11th January 2020 Change to vaccination data made available by UK gov - now just cumulative number of vaccines delivered are available for both first and second doses. For the devolved nations the cumulative totals are available for the dates from when given, however for the UK as a whole the total doses given is just on the last date of the index, regardless of when those vaccines were given.

4th January 2020 VACCINATION DATA ADDED - New and Cumulative First Dose Vaccination Data added to UK_National_Total_COVID_Dataset.csv and UK_Devolved_Nations_COVID_Dataset.csv

2nd December 2020:

NEW population, land area and population density data added in file NEW_Official_Population_Data_ONS_mid-2019.csv. This data is scraped from the Office for National Statistics and covers the UK, devolved UK nations, regions and local authorities (boroughs).

20th November 2020:

With European governments struggling with a 'second-wave' of rising cases, hospitalisations and deaths resulting from the SARS-CoV-2 virus (COVID-19), I wanted to make a comparative analysis between the data coming out of major European nations since the start of the pandemic.

I started by creating a Sweden COVID-19 dataset and now I'm looking at my own country, the United Kingdom.

The data comes from https://coronavirus.data.gov.uk/ and I used the Developer's Guide to scrape the data, so it was a fairly simple process. The notebook that scapes the data is public and can be found here. Further information about data collection methodologies and definitions can be found here.

The data includes the overall numbers for the UK as a whole, the numbers for each of the devolved UK nations (Eng, Sco, Wal & NI), English Regions and Upper Tier Local Authorities (UTLA) for all of the UK (what we call Boroughs). I have also included a small table with the populations of the 4 devolved UK nations, used to calculate the death rates per 100,000 population.

As I've said for before - I am not an Epidemiologist, Sociologist or even a Data Scientist. I am actually a Mechanical Engineer! The objective here is to improve my data science skills and maybe provide some useful data to the wider community.

Any questions, comments or suggestions are most welcome! I am open to requests and collaborations! Stay Safe!

Data on activities that respondents have been doing more of since the start of the coronavirus pandemic and will keep doing after the end of the pandemic. Data are based on the COVID-19 module of the OPN, collected between 10 and 14 March 2021.

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).

United Kingdom recorded 24603076 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, United Kingdom reported 225324 Coronavirus Deaths. This dataset includes a chart with historical data for the United Kingdom Coronavirus Cases.

Due to changes in the collection and availability of data on COVID-19, this website will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard and the UKHSA GLA Covid-19 Mobility Report Since March 2020, London has seen many different levels of restrictions - including three separate lockdowns and many other tiers/levels of restrictions, as well as easing of restrictions and even measures to actively encourage people to go to work, their high streets and local restaurants. This reports gathers data from a number of sources, including google, apple, citymapper, purple wifi and opentable to assess the extent to which these levels of restrictions have translated to a reductions in Londoners' movements. The data behind the charts below come from different sources. None of these data represent a direct measure of how well people are adhering to the lockdown rules - nor do they provide an exhaustive data set. Rather, they are measures of different aspects of mobility, which together, offer an overall impression of how people Londoners are moving around the capital. The information is broken down by use of public transport, pedestrian activity, retail and leisure, and homeworking. Public Transport For the transport measures, we have included data from google, Apple, CityMapper and Transport for London. They measure different aspects of public transport usage - depending on the data source. Each of the lines in the chart below represents a percentage of a pre-pandemic baseline. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Citymapper Citymapper mobility index 2021-09-05 Compares trips planned and trips taken within its app to a baseline of the four weeks from 6 Jan 2020 7.9% 28% 19% Google Google Mobility Report 2022-10-15 Location data shared by users of Android smartphones, compared time and duration of visits to locations to the median values on the same day of the week in the five weeks from 3 Jan 2020 20.4% 40% 27% TfL Bus Transport for London 2022-10-30 Bus journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 34% 24% TfL Tube Transport for London 2022-10-30 Tube journey ‘taps' on the TfL network compared to same day of the week in four weeks starting 13 Jan 2020 - 30% 21% Pedestrian activity With the data we currently have it's harder to estimate pedestrian activity and high street busyness. A few indicators can give us information on how people are making trips out of the house: activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Walking Apple Mobility Index 2021-11-09 estimates the frequency of trips made on foot compared to baselie of 13 Jan '20 22% 47% 36% Parks Google Mobility Report 2022-10-15 Frequency of trips to parks. Changes in the weather mean this varies a lot. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail & Rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 30% 55% 41% Retail and recreation In this section, we focus on estimated footfall to shops, restaurants, cafes, shopping centres and so on. activity Source Latest Baseline Min value in Lockdown 1 Min value in Lockdown 2 Min value in Lockdown 3 Grocery/pharmacy Google Mobility Report 2022-10-15 Estimates frequency of trips to grovery shops and pharmacies. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Retail/rec Google Mobility Report 2022-10-15 Estimates frequency of trips to shops/leisure locations. Compared to baseline of 5 weeks from 3 Jan '20 32% 55.00% 45.000% Restaurants OpenTable State of the Industry 2022-02-19 London restaurant bookings made through OpenTable 0% 0.17% 0.024% Home Working The Google Mobility Report estimates changes in how many people are staying at home and going to places of work compared to normal. It's difficult to translate this into exact percentages of the population, but changes back towards ‘normal' can be seen to start before any lockdown restrictions were lifted. This value gives a seven day rolling (mean) average to avoid it being distorted by weekends and bank holidays. name Source Latest Baseline Min/max value in Lockdown 1 Min/max value in Lockdown 2 Min/max value in Lockdown 3 Residential Google Mobility Report 2022-10-15 Estimates changes in how many people are staying at home for work. Compared to baseline of 5 weeks from 3 Jan '20 131% 119% 125% Workplaces Google Mobility Report 2022-10-15 Estimates changes in how many people are going to places of work. Compared to baseline of 5 weeks from 3 Jan '20 24% 54% 40% Restriction Date end_date Average Citymapper Average homeworking Work from home advised 17 Mar '20 21 Mar '20 57% 118% Schools, pubs closed 21 Mar '20 24 Mar '20 34% 119% UK enters first lockdown 24 Mar '20 10 May '20 10% 130% Some workers encouraged to return to work 10 May '20 01 Jun '20 15% 125% Schools open, small groups outside 01 Jun '20 15 Jun '20 19% 122% Non-essential businesses re-open 15 Jun '20 04 Jul '20 24% 120% Hospitality reopens 04 Jul '20 03 Aug '20 34% 115% Eat out to help out scheme begins 03 Aug '20 08 Sep '20 44% 113% Rule of 6 08 Sep '20 24 Sep '20 53% 111% 10pm Curfew 24 Sep '20 15 Oct '20 51% 112% Tier 2 (High alert) 15 Oct '20 05 Nov '20 49% 113% Second Lockdown 05 Nov '20 02 Dec '20 31% 118% Tier 2 (High alert) 02 Dec '20 19 Dec '20 45% 115% Tier 4 (Stay at home advised) 19 Dec '20 05 Jan '21 22% 124% Third Lockdown 05 Jan '21 08 Mar '21 22% 122% Roadmap 1 08 Mar '21 29 Mar '21 29% 118% Roadmap 2 29 Mar '21 12 Apr '21 36% 117% Roadmap 3 12 Apr '21 17 May '21 51% 113% Roadmap out of lockdown: Step 3 17 May '21 19 Jul '21 65% 109% Roadmap out of lockdown: Step 4 19 Jul '21 07 Nov '22 68% 107%

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. 

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech (speech not available in open access version) were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Contents

• participant_metadata.csv row-wise, participant identifier indexed information on participant demographics and health status. Please see A large-scale and PCR-referenced vocal audio dataset for COVID-19 for a full description of the dataset.
• audio_metadata.csv row-wise, participant identifier indexed information on three recorded audio modalities, including audio filepaths. Please see A large-scale and PCR-referenced vocal audio dataset for COVID-19 for a full description of the dataset.
• train_test_splits.csv row-wise, participant identifier indexed information on train test splits for the following sets: 'Randomised' train and test set, Standard' train and test set, Matched' train and test sets, 'Longitudinal' test set and 'Matched Longitudinal' test set. Please see Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers for a full description of the train test splits.
• audio/ directory containing all the recordings in .wav format
  • Due to the large size of the dataset, to assist with ease of download, the audio files have been zipped into covid_data.z{ip, 01-24}. This enables the dataset to be downloaded in short periods, reducing the chances of a dropped internet connection scuppering progress. To unzip, first, ensure that all zip files are in the same directory. Then run the command 'unzip covid_data.zip' or right-click on 'covid_data.zip' and use a programme such as 'The Unarchiver' to open the file.
  • Once extracted, to check the validity of the download, please run the 'python Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers/data-paper/unit-tests.py. All tests should pass with no exceptions. Please clone the GitHub repo detailed below.
• README.md full dataset descriptor.
• DataDictionary_UKCOVID19VocalAudioDataset_OpenAccess.xlsx descriptor of each dataset attribute with the percentage coverage.

Code Base

The accompanying code can be found here: https://github.com/alan-turing-institute/Turing-RSS-Health-Data-Lab-Biomedical-Acoustic-Markers

Citations:

Please cite.

@article{coppock2022,

author = {Coppock, Harry and Nicholson, George and Kiskin, Ivan and Koutra, Vasiliki and Baker, Kieran and Budd, Jobie and Payne, Richard and Karoune, Emma and Hurley, David and Titcomb, Alexander and Egglestone, Sabrina and Cañadas, Ana Tendero and Butler, Lorraine and Jersakova, Radka and Mellor, Jonathon and Patel, Selina and Thornley, Tracey and Diggle, Peter and Richardson, Sylvia and Packham, Josef and Schuller, Björn W. and Pigoli, Davide and Gilmour, Steven and Roberts, Stephen and Holmes, Chris},

title = {Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers},

journal = {arXiv},

year = {2022},

doi = {10.48550/ARXIV.2212.08570},

url = {https://arxiv.org/abs/2212.08570},

}

@article{budd2022,

author={Jobie Budd and Kieran Baker and Emma Karoune and Harry Coppock and Selina Patel and Ana Tendero Cañadas and Alexander Titcomb and Richard Payne and David Hurley and Sabrina Egglestone and Lorraine Butler and George Nicholson and Ivan Kiskin and Vasiliki Koutra and Radka Jersakova and Peter Diggle and Sylvia Richardson and Bjoern Schuller and Steven Gilmour and Davide Pigoli and Stephen Roberts and Josef Packham Tracey Thornley Chris Holmes},

title={A large-scale and PCR-referenced vocal audio dataset for COVID-19},

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.07738}

}

@article{Pigoli2022,

author={Davide Pigoli and Kieran Baker and Jobie Budd and Lorraine Butler and Harry Coppock and Sabrina Egglestone and Steven G.\ Gilmour and Chris Holmes and David Hurley and Radka Jersakova and Ivan Kiskin and Vasiliki Koutra and George Nicholson and Joe Packham and Selina Patel and Richard Payne and Stephen J.\ Roberts and Bj\"{o}rn W.\ Schuller and Ana Tendero-Ca$\tilde{n}$adas and Tracey Thornley and Alexander Titcomb},

title={Statistical Design and Analysis for Robust Machine Learning: A Case Study from Covid-19},

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.08571}

}

The Dublin Core™ Metadata Initiative

- Title: The UK COVID-19 Vocal Audio Dataset, Open Access Edition.

- Creator: The UK Health Security Agency (UKHSA) in collaboration with The Turing-RSS Health Data Lab.

- Subject: COVID-19, Respiratory symptom, Other audio, Cough, Asthma, Influenza.

- Description: The UK COVID-19 Vocal Audio Dataset Open Access Edition is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs and exhalations were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset Open Access Edition represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

- Publisher: The UK Health Security Agency (UKHSA).

- Contributor: The UK Health Security Agency (UKHSA) and The Alan Turing Institute.

- Date: 2021-03/2022-03

- Type: Dataset

- Format: Waveform Audio File Format audio/wave, Comma-separated values text/csv

- Identifier: 10.5281/zenodo.10043978

- Source: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to Accessing UKHSA protected data.

- Language: eng

- Relation: The UK COVID-19 Vocal Audio Dataset Protected Edition, accessed via application to Accessing UKHSA protected data.

- Coverage: United Kingdom, 2021-03/2022-03.

- Rights: Open Government Licence version 3 (OGL v.3), © Crown Copyright UKHSA 2023.

- accessRights: When you use this information under the Open Government Licence, you should include the following attribution: The UK COVID-19 Vocal Audio Dataset Open Access Edition, UK Health Security Agency, 2023, licensed under the Open Government Licence v3.0 and cite the papers detailed above.

This briefing presents evidence on the socio-economic impact of COVID-19 on London and Londoners​ Topics included in the briefing focus on recent data releases published in the preceding months that tell us how social policy issues are evolving in London since the start of the COVID-19 pandemic For more on the health and demographic impacts see the Demographic Impact Briefing and for labour market impacts see Labour Market Analysis. A page linking to all Covid-19 related data and analyses can be found here.

Context

• Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus SARS-CoV2.
• Person to person transmission occurs via several different modes, mainly when people are in close proximity to one another through respiratory droplets.
• Mitigations by various countries have been put into place to limit the spread of this virus.

Content

• ConfirmedCasesByDateReported.csv
• ConfirmedCasesBySpecimenDate.csv
• Deaths.csv
• PatientNewAdmissions.csv
• PatientsInHospital.csv
• PatientsMVBeds.csv
• PCRTesting.csv
• Vaccinations.csv
• VaccinationsDaily.csv

Acknowledgements and Collection Methodology

Data downloaded from https://coronavirus.data.gov.uk

Version

• Version 11 - 25 - Various Files Updated.
• Version 10 - Added VaccinationsDaily File, data upto and including the 20th Jan 2021.
• Version 9 - Updated Deaths file, data upto and including the 20th Jan 2021.
• Version 8 - Updated ConfirmedCasesByDateReported and ConfirmedCasesBySpecimenDate files, data upto and including the 17th to 19th Jan 2021 respectively.
• Version 7 - Updated PatientNewAdmissions, PatientsInHospital and PatientsMVBeds files, data upto and including the 12th to 15th Jan 2020 depending on file.
• Version 6 - Updated PCR Testing file, data upto and including the 14th Jan 2021.
• Version 4 - Updated Vaccinations file, data upto and including the 3rd Jan 2021.
• Version 3 - Updated to include data unto and including the 28th December 2020. Additionally added data on the progress of Vaccinations.
• Version 2 - Updated to include data unto and including the 3rd November 2020.

NHS UK - COVID-19 Daily Deaths

This section contains information on deaths of patients who have died in hospitals in England and had tested positive for COVID-19 at time of death. All deaths are recorded against the date of death rather than the date the deaths were announced. Interpretation of the figures should take into account the fact that totals by date of death, particularly for most recent days, are likely to be updated in future releases. For example as deaths are confirmed as testing positive for COVID-19, as more post-mortem tests are processed and data from them are validated. Any changes are made clear in the daily files.

These figures do not include deaths outside hospital, such as those in care homes. This approach makes it possible to compile deaths data on a daily basis using up to date figures.

Dataset Content

These figures will be updated at 2pm each day and include confirmed cases reported at 5pm the previous day. Confirmation of COVID-19 diagnosis, death notification and reporting in central figures can take up to several days and the hospitals providing the data are under significant operational pressure. This means that the totals reported at 5pm on each day may not include all deaths that occurred on that day or on recent prior days.

The original dataset is sourced directly from the NHS source site, this original dataset is then cleaned and converted to a csv format available for inclusion into a Kaggle notebook.

There are 3 files considered within the data :- 1. Fatalities_by_age_uk 2.Fatalities_by_region_uk 3.Fatalities_by_trust_uk

Data runs from March 1st up to the current day. Any discrepancies will be outlined. The first is cumulative for any previous days leading up to of relevance. The following days are not cumulative and represent the updated value for the date under consideration.

A start kernel is provided to demonstrate using the dataset.

Citations

This dataset is sourced from the NHS statistical work areas:- https://www.england.nhs.uk/statistics/statistical-work-areas/

This dataset has been sourced and provided to aid in the following competition:- https://www.kaggle.com/c/covid19-global-forecasting-week-4

United Kingdom recorded 344 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, United Kingdom reported 148003 Coronavirus Deaths. This dataset includes a chart with historical data for the United Kingdom Coronavirus Recovered.

There is an urgent need to understand the factors that mediate and mitigate the impact of the Covid-19 pandemic on behaviour and wellbeing. However, the onset of the outbreak was unexpected and the rate of acceleration so rapid as to preclude the planning of studies that can address these critical issues. Coincidentally, in January 2020, just prior to the outbreak in the UK, my team launched a study that collected detailed (~50 minute) cognitive and questionnaire assessments from >200,000 members of the UK public as part of a collaboration with the BBC. This placed us in a unique position to examine how aspects of mental health subsequently changed as the pandemic arrived in the UK. Therefore, we collected data from a further ~120,000 people in May, including additional detailed measures of self-perceived pandemic impact and free text descriptions of the main positives, negatives and pragmatic measures that people found helped them maintain their wellbeing.

In this data archive, we include the survey data from January and May 2020 examining impact of Covid-19 on mood, wellbeing and behaviour in the UK population. This data is reported in a preprint article, where we apply a novel fusion of psychometric, multivariate and machine learning analyses to this unique dataset, in order to address some of the most pressing questions regarding wellbeing during the pandemic in a data-driven manner. The preprint is available on this URL. https://www.medrxiv.org/content/10.1101/2020.06.18.20134635v1

Due to changes in the collection and availability of data on COVID-19 this page will no longer be updated. The webpage will no longer be available as of 11 May 2023. On-going, reliable sources of data for COVID-19 are available via the COVID-19 dashboard, Office for National Statistics, and the UKHSA

This page provides a weekly summary of data on deaths related to COVID-19 published by NHS England and the Office for National Statistics. More frequent reporting on COVID-19 deaths is now available here, alongside data on cases, hospitalisations, and vaccinations. This update contains data on deaths related to COVID-19 from:

NHS England COVID-19 Daily Deaths - last updated on 28 June 2022 with data up to and including 27 June 2022.


ONS weekly deaths by Local Authority - last updated on 16 August 2022 with data up to and including 05 August 2022.

Summary notes about each these sources are provided at the end of this document.

Note on interpreting deaths data: statistics from the available sources differ in definition, timing and completeness. It is important to understand these differences when interpreting the data or comparing between sources.

Weekly Key Points

An additional 24 deaths in London hospitals of patients who had tested positive for COVID-19 and an additional 5 where COVID-19 was mentioned on the death certificate were announced in the week ending 27 June 2022. This compares with 40 and 3 for the previous week. A total of 306 deaths in hospitals of patients who had tested positive for COVID-19 and 27 where COVID-19 was mentioned on the death certificate were announced for England as whole. This compares with 301 and 26 for the previous week. The total number of COVID-19 deaths reported in London hospitals of patients who had tested positive for COVID-19 is now 19,102. The total number of deaths in London hospitals where COVID-19 was mentioned on the death certificate is now 1,590. This compares to figures of 119,237 and 8,197 for English hospitals as a whole. Due to the delay between death occurrence and reporting, the estimated number of deaths to this point will be revised upwards over coming days These figures do not include deaths that occurred outside of hospitals. Data from ONS has indicated that the majority (79%) of COVID-19 deaths in London have taken place in hospitals.

Recently announced deaths in Hospitals

21 June 22 June 23 June 24 June 25 June 26 June 27 June London No positive test 0 0 1 4 0 0 0 London Positive test 3 7 2 10 0 0 2 Rest of England No positive test 2 6 4 4 0 0 6 Rest of England Positive test 47 49 41 58 6 0 81

16 May 23 May 30 May 06 June 13 June 20 June 27 June London No positive test 14 3 4 0 4 3 5 London Positive test 45 34 55 20 62 40 24 Rest of England No positive test 41 58 33 23 47 23 22 Rest of England Positive test 456 375 266 218 254 261 282 Deaths by date of occurrence

21 June 22 June 23 June 24 June 25 June 26 June 27 June London 20,683 20,686 20,690 20,691 20,692 20,692 20,692 Rest of England 106,604 106,635 106,679 106,697 106,713 106,733 106,742 Interpreting the data The data published by NHS England are incomplete due to:

delays in the occurrence and subsequent reporting of deaths deaths occurring outside of hospitals not being included

The total deaths reported up to a given point are therefore less than the actual number that have occurred by the same point. Delays in reporting NHS provide the following guidance regarding the delay between occurrence and reporting of deaths: Confirmation of COVID-19 diagnosis, death notification and reporting in central figures can take up to several days and the hospitals providing the data are under significant operational pressure. This means that the totals reported at 5pm on each day may not include all deaths that occurred on that day or on recent prior days. The data published by NHS England for reporting periods from April 1st onward includes both date of occurrence and date of reporting and so it is possible to illustrate the distribution of these reporting delays. This data shows that approximately 10% of COVID-19 deaths occurring in London hospitals are included in the reporting period ending on the same day, and that approximately two-thirds of deaths were reported by two days after the date of occurrence.

Deaths outside of hospitals The data published by NHS England does not include deaths that occur outside of hospitals, i.e. those in homes, hospices, and care homes. ONS have published data for deaths by place of occurrence. This shows that, up to 05 August, 79% of deaths in London recorded as involving COVID-19 occurred in hospitals (this compares with 44% for all causes of death). This would suggest that the NHS England data may underestimate overall deaths from COVID-19 by around 20%.

Comparison of data sources

Note on data sources

NHS England provides numbers of patients who have died in hos

Dataset

This dataset was created by Akhat Rakishev

Contents

We examined the construct validity of a 28-item survey that was designed to measure inner wellbeing (i.e., individuals’ thoughts and feelings about what they can do and be; White et al., 2014) among individuals in (1) the Global South nation of India (n = 205), (2) the Global North nation of the United Kingdom (n = 392), and (3) the nation of Greece, which is not readily categorized as Global South or Global North (n = 354) during COVID lockdown. Using a series of multiple-group confirmatory factor analyses via LISREL 10.20 (Joreskog & Sorbom, 2019), we tested the hypothesis that a model specifying seven factors (i.e., economic confidence, agency/participation, social connections, close relationships, physical/mental health, competence/self-worth, and values/meaning as intercorrelated domains) would provide a significantly better fit to the correlational data than would a model specifying a one factor (i.e., unidimensional inner wellbeing).

A lookup file between 2020 Covid Infection Survey Geography to 2020 Countries in the United Kingdom, as at 1 October 2020. (File size - 32 KB) Field Names - CIS20CD, CTRY19CD, CTRY19NM, FIDField Types - Text, Text, Text, NumericField Lengths - 9, 9, 16FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_October_2020_to_Country_Lookup_for_the_United_Kingdom/FeatureServer

The COVID Symptom Tracker (https://covid.joinzoe.com/) mobile application was designed by doctors and scientists at King's College London, Guys and St Thomas’ Hospitals working in partnership with ZOE Global Ltd – a health science company.

This research is led by Dr Tim Spector, professor of genetic epidemiology at King’s College London and director of TwinsUK a scientific study of 15,000 identical and non-identical twins, which has been running for nearly three decades.

The dataset schema includes:

Demographic Information (Year of Birth, Gender, Height, Weight, Postcode) Health Screening Questions (Activity, Heart Disease, Diabetes, Lung Disease, Smoking Status, Kidney Disease, Chemotherapy, Immunosuppressants, Corticosteroids, Blood Pressure Medications, Previous COVID, COVID Symptoms, Needs Help, Housebound Problems, Help Availability, Mobility Aid) COVID Testing Conducted How You Feel? Symptom Description Location Information (Home, Hospital, Back From Hospital) Treatment Received The data is hosted within the SAIL Databank, a trusted research environment facilitating remote access to health, social care, and administrative data for various national organisations.

The process for requesting access to the data is dependent on your use case. SAIL is currently expediting all requests that feed directly into the response to the COVID-19 national emergency, and therefore requests from NHS or Government institutions, or organisations working alongside such care providers and policymakers to feed intelligence directly back into the national response, are being expedited with a ~48-hour governance turnaround for such applications once made. Please make enquiries using the link at the bottom of the page which will go the SAIL Databank team, or to Chris Orton at c.orton@swansea.ac.uk

SAIL is welcoming requests from other organisations and for longer-term academic study on the dataset, but please note if this is not directly relevant to the emergency research being carried out which directly interfaces with national responding agencies, there may be an access delay whilst priority use cases are serviced.

Please note: the CVST dataset in SAIL has not been updated since 01/11/2023.

This dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.

This newsletter series presents a digest of external research that the Greater London Authority (GLA) is making available for the benefit of external stakeholders in tackling the COVID-19 crisis. The City Intelligence Unit at the GLA started to produce the newsletters in April, initially as an internal product for staff in the organisation and in its functional bodies. As from 2nd June, past and current issues have been made available for download. These summaries have been prepared under challenging circumstances and to short timescales. They are not intended to be comprehensive and exhaustive and the do not represent the full body of evidence on which Mayoral Policies are or will be based. Each briefing will offer short summaries and a deep dive into one or two topics.

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.

Estimates of the risk of hospital admission for coronavirus (COVID-19) and death involving COVID-19 by vaccination status, overall and by age group, using anonymised linked data from Census 2021. Experimental Statistics.

Outcome definitions

For this analysis, we define a death as involving COVID-19 if either of the ICD-10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified) is mentioned on the death certificate. Information on cause of death coding is available in the User Guide to Mortality Statistics. We use date of occurrance rather than date of registration to give the date of the death.

We define COVID-109 hospitalisation as an inpatient episode in Hospital Episode Statistics where the primary diagnosis was COVID-19, identified by the ICD-19 codes (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified). Where an individual had experienced more than one COVID-19 hospitalisation, the earliest that occurred within the study period was used. We define the date of COVID-19 hospitalisation as the start of the hospital episode.

ICD-10 code

U07.1 :

COVID-19, virus identified

U07.2:

COVID-19, virus not identified

Vaccination status is defined by the dose and the time since the last dose received

Unvaccinated:

no vaccination to less than 21 days post first dose

First dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post first dose or less than 21 days post second dose

First dose 3+ months:

more than or equal to 91 days post first dose to less than 21 days post second dose

Second dose 21 days to 3 months:

more than or equal to 21 days post second dose to earliest of less than 91 days post second dose or less than 21 days post third dose

Second dose 3-6 months:

more than or equal to 91 days post second dose to earliest of less than 182 days post second dose or less than 21 days post third dose

Second dose 6+ months:

more than or equal to 182 days post second dose to less than 21 days post third dose

Third dose 21 days to 3 months:

more than or equal to 21 days post third dose to less than 91 days post third dose

Third dose 3+ months:

more than or equal to 91 days post third dose

Model adjustments

Three sets of model adjustments were used

Age adjusted:

age (as a natural spline)

Age, socio-demographics adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status)

Fully adjusted:

age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status), plus health-related characteristics (disability, self-reported health, care home residency, number of QCovid comorbidities (grouped), BMI category, frailty flag and hospitalisation within the last 21 days.

Age

Age in years is defined on the Census day 2021 (21 March 2021). Age is included in the model as a natural spline with boundary knots at the 10th and 90th centiles and internal knots at the 25th, 50th and 75th centiles. The positions of the knots are calculated separately for the overall model and for each age group for the stratified model.