COVID-19 is a infectious Disease which has infected more than 500 people in UK and many more people world-wide.

Acknowledgements Sincere thanks to Public Health England and Local governments. Source of Data: UK Government and Public Health UK

****Notes on the methodology**** This service shows case numbers as reported to Public Health England (PHE), matched to Administrative Geography Codes from the Office of National Statistics. Cases include people who have recovered.

Events are time-stamped on the date that PHE was informed of the new case or death.

The map shows circles that grow or shrink in line with the number of cases in that geographic area.

Data from Scotland, Wales and Northern Ireland is represented on the charts, total indicators and on the country level map layer.

Contains Ordnance Survey data © Crown copyright and database right 2020. Contains National Statistics data © Crown copyright and database right 2020.

Terms of Use No special restrictions or limitations on using the item’s content have been provided.

Dataset description

Daily official UK Covid data. The data is available per country (England, Scotland, Wales and Northern Ireland) and for different regions in England. The different regions are split into two different files as part of the data is directly gathered by the NHS (National Health Service). The files that contain the word 'nhsregion' in their name, include data related to hospitals only, such as number of admissions or number of people in respirators. The files containing the word 'region' in their name, include the rest of the data, such as number of cases, number of vaccinated people or number of tests performed per day. The next paragraphs describe the columns for the different file types.

Region files

Files related to regions (word 'region' included in the file name) have the following columns: - "date": date in YYYY-MM-DD format - "area type": type of area covered in the file (region or nation) - "area name": name of area covered in the file (region or nation name) - "daily cases": new cases on a given date - "cum cases": cumulative cases - "new deaths 28days": new deaths within 28 days of a positive test - "cum deaths 28days": cumulative deaths within 28 days of a positive test - "new deaths_60days": new deaths within 60 days of a positive test - "cum deaths 60days": cumulative deaths within 60 days of a positive test - "new_first_episode": new first episodes by date - "cum_first_episode": cumulative first episodes by date - "new_reinfections": new reinfections by specimen data - "cum_reinfections": cumualtive reinfections by specimen data - "new_virus_test": new virus tests by date - "cum_virus_test": cumulative virus tests by date - "new_pcr_test": new PCR tests by date - "cum_pcr_test": cumulative PCR tests by date - "new_lfd_test": new LFD tests by date - "cum_lfd_test": cumulative LFD tests by date - "test_roll_pos_pct": percentage of unique case positivity by date rolling sum - "test_roll_people": unique people tested by date rolling sum - "new first dose": new people vaccinated with a first dose - "cum first dose": cumulative people vaccinated with a first dose - "new second dose": new people vaccinated with a first dose - "cum second dose": cumulative people vaccinated with a first dose - "new third dose": new people vaccinated with a booster or third dose - "cum third dose": cumulative people vaccinated with a booster or third dose

Country files

Files related to countries (England, Northern Ireland, Scotland and Wales) have the above columns and also: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

NHS Region files

Files related to nhsregion (word 'nhsregion' included in the file name) have the following columns: - "new admissions": new admissions, - "cum admissions": cumulative admissions, - "hospital cases": patients in hospitals, - "ventilator beds": COVID occupied mechanical ventilator beds - "trans_rate_min": minimum transmission rate (R) - "trans_rate_max": maximum transmission rate (R) - "trans_growth_min": transmission rate growth min - "trans_growth_max": transmission rate growth max

It's worth noting that the dataset hasn't been cleaned and it needs cleaning. Also, different files have different null columns. This isn't an error in the dataset but the way different countries and regions report the data.

Pre-existing conditions of people who died due to COVID-19, broken down by country, broad age group, and place of death occurrence, usual residents of England and Wales.

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!

Antibody data, by UK country and age, from the Coronavirus (COVID-19) Infection Survey.

The number of deaths registered in England and Wales due to and involving coronavirus (COVID-19). Breakdowns include age, sex, region, local authority, Middle-layer Super Output Area (MSOA), indices of deprivation and place of death. Includes age-specific and age-standardised mortality rates.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

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%

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

This dashboard shows the number of people in either a Local Authority or Neighbourhood who have had their symptoms assessed over the phone by NHS 111 and 999, where coronavirus (COVID-19) was suspected.

The purpose of this dataset is to understand the prevalence of the coronavirus in the UK population, using longitudinal data and including not only cross-sectional data but the inclusion of an antibody test for a sub-sample of people. Demographic information is also included allowing for analyse by different variables to identify patterns and trends.

Participants have three options open to them; can have just have one visit, can have a visit every week for a month or, can have a visit every week for a month and then continue to have visits every month for one year in total from when you joined the study. This is entirely voluntary.

At each visit a field worker conducts a questionnaire, and supervises swab tests. A proportion of visits also include a blood sample being taken. The swab and blood samples are tested at laboratories.

The overall purpose of this study is to understand how many people across the UK have or may already have had the coronavirus. This will help the government manage the pandemic moving forwards.

The COVID-19 Community Infection Survey includes information on: • how many people across England and Wales (extending to Scotland and Northern Ireland) test positive for COVID-19 at a given point in time, regardless of whether they report experiencing symptoms • the average number of new infections per week over the course of the study • the number of people who test positive for antibodies, to indicate how many people are ever likely to have had the virus • key demographic information (sex, age, occupation)

Coronavirus affects some members of the population more than others. Emerging evidence suggests that older people, men, people with health conditions such as respiratory and pulmonary conditions, and people of a Black, Asian Minority Ethnic (BAME) background are at particular risk. There are also a number of other wider public health risk factors that have been found to increase the likelihood of an individual contracting coronavirus. This briefing presents descriptive evidence on a range of these factors, seeking to understand at a London-wide level the proportion of the population affected by each.

Findings from the Coronavirus (COVID-19) Infection Survey for England.

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.

The Public Health Research Database (PHRD) is a linked asset which currently includes Census 2011 data; Mortality Data; Hospital Episode Statistics (HES); GP Extraction Service (GPES) Data for Pandemic Planning and Research data. Researchers may apply for these datasets individually or any combination of the current 4 datasets.

The purpose of this dataset is to enable analysis of deaths involving COVID-19 by multiple factors such as ethnicity, religion, disability and known comorbidities as well as age, sex, socioeconomic and marital status at subnational levels. 2011 Census data for usual residents of England and Wales, who were not known to have died by 1 January 2020, linked to death registrations for deaths registered between 1 January 2020 and 8 March 2021 on NHS number. The data exclude individuals who entered the UK in the year before the Census took place (due to their high propensity to have left the UK prior to the study period), and those over 100 years of age at the time of the Census, even if their death was not linked. The dataset contains all individuals who died (any cause) during the study period, and a 5% simple random sample of those still alive at the end of the study period. For usual residents of England, the dataset also contains comorbidity flags derived from linked Hospital Episode Statistics data from April 2017 to December 2019 and GP Extraction Service Data from 2015-2019.

These files comprise the publicly available data for the COG-UK hospital-onset COVID-19 infection study. The individual CSV files provided are: - HOCI_public_dataset: Anonymized version of main study dataset, with one row per HOCI case included in the final analysis - HOCI_public_varlist: Variable descriptions for main study dataset - epi_data_combined: Weekly data on total SARS-CoV-2 +ve (cov_pos_epi) and -ve (cov_neg_epi) inpatients at each study site -community_incidence_summary: Weekly local community incidence data for each study site, per 100,000 people per week, obtained from UK government testing dashboard and weighted according to outer postcodes of inpatients at each site.

Notes on anonymisation: HOCI_public_dataset is an anonymised version of the main HOCI study database. In order to fully anonymise individuals, and because the focus of the study was on infection control actions rather than patient outcomes, all individual-level patient demographic and clinical characteristics have been removed. Site and ward names have been changed to anonymized codes, and all free text fields have been removed as some of these contained unblinded details of hospitals and wards. All date fields have been removed, with study week of SARS-CoV-2 +ve test result for each HOCI case provided.

Notes on acronyms: In ‘HOCI_public_varlist’, the following acronyms are used: AGP, aerosol-generating procedure CR, contact restrictions CT, contact tracing DIPC, Director of IPC HCAI, healthcare-associated infection HCW, healthcare worker IPC, infection prevention and control SR, sequence report SRO, sequence report output QM, quality management

Estimates of the prevalence of self-reported long COVID and associated activity limitation, using UK Coronavirus (COVID-19) Infection Survey data. Experimental Statistics.

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.

IntroductionElectronic health records can be used to understand the diverse presentation of post-acute and long-term health outcomes following COVID-19 infection. In England, the UK Health Security Agency, in collaboration with the University of Oxford, has created the Evaluation of post-acute COVID-19 Health Outcomes (ECHOES) dataset to monitor how an initial SARS-CoV-2 infection episode is associated with changes in the risk of health outcomes that are recorded in routinely collected health data.MethodsThe ECHOES dataset is a national-level dataset combining national-level surveillance, administrative, and healthcare data. Entity resolution and data linkage methods are used to create a cohort of individuals who have tested positive and negative for SARS-CoV-2 in England throughout the COVID-19 pandemic, alongside information on a range of health outcomes, including diagnosed clinical conditions, mortality, and risk factor information.ResultsThe dataset contains comprehensive COVID-19 testing data and demographic, socio-economic, and health-related information for 44 million individuals who tested for SARS-CoV-2 between March 2020 and April 2022, representing 15,720,286 individuals who tested positive and 42,351,016 individuals who tested negative.DiscussionWith the application of epidemiological and statistical methods, this dataset allows a range of clinical outcomes to be investigated, including pre-specified health conditions and mortality. Furthermore, understanding potential determinants of health outcomes can be gained, including pre-existing health conditions, acute disease characteristics, SARS-CoV-2 vaccination status, and genomic variants.

The Camden and Islington Public Health Intelligence team has recently completed a needs assessment of Long Covid in North Central London, to explore the burden of Long Covid locally, its impact on residents, and analysis of the system response. The analysis highlights that expected prevalence of Long Covid is much higher than recorded diagnoses in primary care, suggesting that many people with Long Covid may not be receiving a formal diagnosis. The analysis also explores patterns in expected prevalence, diagnosis and referral rates by age, gender, deprivation and ethnicity, variation between primary care networks, and analysis of data from NCL’s Post-Covid specialist clinic. This analysis will help to identify opportunities to improve Long Covid awareness, pathways and care.