central to our approach is MHCvalidator, a novel artificial neural network algorithm enhancing MS-based immunopeptidomics sensitivity by modeling antigen presentation and sequence features. MHCvalidator identified a novel nonconventional SARS-CoV-2 T-cell epitope presented by B7 supertype molecules, originating from a +1-frameshift in a truncated Spike (S) antigen, supported by ribo-seq data. Intra-host analysis of SARS-CoV-2 proteomes from ~100,000 COVID-19 patients revealed a prevalent S antigen truncation in ~51% of cases, exposing a rich source of frameshifted viral antigens. Our framework includes EpiTrack, a new computational pipeline tracking global mutational dynamics of MHCvalidator-identified SARS-CoV-2 CD8+ epitopes from vaccine BNT162b4. While most vaccine-encoded CD8+ epitopes exhibit global conservation from January 2020 to October 2023, a highly immunodominant A*01-associated epitope, especially in hospitalized patients, undergoes substantial mutations in Delta and Omicron variants. Our approach unveils unprecedented SARS-CoV-2 T-cell epitopes, elucidates novel antigenic features, and underscores mutational dynamics of vaccine-relevant epitopes. The analysis platform is applicable to any viruses, and underscores the need for continual vigilance in T-cell vaccine development against the evolving landscape of hypermutating SARS-CoV-2 variants.

  7. f

    Characteristics and exposures of Delta versus Omicron reinfection cases.

    • plos.figshare.com
    xls
    Updated Sep 6, 2024
    + more versions
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    Mark Postans; Nicole Pacchiarini; Jiao Song; Simon Cottrell; Catie Williams; Andrew Beazer; Catherine Moore; Thomas R. Connor; Christopher Williams (2024). Characteristics and exposures of Delta versus Omicron reinfection cases. [Dataset]. http://doi.org/10.1371/journal.pone.0309645.t004
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    xlsAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Mark Postans; Nicole Pacchiarini; Jiao Song; Simon Cottrell; Catie Williams; Andrew Beazer; Catherine Moore; Thomas R. Connor; Christopher Williams
    License

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

    Description

    Characteristics and exposures of Delta versus Omicron reinfection cases.

  8. Pearson correlation between modelled estimates of swab positivity and...

    • plos.figshare.com
    xlsx
    Updated May 30, 2023
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    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley (2023). Pearson correlation between modelled estimates of swab positivity and modelled estimates of daily cases. [Dataset]. http://doi.org/10.1371/journal.pbio.3002118.s006
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    xlsxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley
    License

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

    Description

    Pearson correlation between modelled estimates of swab positivity and modelled estimates of daily cases.

  9. The average proportion of the population vaccinated, average proportion of...

    • plos.figshare.com
    xlsx
    Updated Jun 2, 2023
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    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley (2023). The average proportion of the population vaccinated, average proportion of infections caused by each variant, and mean IFR and IHR over fixed periods of time over the duration of rounds 14–19 of REACT-1. [Dataset]. http://doi.org/10.1371/journal.pbio.3002118.s004
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    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley
    License

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

    Description

    Mean IFR and IHR for each period are compared to a baseline period running from 4 September 2021–16 October 2021. The time-lags between positivity and deaths and hospitalisations used in calculating mean IFR and IHR are the best-fit time-lags from the time-delay models fit to rounds 14–19 of REACT-1. (XLSX)

  10. Estimated time-lags and case ascertainment for time-delay models fit to...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xlsx
    Updated Jun 2, 2023
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    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley (2023). Estimated time-lags and case ascertainment for time-delay models fit to REACT-1 using the time series of daily cases. [Dataset]. http://doi.org/10.1371/journal.pbio.3002118.s005
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    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley
    License

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

    Description

    Estimated time-lags and case ascertainment for time-delay models fit to REACT-1 using the time series of daily cases.

  11. f

    Estimated time-lags and IFR and IHR for all time-delay models fit to...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xlsx
    Updated Jun 2, 2023
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    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley (2023). Estimated time-lags and IFR and IHR for all time-delay models fit to REACT-1. [Dataset]. http://doi.org/10.1371/journal.pbio.3002118.s001
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Oliver Eales; David Haw; Haowei Wang; Christina Atchison; Deborah Ashby; Graham S. Cooke; Wendy Barclay; Helen Ward; Ara Darzi; Christl A. Donnelly; Marc Chadeau-Hyam; Paul Elliott; Steven Riley
    License

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

    Description

    Estimated time-lags and IFR and IHR for all time-delay models fit to REACT-1.

  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Statista (2024). COVID-19 cases and deaths per million in 210 countries as of July 13, 2022 [Dataset]. https://www.statista.com/statistics/1104709/coronavirus-deaths-worldwide-per-million-inhabitants/
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COVID-19 cases and deaths per million in 210 countries as of July 13, 2022

Explore at:
162 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Nov 25, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Worldwide
Description

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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