In early-February 2020, the first cases of COVID-19 in the United Kingdom (UK) were confirmed. As of December 2023, the South East had the highest number of confirmed first episode cases of the virus in the UK with 3,180,101 registered cases, while London had 2,947,727 confirmed first-time cases. Overall, there has been 24,243,393 confirmed cases of COVID-19 in the UK as of January 13, 2023.

COVID deaths in the UK COVID-19 was responsible for 202,157 deaths in the UK as of January 13, 2023, and the UK had the highest death toll from coronavirus in western Europe. The incidence of deaths in the UK was 297.8 per 100,000 population as January 13, 2023.

Current infection rate in Europe The infection rate in the UK was 43.3 cases per 100,000 population in the last seven days as of March 13, 2023. Austria had the highest rate at 224 cases per 100,000 in the last week.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In early-February, 2020, the first cases of the coronavirus (COVID-19) were reported in the United Kingdom (UK). The number of cases in the UK has since risen to 24,243,393, with 1,062 new cases reported on January 13, 2023. The highest daily figure since the beginning of the pandemic was on January 6, 2022 at 275,646 cases.

COVID deaths in the UK COVID-19 has so far been responsible for 202,157 deaths in the UK as of January 13, 2023, and the UK has one of the highest death toll from COVID-19 in Europe. As of January 13, the incidence of deaths in the UK is 298 per 100,000 population.

Regional breakdown The South East has the highest amount of cases in the country with 3,123,050 confirmed cases as of January 11. London and the North West have 2,912,859 and 2,580,090 cases respectively.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents.

Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London:

ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis.


Number of people ages 18 and over on the National Immunisation Management Service (NIMS)


ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population.

Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues:

Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates.

For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available,

The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011.

Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

GLA Ethnic group population projections - 2016 as at 2021


ONS Population Denominators produced for Race Disparity Audit as at 2018


ETHPOP population projections produced by the University of Leeds as at 2020

Antibody prevalence estimates As part of the ONS Coronavirus (COVID-19) Infection Survey ONS publish a modelled estimate of the percent of the adult population testing positive for antibodies to Coronavirus by region. Antibodies can be generated by vaccination or previous infection.

Vaccine effects on cases, hospitalisations and deaths When the vaccine roll out began in December 2020 coronavirus cases, hospital admissions and deaths were rising steeply. The peak of infections came in London in early January 2021, before reducing during the national lockdown and as the vaccine roll out progressed. As the vaccine roll out began in older age groups the effect of vaccinations can be separated from the effect of national lockdown by comparing changes in cases, admissions and deaths

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

Simple strategy for consented studies at the Nightingale hospital, London.

Weekly contact matrices calculated from data collected as part of the UK arm of the CoMix survey. All contact matrices were calculated over two survey rounds (SR) of data to account for alternating panels (the indicated SR and the previous SR). Full details of composition can be found in Munday et. al. [1]. Contact matrices are provided for age-groups consistent with publicly available case data from the UKHSA COVID-19 dashboard (0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+) and publicly available aggregates of infection and antibody prevalence from the ONS COVID-19 infection survey (2-10, 11-15, 16-24, 25-34, 35-49, 49-69 and 70+). The data is provided in qs files as 1000 bootstrapped samples of each contact matrix for weekly 'survey rounds' between 19 and 94 (see directory "survey_round_dates.csv"). The files that begin with UKHSA contain the contact matrices for the age stratification of the UKHSA COVID-19 dashboard case data. The files that begin with ONS contain the contact matrices for the age stratification of the ONS COVID-19 infection survey.

Ethics: The study and method of informed consent were approved by the ethics committee of the London School of Hygiene & Tropical Medicine (LSHTM; reference number 21795).

1. Munday, J.D., Jarvis, C.I., Gimma, A. et al. Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data. BMC Med 19, 233 (2021). https://doi.org/10.1186/s12916-021-02107-0

Initial estimates of prevalence of ongoing symptoms following coronavirus (COVID-19) infection in staff and pupils from the COVID-19 Schools Infection Survey across a sample of schools, within selected local authority areas in England. This Schools Infection Survey is jointly led by the London School of Hygiene & Tropical Medicine, Public Health England and the Office for National Statistics.

These datasets comprise the main analyses for the paper “Household Transmission of Seasonal Coronavirus Infections: Results from the Flu Watch cohort study”, published in Wellcome Open Research. Details of the statistical methods are reported in the article. Datasets are given in CSV format and, where relevant, in .dta format. Descriptions for each dataset are as follows:

Household_CoV_acquired.csv/dta – data required to compute the proportion of cases presumably acquired outside of the household versus and the proportion acquired from household transmission. Each row represents an anonymised PCR-confirmed seasonal coronavirus case.

Household_CoV_TransmissionRisk.csv/dta – data required to compute the risk of symptomatic onward household transmission following a seasonal coronavirus index case, and perform stratified descriptive analyses.

Household_CoV_SAR.csv/.dta – data required to compute the seasonal coronavirus secondary attack risk overall and by strain. Each row represents an anonymised exposed-index pair from a given outbreak.

HH Transmission Serial Interval.csv – presents available, anonymised data required to compute the median clinical-onset serial interval overall and by strain for each household outbreak

REal-time Assessment of Community Transmission (REACT-2) started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study involves approximately 150,000 unique people who use a finger prick test over 6 week periods, with additional information collected on contact with known cases to assess an infection point prevalence at national, regional and local levels. Within REACT 2 there is also a study on usability and efficacy of different tests.

Imperial College London is leading a major programme of home testing for COVID-19 to track the progress of the infection across England. Called REACT, the programme was commissioned by the Department of Health and Social Care, and is being carried out in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.

REACT-2 is a world largest surveillance study undertaken in England that examines the prevalence of antibodies in the community. The study focusses on finger prick self-testing at home by individuals aged 18 or over.The findings will provide the government with a better understanding of the use of antibody tests at home as well as assess the trends in antibody levels and how they vary across different population subgroups. This will inform government policies to protect health and save lives.

Background: There is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 due to the lack of large studies.Methods: We used data from a single large UK Cancer Center to assess the demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19 diagnosis between 29 February and 12 May 2020. Logistic/Cox proportional hazards models were used to identify which demographic and/or clinical characteristics were associated with COVID-19 severity/death.Results: 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with a severe case of the disease. An initial cancer diagnosis >24 months before COVID-19 [OR: 1.74 (95% CI: 0.71–4.26)], presenting with fever [6.21 (1.76–21.99)], dyspnea [2.60 (1.00–6.76)], gastro-intestinal symptoms [7.38 (2.71–20.16)], or higher levels of C-reactive protein [9.43 (0.73–121.12)] were linked with greater COVID-19 severity. During a median follow-up of 37 days, 34 patients had died of COVID-19 (22%). Being of Asian ethnicity [3.73 (1.28–10.91)], receiving palliative treatment [5.74 (1.15–28.79)], having an initial cancer diagnosis >24 months before [2.14 (1.04–4.44)], dyspnea [4.94 (1.99–12.25)], and increased CRP levels [10.35 (1.05–52.21)] were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin [0.04 (0.01–0.04)].Conclusions: A longer-established diagnosis of cancer was associated with increased severity of infection as well as COVID-19 death, possibly reflecting the effects a more advanced malignant disease has on this infection. Asian ethnicity and palliative treatment were also associated with COVID-19 death in cancer patients.

REal-time Assessment of Community Transmission (REACT-1) is one of the largest population surveillance studies in the world. It started in April 2020 to measure the prevalence of SARS-CoV-2 in the general population in England. Each month around 150,000 people completed a questionnaire and returned a PCR test.

The study tracked the progress of infection across England. It was commissioned by the Department of Health and Social Care and was carried out between April 2020 and April 2022 in partnership with Imperial College Healthcare NHS Trust and Ipsos MORI.

The partner study, REal-time Assessment of Community Transmission (REACT-2), started in May 2020 to determine the prevalence of and trends in antibodies levels in study participants. This study is also described in the Innovation Gateway.

For further information and the study questionnaires, please see the REACT study website.

We present the full meta-data catalogue in the Innovation Gateway. Researchers external to Imperial College London can apply to access subsets of anonymised data from REACT participants. To access the full REACT dataset on Imperial's TRE, researchers require an affiliation or collaboration with Imperial College London.

REACT has a data sharing agreement with NHS England, details of which can be found under agreement DARS-NIC-431352-G7F1M-v2.2 via https://digital.nhs.uk/services/data-access-request-service-dars/data-uses-register

COVID-19 has caused the greatest pandemic in living memory. Alongside providing excellent clinical care in the most challenging of environments, there is also a critical need for clinical research to better understand this disease. This will equip us to better deal with the current pandemic as well future ones.

We need to establish why some people develop severe disease and others never get ill despite infection. We need to know whether there are targets for drug development to treat the disease or to give people who are exposed. We will look at genetic influences and immunology (including prior protective viral exposure), seek neutralising antibodies, understand the cellular responses and assess ethnic and sociological factors – all by collecting a biorepository of over 200,000 samples taken from our own healthcare staff weekly over the next 4 months. These samples will then be divided up and sent to the UKs best academic and pharmaceutical research institutions for collaborative, swift science maximising the yield of the consortium to answer the questions in such urgent need of answers.

Dr Charlotte Manisty, Professor James Moon and their team embarked on a pioneering project with Barts NHS Health Trust, in collaboration with University College London (UCL) and Queen Mary’s University London (QMLU). Their research focused on gathering blood samples and health data from frontline healthcare workers, rather than patients admitted to hospital with COVID-19. This was because healthcare workers have high exposure rates to the disease – it also allows researchers to compare samples from each person before, during and after their exposure to COVID-19, and to investigate the disease in people who develop only mild symptoms or are asymptomatic.

Context - Covid data falsification discussion:

An active discussion about the mortality data in Moscow has erupted in the days. The Moscow Times newspaper drew attention to a significant increase in official mortality rates in April 2020: "Moscow recorded 20% more fatalities in April 2020 compared to its average April mortality total over the past decade, according to newly published preliminary data from Moscow’s civil registry office. The data comes as Russia sees the fastest growth in coronavirus infections in Europe, while its mortality rate remains much lower than in many countries. Moscow, the epicenter of Russia’s coronavirus outbreak, has continued to see daily spikes in new cases despite being under lockdown since March 30. According to the official data, 11,846 people died in Russia’s capital in April of this year, roughly a 20% increase from the 10-year average for April deaths, which is 9,866. The numbers suggest that the city’s statistics of coronavirus deaths may be higher in reality than official numbers indicate. Russia boasts a relatively low coronavirus mortality rate of 0.9%, which experts believe is linked to the way coronavirus-related deaths are counted."

After this publication have been realesed The Moscow Department of Health has denied the statement of the inaccuracy of counting.:

First, Moscow is a region that openly publishes mortality data on its websites. Moscow on an initiative basis published data for April before the federal structures did it. Secondly, the comparison of mortality rates in the monthly dynamics is incorrect and is not a clear evidence of any trends. In April 2020, indeed, according to the Civil Registry Office in Moscow, 11,846 death certificates were issued. So, the increase compared to April 2019 amounted to 1841 people, and compared to the same month of 2018 - 985 people, i.e. 2 times less. Thirdly, the diagnosis of coronavirus-infected deaths in Moscow is established after a mandatory autopsy is performed in strict accordance with the Provisional Guidelines of the Russian Ministry of Health.Of the total number of deaths in April 2020, 639 are people whose cause of death is coronavirus infection and its complications, most often pneumonia.It should be emphasized that the pathological autopsy of the dead with suspected CoV-19 in Russia and Moscow is carried out in 100% of cases, unlike most other countries.It is impossible to name the cause of death of COVID-19 in other cases. For example, over 60% of deaths occurred from obvious alternative causes, such as vascular accidents (myocardial infarction and stroke), stage 4 malignant diseases (essentially palliative patients), leukemia, systemic diseases with the development of organ failure (e.g. amyloidosis and terminal renal insufficiency) and other non-curable deadly diseases. Fourth, any seasonal increase in the incidence of SARS, not to mention the pandemic caused by the spread of the new coronavirus, is always accompanied by an increase in mortality. This is due to the appearance of the dead directly from an infectious disease, but to an even greater extent from other diseases, the exacerbation of which and the decompensation of the condition of patients suffering from these diseases also leads to death. In these cases, the infectious onset is a catalyst for the rapid progression of chronic diseases and the manifestation of new diseases. Fifthly, a similar situation with statistics is observed in other countries - mortality from COVID-19 is lower than the overall increase in mortality. According to the official sites of cities:In New York, mortality from coronavirus in April amounted to 11,861 people. At the same time, the total increase in mortality compared to the same period in 2019 is 15709.In London, in April, 3,589 people died with a diagnosis of coronavirus, while the total increase was 5531 Sixth, even if all the additional mortality for April in Moscow is attributed to coronavirus, the mortality from COVID will be slightly more than 3%, which is lower than the official mortality in New York and London (10% and 23%, respectively). Moreover, if you make such a recount in these cities, the mortality rate in them will be 13% and 32%, respectively. Seventh, Moscow is open for discussion and is ready to share experience with both Russian and foreign experts.

Content

I think community members would be interested in studying the data on mortality in the Russian capital themselves and conducting a competent statistical check.

This may be of particular interest in connection with that he [US announced a grant of $ 250 thousand to "expose the disinformation of health care" in Russia](https://www....

This report and dataset were commissioned by the Greater London Authority, and produced by Centric Lab to uncover the work of community groups that connect with environmental issues in Hackney, Newham, Tower Hamlets and Waltham Forest. These four boroughs were selected as they have some of the highest representation of multi-ethnic low income communities who were greatly affected by the coronavirus pandemic. Many reports highlighted the role of environmental pollutants and structural deprivation as a determinant of Covid-19 infection and impact. As evidenced in this report communities demonstrate numerous and diverse capabilities in tackling the structural challenges of modern day London. This report highlights the different community groups committed to improving the lives of their families, neighbours, and future generations who grace the place they call home. The Mayor of London’s Engaging Londoners in Recovery Programme 2021-2023 sets out an agenda to champion this intellect and capacity to help deliver bottom-up led change in policy such as that of a Green New Deal for London. This report was produced in late 2021 and early 2022 when ‘Plan B’ Covid-19 restrictions were in place for most of its duration. This project was a desktop study and focused on working remotely. All data was gathered through online means and digital communications. This means that despite collating the amazing work of 143 organisations there’s a chance some were missed due to low digital visibility.

These indicators are designed to accompany the SHMI publication. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. There has been a fall in the number of spells for some trusts due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Contextual indicators on the number of provider spells which are excluded from the SHMI due to them being related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. These indicators are being published as experimental statistics. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the Hospital Episode Statistics (HES) data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 2. There is a shortfall in the number of records for Royal Free London NHS Foundation Trust (trust code RAL) and Northern Care Alliance NHS Foundation Trust (trust code RM3). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A proposed merger between Northern Devon Healthcare NHS Trust (trust code RBZ) and Royal Devon and Exeter NHS Foundation Trust (trust code RH8) was due to take place on 1 April 2022. The new trust name and code is yet to be confirmed. Please note that separate indicator values have been produced for these organisations for this publication. When we receive confirmation of the new trust name and code we will reflect the new organisation structure within future publications. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Parameter tuning in the UK case.

This is an indicator designed to accompany the Summary Hospital-level Mortality Indicator (SHMI). As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. This indicator shows the number of provider spells which are coded as COVID-19, and therefore excluded from the SHMI, as a percentage of all provider spells in the SHMI (prior to the exclusion). This indicator is being published as an experimental statistic. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. Please note that there has been a fall in the number of spells for most trusts between this publication and the previous SHMI publication, ranging from 0 per cent to 5 per cent. This is due to COVID-19 impacting on activity from March 2020 onwards and appears to be an accurate reflection of hospital activity rather than a case of missing data. 2. The data for St Helens and Knowsley Teaching Hospitals NHS Trust (trust code RBN) has incomplete information on secondary conditions that the patients suffers from, and this will have affected the calculation of this indicator. Values for this trust should therefore be interpreted with caution. Please note, this issue was not identified until after this publication was initially released on 13th May 2021. Data quality notices were later added to this publication in July 2021. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. There is a shortfall in the number of records for Mid Cheshire Hospitals NHS Foundation Trust (trust code RBT), meaning that values for this trust are based on incomplete data and should therefore be interpreted with caution. 5. We recommend that values for Guy’s and St Thomas’ NHS Foundation Trust (trust code RJ1) are interpreted with caution as there is a possible shortfall in the number of records which is currently under investigation. 6. On 1 April 2021 Western Sussex Hospitals NHS Foundation Trust (trust code RYR) merged with Brighton and Sussex University Hospitals NHS Trust (trust code RXH). The new trust is called University Hospitals Sussex NHS Foundation Trust (trust code RYR). However, as we received notification of this change after data processing for this publication began, separate indicator values have been produced for this publication. The next publication in this series will reflect the updated organisation structure. 7. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

Forecasts for six other European countries.

Unequal impact of COVID-19: BAME disproportionality The shielded population are those that have been defined by Government on medical grounds as medically vulnerable due to a clinical condition that puts them at High risk of developing complications from COVID-19 infection.

Those recommended to shield include: • Organ transplant recipients • Pregnant women with congenital heart conditions • Those with rare diseases such as homozygous sickle cell, SCID and others • Those on immunosuppression therapies • People with specific cancers or those with cancer undergoing chemo/radiotherapy.

Camden has so far received the contact details of almost 8,000 residents identified by central Government, with more records likely in future.

The details received do not contain the personal characteristics of those on the list, although we have received an overview from North Central London NHS. (note: above is broken down into shielder’s location and BAME ward profile, shielding population by ethnicity, shielded food need and dietary requirements).