78 datasets found
  1. y

    UK Coronavirus Recoveries

    • ycharts.com
    html
    Updated Mar 10, 2023
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    Johns Hopkins Center for Systems Science and Engineering (2023). UK Coronavirus Recoveries [Dataset]. https://ycharts.com/indicators/uk_coronavirus_recoveries
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    htmlAvailable download formats
    Dataset updated
    Mar 10, 2023
    Dataset provided by
    YCharts
    Authors
    Johns Hopkins Center for Systems Science and Engineering
    License

    https://www.ycharts.com/termshttps://www.ycharts.com/terms

    Time period covered
    Jan 22, 2020 - Mar 9, 2023
    Area covered
    United Kingdom
    Variables measured
    UK Coronavirus Recoveries
    Description

    View daily updates and historical trends for UK Coronavirus Recoveries. from United Kingdom. Source: Johns Hopkins Center for Systems Science and Engineer…

  2. Eating out and drinking sector: plans for business recovery from COVID-19 UK...

    • statista.com
    Updated May 26, 2025
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    Statista (2025). Eating out and drinking sector: plans for business recovery from COVID-19 UK [Dataset]. https://www.statista.com/statistics/1118762/business-recovery-plans-in-eating-out-sector-uk/
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    Dataset updated
    May 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 17, 2020 - Apr 24, 2020
    Area covered
    United Kingdom
    Description

    In the UK eating out and drinking sector in April 2020, a total of 81 percent of business leaders had already started recovery planning for various scenarios. Others were waiting for more information, or said they do not have the capactiy to plan for recovery yet. Only one percent of respondents did not have any recovery plans at the time.

  3. u

    Living With Covid Recovery final dataset

    • rdr.ucl.ac.uk
    bin
    Updated Jan 1, 2024
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    Elizabeth Murray; Henry Goodfellow; Ann Blandford; Katherine Bradbury; Manuel De Oliveira Gomes; Fiona Hamilton; William Henley; Fiona Stevenson (2024). Living With Covid Recovery final dataset [Dataset]. http://doi.org/10.5522/04/24000087.v1
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    binAvailable download formats
    Dataset updated
    Jan 1, 2024
    Dataset provided by
    University College London
    Authors
    Elizabeth Murray; Henry Goodfellow; Ann Blandford; Katherine Bradbury; Manuel De Oliveira Gomes; Fiona Hamilton; William Henley; Fiona Stevenson
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Release model requires permission from Fiona Stevenson for data protection purposes. For access to this dataset please contact f.stevenson@ucl.ac.uk

    Please find further information regarding this dataset in the attached file. Design Cross-sectional single-arm service evaluation of real-time user data. Setting 31 Post-COVID clinics in the UK. Participants 3,754 adults diagnosed with PCS in primary or secondary care, deemed suitable for rehabilitation. Intervention Patients using the Living With Covid Recovery (LWCR) Digital Health Intervention (DHI) registered between 30/11/20 and 23/03/22. Primary and secondary outcome measures The primary outcome was the baseline Work and Social Adjustment Scale (WSAS). WSAS measures the functional limitations of the patient; scores ≥20 indicate moderately severe limitations. Other symptom data collected included fatigue (FACIT-F), depression (PHQ-8), anxiety (GAD-7), breathlessness (MRC Dyspnoea Scale and Dyspnoea-12), cognitive impairment (PDQ-5) and health-related quality of life (EQ-5D).

    Data collection period 30/11/20 to 17/7/22 (inclusive)

  4. Inclusive Recovery and COVID Impact Assessment - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Mar 31, 2025
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    ckan.publishing.service.gov.uk (2025). Inclusive Recovery and COVID Impact Assessment - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/inclusive-recovery-and-covid-impact-assessment
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    Dataset updated
    Mar 31, 2025
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    Calderdale COVID Impact Assessment has been produced as evidence for the development and delivery for the Calderdale Inclusive Economic Recovery Plan and sets out a focus on economic recovery, but also considers the wider impacts of COVID-19 on Calderdale and its communities. Also see Inclusive Recovery and COVID Impact Assessment for more information and a range of related reports and datasets.

  5. Eating and drinking out sector: challenges for business recovery...

    • statista.com
    Updated May 6, 2020
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    Statista (2020). Eating and drinking out sector: challenges for business recovery post-COVID-19 UK [Dataset]. https://www.statista.com/statistics/1118854/business-recovery-challenges-in-eating-out-sector-uk/
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    Dataset updated
    May 6, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 17, 2020 - Apr 24, 2020
    Area covered
    United Kingdom
    Description

    Business leaders within the eating and drinking out sector in the United Kingdom expected their biggest challenges for business recovery post-lockdown due to the coronavirus (COVID-19) pandemic to be operational changes and Government regulations. Challenges related to staff and supply were not considered as major.

  6. Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2,...

    • statista.com
    + more versions
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    Statista, Coronavirus (COVID-19) cases, recoveries, and deaths worldwide as of May 2, 2023 [Dataset]. https://www.statista.com/statistics/1087466/covid19-cases-recoveries-deaths-worldwide/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 2, 2023
    Area covered
    Worldwide
    Description

    As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

    The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

    Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

  7. s

    Wider impacts of the COVID-19 pandemic and recovery of population health...

    • ckan.publishing.service.gov.uk
    Updated Oct 28, 2025
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    (2025). Wider impacts of the COVID-19 pandemic and recovery of population health outcomes for London [Dataset]. https://ckan.publishing.service.gov.uk/dataset/wider-impacts-of-the-covid-19-pandemic-and-recovery-of-population-health-outcomes-for-london1
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    Dataset updated
    Oct 28, 2025
    Area covered
    London
    Description

    These documents were produced through a collaboration between GLA, PHE London and Association of Directors of Public Health London. The wider impacts slide set pulls together a series of rapid evidence reviews and consultation conversations with key London stakeholders. The evidence reviews and stakeholder consultations were undertaken to explore the wider impacts of the pandemic on Londoners and the considerations for recovery within the context of improving population health outcomes. The information presented in the wider impact slides represents the emerging evidence available at the time of conducting the work (May-August 2020). The resource is not routinely updated and therefore further evidence reviews to identify more recent research and evidence should be considered alongside this resource. It is useful to look at this in conjunction with the ‘People and places in London most vulnerable to COVID-19 and its social and economic consequences’ report commissioned as part of this work programme and produced by the New Policy Institute. Additional work was also undertaken on the housing issues and priorities during COVID. A short report and examples of good practice are provided here. These reports are intended as a resource to support stakeholders in planning during the transition and recovery phase. However, they are also relevant to policy and decision-making as part of the ongoing response. The GLA have also commissioned the University of Manchester to undertake a rapid evidence review on inequalities in relation to COVID-19 and their effects on London.

  8. Coronavirus (COVID-19) data on funding claims by institutions

    • gov.uk
    • s3.amazonaws.com
    Updated Jul 3, 2025
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    Education and Skills Funding Agency (2025). Coronavirus (COVID-19) data on funding claims by institutions [Dataset]. https://www.gov.uk/government/publications/coronavirus-covid-19-data-on-funding-claims-by-institutions
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    Dataset updated
    Jul 3, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Education and Skills Funding Agency
    Description

    The Education and Skills Funding Agency (ESFA) closed on 31 March 2025. All activity has moved to the Department for Education (DfE). You should continue to follow this guidance.

    This page outlines payments made to institutions for claims they have made to ESFA for various grants. These include, but are not exclusively, COVID-19 support grants. Information on funding for grants based on allocations will be on the specific page for the grant.

    Claim-based grants included

    Senior mental health lead training

    Financial assistance towards the cost of training a senior member of school or college staff in mental health and wellbeing in the 2021 to 2022, 2022 to 2023, 2023 to 2024 and 2024 to 2025 financial years. The information provided is for payments up to the end of March 2025.

    COVID-19 16 to 19 tuition fund 2020 to 2021 and 2021 to 2022

    Funding for eligible 16 to 19 institutions to deliver small group and/or one-to-one tuition for disadvantaged students and those with low prior attainment to help support education recovery from the COVID-19 pandemic.

    Due to continued pandemic disruption during academic year 2020 to 2021 some institutions carried over funding from academic year 2020 to 2021 to 2021 to 2022.

    Therefore, any considerations of spend or spend against funding allocations should be considered across both years.

    School funding: exceptional costs associated with coronavirus (COVID-19)

    Financial assistance available to schools to cover increased premises, free school meals and additional cleaning-related costs associated with keeping schools open over the Easter and summer holidays in 2020, during the coronavirus (COVID-19) pandemic.

    Coronavirus (COVID-19) free school meals: additional costs

    Financial assistance available to meet the additional cost of the provision of free school meals to pupils and students where they were at home during term time, for the period January 2021 to March 2021.

    Alternative provision: year 11 transition funding

    Financial assistance for alternative provision settings to provide additional transition support into post-16 destinations for year 11 pupils from June 2020 until the end of the autumn term (December 2020). This has now been updated to include funding for support provided by alternative provision settings from May 2021 to the end of February 2022.

    Coronavirus (COVID-19) 2021 qualifications fund for schools and colleges

    Financial assistance for schools, colleges and other exam centres to run exams and assessments during the period October 2020 to March 2021 (or for functional skills qualifications, October 2020 to December 2020). Now updated to include claims for eligible costs under the 2021 qualifications fund for the period October 2021 to March 2022.

    "https://www.gov.uk/guidance/academic-mentors-programme-grant-conditions-of-funding">National tutoring programme: academic mentors programme

  9. Latest Coronavirus COVID-19 figures for UK

    • covid19-today.pages.dev
    json
    Updated Jul 30, 2025
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    Worldometers (2025). Latest Coronavirus COVID-19 figures for UK [Dataset]. https://covid19-today.pages.dev/countries/uk/
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    jsonAvailable download formats
    Dataset updated
    Jul 30, 2025
    Dataset provided by
    Worldometershttps://dadax.com/
    CSSE at JHU
    License

    https://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE

    Area covered
    United Kingdom
    Description

    In past 24 hours, UK, Europe had N/A new cases, N/A deaths and N/A recoveries.

  10. COVID-19 online diaries - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Jun 9, 2025
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    ckan.publishing.service.gov.uk (2025). COVID-19 online diaries - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/covid-19-online-diaries
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    Dataset updated
    Jun 9, 2025
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    The Opinion Research team set up an online diary to capture the views, behaviours and experiences of a sample of Londoners during the coronavirus outbreak. Commencing in late May, this online diary ran for 8 weeks. Fortnightly summary reports can be found accessed below, and included: Week 1 and 2 – reflected on experiences during lockdown to date and explored views on easing lockdown. Week 3 and 4 – explored priorities for London’s recovery, both short and long-term, and views on the Test and Trace system. Week 5 and 6 – explored growing disengagement with the coronavirus outbreak and reflected on some of the positive impacts of lockdown.​ Week 7 and 8 – explored the idea of a 15-minute city and aspirations for the future of London. 

  11. Covid 19 Resources

    • ckan.publishing.service.gov.uk
    • data.leicester.gov.uk
    • +1more
    Updated Feb 8, 2023
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    ckan.publishing.service.gov.uk (2023). Covid 19 Resources [Dataset]. https://ckan.publishing.service.gov.uk/dataset/covid-19-resources
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    Dataset updated
    Feb 8, 2023
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    A joint map of resources targeted towards the remedy and recovery during and after the COVID 19 crisis. Information about resources and support services provided by a number of organisations across the city.If you are a provider of services and resources, your information can be added and made public via the form available from here.If you have any questions about this dataset please email smart@leicester.gov.uk or complete the form available from here.

  12. Researching Community Collecting During COVID-19 - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Jul 30, 2021
    + more versions
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    ckan.publishing.service.gov.uk (2021). Researching Community Collecting During COVID-19 - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/researching-community-collecting-during-covid-19
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    Dataset updated
    Jul 30, 2021
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    The Community Engagement team at the Greater London Authority (GLA) commissioned this report to identify and examine past and present projects which involve collecting Londoners experiences of COVID-19 through a variety of creative and non-traditional materials. The purpose of the report is to: provide an overview of projects and activities which record Londoners COVID-19 stories and experiences. outline who is responsible for these projects and activities (individuals, museums, community groups, charities, community interest groups, non-profits, other institutions and organisations). analyse the voices of individuals/groups/communities targeted in the projects and activities. highlight obvious gaps in the collected data which can inform future programmes geographically map out projects and other activities which record COVID-19 stories and experiences across Greater London. The data provides insight into trends and patterns in COVID-19 collecting projects and activities that have been carried out in London from March 2020 to March 2021. Reflections and final suggestions on how to navigate these projects and activities for specific next steps in the Community-Led Recovery Programme, targeted missions, suggestions etc. will be discussed later in this report. In particular, this report provides information relevant to the London Community Story (LCS) Programme, one of the two strands of the Community-Led Recovery programme. Alongside this report is a dataset outlining 160 COVID-19 collecting projects that took place in London. The dataset gives project names, boroughs, material types, collecting organisation type and organisation names. We encourage you to use this dataset as a starting point and then do your own additional research on the 160 projects. If you are aware of a project that has not been included, please let us know and we can add it.

  13. COVID-19 by country

    • kaggle.com
    zip
    Updated Sep 13, 2021
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    Juan Carlos Santiago Culebras (2021). COVID-19 by country [Dataset]. https://www.kaggle.com/jcsantiago/covid19-by-country-with-government-response
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    zip(6766232 bytes)Available download formats
    Dataset updated
    Sep 13, 2021
    Authors
    Juan Carlos Santiago Culebras
    License

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

    Description

    Context

    Within the current response of a pandemic caused by the SARS-CoV-2 coronavirus, which in turn causes the disease, called COVID-19. It is necessary to join forces to minimize the effects of this disease.

    Therefore, the intention of this dataset is to save data scientists time:

    • Gather the data at the country level, encoding the country with its ISO code to allow easy access to other data
    • Perform pre-processing of data, calculations of increments and other indicators that can facilitate modeling.
    • Add the response of the governments over time so that it can be taken into account in the modeling.
    • Daily update.

    This dataset is not intended to be static, so suggestions for expanding it are welcome. If someone considers it important to add information, please let me know.

    Content

    The data contained in this dataset comes mainly from the following sources:

    Source: Center for Systems Science and Engineering (CSSE) at Johns Hopkins University https://github.com/CSSEGISandData/COVID-19 Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

    Source: OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker Hale, Thomas and Samuel Webster (2020). Oxford COVID-19 Government Response Tracker. Data use policy: Creative Commons Attribution CC BY standard.

    The original data is updated daily.

    The features it includes are:

    • Country Name

    • Country Code ISO 3166 Alpha 3

    • Date

    • Incidence data:

      • confirmed
      • deaths
      • recoveries
    • Daily increments:

      • confirmed_inc
      • deaths_inc
      • recoveries_inc
    • Empirical Contagion Rate - ECR

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3508582%2F3e90ecbcdf76dfbbee54a21800f5e0d6%2FECR.jpg?generation=1586861653126435&alt=media" alt="">

    • GOVERNMENT RESPONSE TRACKER - GRTStringencyIndex

      OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER - Stringency Index

    • Indices from Start Contagion

      • Days since the first case of contagion is overcome
      • Days since 100 cases are exceeded
    • Percentages over the country's population:

      • confirmed_PopPct
      • deaths_PopPct
      • recoveries_PopPct

    The method of obtaining the data and its transformations can be seen in the notebook:

    Notebook COVID-19 Data by country with Government Response

    Photo by Markus Spiske on Unsplash

  14. Perceived loneliness, anxiety and depression symptomology before, during and...

    • figshare.com
    xlsx
    Updated Jan 29, 2025
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    Katie Barfoot (2025). Perceived loneliness, anxiety and depression symptomology before, during and after COVID-19 lockdowns in England [Dataset]. http://doi.org/10.6084/m9.figshare.28303919.v2
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    xlsxAvailable download formats
    Dataset updated
    Jan 29, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Katie Barfoot
    License

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

    Area covered
    England
    Description

    Objectives: This study investigated perceived loneliness, anxiety, and depression among young adults in the UK across five timepoints: pre-pandemic (December 2019), two coronavirus disease (COVID-19) lockdowns (March–June 2020, January–April 2021), and two post-lockdown phases (November–December 2021, May 2022). It aimed to assess mental health resilience, defined as a return to baseline levels post-lockdown, and identify critical timepoints where loneliness predicted mental health outcomes.Methods: A total of 158 participants (aged 18–82, predominantly under 25) completed online questionnaires measuring mental health (Patient Health Questionnaire-8 (PHQ-8); General Anxiety Disorder-7 (GAD-7)) and loneliness (DeJong Gierveld Loneliness Scale) at two data collection points, under a cross-sectional design. Retrospective data were collected for pre-pandemic and lockdown periods, while prospective data were gathered post-lockdown. Linear mixed models and regression analyses were used to examine changes in mental health and loneliness over time and to identify predictive relationships.Results: Loneliness and mental health significantly deteriorated during lockdowns, with depression and anxiety scores worsening from pre-pandemic levels. Partial recovery was observed post-lockdown, but scores remained above baseline. Loneliness emerged as a key predictor of mental health outcomes, particularly during post-lockdown phases. The immediate post-lockdown period was identified as a critical window for interventions.Conclusions: COVID-19 lockdowns were associated with heightened loneliness and mental health challenges, with sustained effects post-lockdown. Timely interventions targeting loneliness, especially after periods of social restriction, are essential to mitigate long-term mental health impacts and inform future responses to global crises.

  15. COVID-19 Health Inequalities Monitoring in England tool (CHIME)

    • gov.uk
    • s3.amazonaws.com
    Updated May 24, 2023
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    Office for Health Improvement and Disparities (2023). COVID-19 Health Inequalities Monitoring in England tool (CHIME) [Dataset]. https://www.gov.uk/government/statistics/covid-19-health-inequalities-monitoring-in-england-tool-chime
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    Dataset updated
    May 24, 2023
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Office for Health Improvement and Disparities
    Area covered
    England
    Description

    The COVID-19 Health Inequalities Monitoring in England (CHIME) tool brings together data relating to the direct impacts of coronavirus (COVID-19) on factors such as mortality rates, hospital admissions, confirmed cases and vaccinations.

    By presenting inequality breakdowns - including by age, sex, ethnic group, level of deprivation and region - the tool provides a single point of access to:

    • show how inequalities have changed during the course of the pandemic and what the current cumulative picture is
    • bring together data in one tool to enable users to access and use the intelligence more easily
    • provide indicators with a consistent methodology across different data sets to facilitate understanding
    • support users to identify and address inequalities within their areas, and identify priority areas for recovery

    In the March 2023 update, data has been updated for deaths, hospital admissions and vaccinations. Data on inequalities in vaccination uptake within upper tier local authorities has been added to the tool for the first time. This replaces data for lower tier local authorities, published in December 2022, allowing the reporting of a wider range of inequality breakdowns within these areas.

    Updates to the CHIME tool are paused pending the results of a review of the content and presentation of data within the tool. The tool has not been updated since the 16 March 2023.

    Please send any questions or comments to PHA-OHID@dhsc.gov.uk

  16. z

    A patient-centric modelling framework captures recovery from SARS-CoV-2...

    • zenodo.org
    csv
    Updated Nov 4, 2022
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    Hélène Ruffieux; Aimee L. Hanson; Samantha Lodge; Nathan G. Lawler; Luke Whiley; Nicola Gray; Tui H. Nolan; Laura Bergamaschi; Federica Mescia; Lorinda Turner; Aloka de Sa; Victoria S. Pelly; The Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) BioResource COVID-19 Collaboration; Prasanti Kotagiri; Nathalie Kingston; John R. Bradley; Elaine Holmes; Julien Wist; Jeremy K. Nicholson; Paul A. Lyons; Kenneth G.C. Smith; Sylvia Richardson; Glenn R. Bantug; Christoph Hess; Hélène Ruffieux; Aimee L. Hanson; Samantha Lodge; Nathan G. Lawler; Luke Whiley; Nicola Gray; Tui H. Nolan; Laura Bergamaschi; Federica Mescia; Lorinda Turner; Aloka de Sa; Victoria S. Pelly; The Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) BioResource COVID-19 Collaboration; Prasanti Kotagiri; Nathalie Kingston; John R. Bradley; Elaine Holmes; Julien Wist; Jeremy K. Nicholson; Paul A. Lyons; Kenneth G.C. Smith; Sylvia Richardson; Glenn R. Bantug; Christoph Hess (2022). A patient-centric modelling framework captures recovery from SARS-CoV-2 infection [Dataset]. http://doi.org/10.5281/zenodo.7277164
    Explore at:
    csvAvailable download formats
    Dataset updated
    Nov 4, 2022
    Dataset provided by
    Zenodo
    Authors
    Hélène Ruffieux; Aimee L. Hanson; Samantha Lodge; Nathan G. Lawler; Luke Whiley; Nicola Gray; Tui H. Nolan; Laura Bergamaschi; Federica Mescia; Lorinda Turner; Aloka de Sa; Victoria S. Pelly; The Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) BioResource COVID-19 Collaboration; Prasanti Kotagiri; Nathalie Kingston; John R. Bradley; Elaine Holmes; Julien Wist; Jeremy K. Nicholson; Paul A. Lyons; Kenneth G.C. Smith; Sylvia Richardson; Glenn R. Bantug; Christoph Hess; Hélène Ruffieux; Aimee L. Hanson; Samantha Lodge; Nathan G. Lawler; Luke Whiley; Nicola Gray; Tui H. Nolan; Laura Bergamaschi; Federica Mescia; Lorinda Turner; Aloka de Sa; Victoria S. Pelly; The Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) BioResource COVID-19 Collaboration; Prasanti Kotagiri; Nathalie Kingston; John R. Bradley; Elaine Holmes; Julien Wist; Jeremy K. Nicholson; Paul A. Lyons; Kenneth G.C. Smith; Sylvia Richardson; Glenn R. Bantug; Christoph Hess
    License

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

    Description

    The biology driving individual patient responses to SARS-CoV-2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year post-disease onset, from 215 SARS-CoV-2 infected subjects with differing disease severities. Our analyses revealed distinct “systemic recovery” profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter- and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery at the patient level, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app, designed to test our findings prospectively.

  17. Daily domestic transport use by mode

    • gov.uk
    Updated Nov 12, 2025
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    Department for Transport (2025). Daily domestic transport use by mode [Dataset]. https://www.gov.uk/government/statistics/transport-use-during-the-coronavirus-covid-19-pandemic
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    Dataset updated
    Nov 12, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Transport
    Description

    Our statistical practice is regulated by the Office for Statistics Regulation (OSR). OSR sets the standards of trustworthiness, quality and value in the Code of Practice for Statistics that all producers of official statistics should adhere to. You are welcome to contact us directly by emailing transport.statistics@dft.gov.uk with any comments about how we meet these standards.

    These statistics on transport use are published monthly.

    For each day, the Department for Transport (DfT) produces statistics on domestic transport:

    • road traffic in Great Britain
    • rail passenger journeys in Great Britain
    • Transport for London (TfL) tube and bus routes
    • bus travel in Great Britain (excluding London)

    The associated methodology notes set out information on the data sources and methodology used to generate these headline measures.

    From September 2023, these statistics include a second rail usage time series which excludes Elizabeth Line service (and other relevant services that have been replaced by the Elizabeth line) from both the travel week and its equivalent baseline week in 2019. This allows for a more meaningful like-for-like comparison of rail demand across the period because the effects of the Elizabeth Line on rail demand are removed. More information can be found in the methodology document.

    The table below provides the reference of regular statistics collections published by DfT on these topics, with their last and upcoming publication dates.

    ModePublication and linkLatest period covered and next publication
    Road trafficRoad traffic statisticsFull annual data up to December 2024 was published in June 2025.

    Quarterly data up to March 2025 was published June 2025.
    Rail usageThe Office of Rail and Road (ORR) publishes a range of statistics including passenger and freight rail performance and usage. Statistics are available at the https://dataportal.orr.gov.uk/">ORR website.

    Statistics for rail passenger numbers and crowding on weekdays in major cities in England and Wales are published by DfT.
    ORR’s latest quarterly rail usage statistics, covering January to March 2025, was published in June 2025.

    DfT’s most recent annual passenger numbers and crowding statistics for 2024 were published in July 2025.
    Bus usageBus statisticsThe most recent annual publication covered the year ending March 2024.

    The most recent quarterly publication covered April to June 2025.
    TfL tube and bus usageData on buses is covered by the section above. https://tfl.gov.uk/status-updates/busiest-times-to-travel">Station level business data is available.
    Cross Modal and journey by purposeNational Travel Survey2024 calendar year data published in August 2025.

  18. COVID-19 Community Mobility Reports

    • google.com
    • google.com.tr
    • +4more
    csv, pdf
    Updated Oct 17, 2022
    + more versions
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    Google (2022). COVID-19 Community Mobility Reports [Dataset]. https://www.google.com/covid19/mobility/
    Explore at:
    csv, pdfAvailable download formats
    Dataset updated
    Oct 17, 2022
    Dataset authored and provided by
    Googlehttp://google.com/
    Description

    As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

  19. u

    Healthy Ageing in Scotland: COVID-19 Impact and Recovery Study, 2021-2022

    • beta.ukdataservice.ac.uk
    Updated Sep 20, 2023
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    University of Stirling (2023). Healthy Ageing in Scotland: COVID-19 Impact and Recovery Study, 2021-2022 [Dataset]. http://doi.org/10.5255/UKDA-SN-9130-1
    Explore at:
    Dataset updated
    Sep 20, 2023
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    University of Stirling
    Area covered
    Scotland
    Description

    The Healthy Ageing in Scotland (HAGIS): COVID-19 Impact and Recovery Study, 2021-2022 is a multidisciplinary large-scale study of older adults (aged 50 and over) living in Scotland. The study was established to explore the spectrum of COVID-19 concerns in older adults and its impact on their willingness to (re)engage across health, social, and economic domains as Scotland's economy and society emerged from the pandemic. The survey data were collected between October 2021 and January 2022 using electronic, postal self-completion interviews and telephone-assisted personal interviews. From a target sample of 15,674 older adults, drawn from two existing Scottish longitudinal studies and a predefined panel, 3,373 individuals (59 percent women and 41 percent men) completed the survey.

    The data provide a wealth of information on older adults' socio-demographics,
    COVID-19-induced fear, worries and concerns, health domains, social capital and participation, economic and consumption behaviours, return to workplace experiences and preferences.

    Further information is available HAGIS COVID-19 Impact and Recovery Study webpage.

  20. COVID-19 Impact on Travel and Tourism Social Media - Thematic Research

    • store.globaldata.com
    Updated Jun 30, 2020
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    GlobalData UK Ltd. (2020). COVID-19 Impact on Travel and Tourism Social Media - Thematic Research [Dataset]. https://store.globaldata.com/report/covid-19-impact-on-travel-and-tourism-social-media-thematic-research/
    Explore at:
    Dataset updated
    Jun 30, 2020
    Dataset provided by
    GlobalDatahttps://www.globaldata.com/
    Authors
    GlobalData UK Ltd.
    License

    https://www.globaldata.com/privacy-policy/https://www.globaldata.com/privacy-policy/

    Time period covered
    2020 - 2024
    Area covered
    Global
    Description

    Social media has been vital for consumers, suppliers, workforce and partners to remain engaged amidst this exogenous event of COVID-19.
    This thematic research report takes an in-depth look at the theme of Social Media and its impact on travel and tourism during COVID-19 affecting super-national organizations, DMO’s, airlines, lodging providers, cruise operators and travel intermediaries. This report analyzes the major impacts that may become longstanding and then presents an array of case studies demonstrating the creative and innovative ways companies and organizations have acted during this time.
    “Social media has most openly been utilized as a tool for travel businesses and DMO’s to maintain contact with consumers worldwide – to generate wanderlust and look towards recovery when travel is once again possible. Even though the battle with COVID-19 is now beginning to lessen and restrictions are easing, it is clear there will be long-standing impacts on consumer behavior and social media is one of the major themes that will drive future changes”. – Johanna Bonhill-Smith, Travel & Tourism Associate Analyst, GlobalData. Read More

Share
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Click to copy link
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Johns Hopkins Center for Systems Science and Engineering (2023). UK Coronavirus Recoveries [Dataset]. https://ycharts.com/indicators/uk_coronavirus_recoveries

UK Coronavirus Recoveries

Explore at:
htmlAvailable download formats
Dataset updated
Mar 10, 2023
Dataset provided by
YCharts
Authors
Johns Hopkins Center for Systems Science and Engineering
License

https://www.ycharts.com/termshttps://www.ycharts.com/terms

Time period covered
Jan 22, 2020 - Mar 9, 2023
Area covered
United Kingdom
Variables measured
UK Coronavirus Recoveries
Description

View daily updates and historical trends for UK Coronavirus Recoveries. from United Kingdom. Source: Johns Hopkins Center for Systems Science and Engineer…

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