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.

Due to changes in the collection and availability of data on COVID-19, this dataset is no longer updated. Latest information about COVID-19 is available via the UKHSA data dashboard. The UK government publish daily data, updated weekly, on COVID-19 cases, vaccinations, hospital admissions and deaths. This note provides a summary of the key data for London from this release. Data are published through the UK Coronavirus Dashboard, last updated on 23 March 2023. This update contains: Data on the number of cases identified daily through Pillar 1 and Pillar 2 testing at the national, regional and local authority level Data on the number of people who have been vaccinated against COVID-19 Data on the number of COVID-19 patients in Hospital Data on the number of people who have died within 28 days of a COVID-19 diagnosis Data for London and London boroughs and data disaggregated by age group Data on weekly deaths related to COVID-19, published by the Office for National Statistics and NHS, is also available. Key Points On 23 March 2023 the daily number of people tested positive for COVID-19 in London was reported as 2,775 On 23 March 2023 it was newly reported that 94 people in London died within 28 days of a positive COVID-19 test The total number of COVID-19 cases identified in London to date is 3,146,752 comprising 15.2 percent of the England total of 20,714,868 cases In the most recent week of complete data (12 March 2023 - 18 March 2023) 2,951 new cases were identified in London, a rate of 33 cases per 100,000 population. This compares with 2,883 cases and a rate of 32 for the previous week In England as a whole, 29,426 new cases were identified in the most recent week of data, a rate of 52 cases per 100,000 population. This compares with 26,368 cases and a rate of 47 for the previous week Up to and including 22 March 2023 6,452,895 people in London had received the first dose of a COVID-19 vaccine and 6,068,578 had received two doses Up to and including 22 March 2023 4,435,586 people in London had received either a third vaccine dose or a booster dose On 22 March 2023 there were 1,370 COVID-19 patients in London hospitals. This compares with 1,426 patients on 15 March 2023. On 22 March 2023 there were 70 COVID-19 patients in mechanical ventilation beds in London hospitals. This compares with 72 patients on 15 March 2023. Update: From 1st July updates are weekly From Friday 1 July 2022, this page will be updated weekly rather than daily. This change results from a change to the UK government COVID-19 Dashboard which will move to weekly reporting. Weekly updates will be published every Thursday. Daily data up to the most recent available will continue to be added in each weekly update. Data summary 리소스 CSV phe_vaccines_age_london_boroughs.csv CSV 다운로드 phe_vaccines_age_london_boroughs.csv CSV phe_healthcare_admissions_age.csv CSV 다운로드

As of January 12, 2023, COVID-19 has been responsible for 202,157 deaths in the UK overall. The North West of England has been the most affected area in terms of deaths at 28,116, followed by the South East of England with 26,221 coronavirus deaths. Furthermore, there have been 22,264 mortalities in London as a result of COVID-19.

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

Although an international chain, its base and the majority of Pret a Manger (Pret) stores are in the UK and in London in particular. The continual development and communication strategies that the company has undertaken in this market have demonstrated both the long-term strategies that foodservice operators need to build on in order to comply with consumer demands and lifestyles but also the short-term engagement opportunities that are vital in maintaining and inspiring a loyal consumer base, especially during the developing coronavirus pandemic. Read More

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

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%. Number of deaths Proportion of deaths Week ending Hospital Care home Home Other Hospital Care home Home Other 06 Mar 2020 1 1 0 0 50% 50% 0% 0% 13 Mar 2020 13 0 4 0 76% 0% 24% 0% 20 Mar 2020 148 9 11 0 88% 5% 7% 0% 27 Mar 2020 610 45 53 14 84% 6% 7% 2% 03 Apr 2020 1,215 132 143 27 80% 9% 9% 2% 10 Apr 2020 1,495 282 162 32 76% 14% 8% 2% 17 Apr 2020 1,076 295 101 29 72% 20% 7% 2% 24 Apr 2020 669 210 72 35 68% 21% 7% 4% 01 May 2020 348 125 43 15 66% 24% 8% 3% 08 May 2020 261 93 29 16 65% 23% 7% 4% 15 May 2020 152 51 22 5 66% 22% 10% 2% 22 May 2020 93 51 10 3 59% 32% 6% 2% 29 May 2020 62 25 7 6 62% 25% 7% 6% 05 Jun 2020 53 23 4 1 65% 28% 5% 1% 12 Jun 2020 27 11 9 3 54% 22% 18% 6% 19 Jun 2020 22 7 6 2 59% 19% 16% 5% 26 Jun 2020 14 14 5 1 41% 41% 15% 3% 03 Jul 2020 10 5 2 5 45% 23% 9% 23% 10 Jul 2020 15 3 0 1 79% 16% 0% 5% 17 Jul 2020 8 7 2 0 47% 41% 12% 0% 24 Jul 2020 15 1 0 2 83% 6% 0% 11% 31 Jul 2020 6 2 1 0 67% 22% 11% 0% 07 Aug 2020 6 2 0 1 67% 22% 0% 11% 14 Aug 2020 7 4 2 1 50% 29% 14% 7% 21 Aug 2020 4 0 0 0 100% 0% 0% 0% 28 Aug 2020 1 2 0 0 33% 67% 0% 0% 04 Sep 2020 3 0 1 0 75% 0% 25% 0% 11 Sep 2020 7 2 0 1 70% 20% 0% 10% 18 Sep 2020 9 2 1 0 75% 17% 8% 0% 25 Sep 2020 23 3 3 0 79% 10% 10% 0% 02 Oct 2020 27 3 2 0 84% 9% 6% 0% 09 Oct 2020 36 3 3 0 86% 7% 7% 0% 16 Oct 2020 41 0 2 0 95% 0% 5% 0% 23 Oct 2020 47 4 4 0 85% 7% 7% 0% 30 Oct 2020 91 3 5 1 91% 3% 5% 1% 06 Nov 2020 93 7 5 2 87% 7% 5% 2% 13 Nov 2020 109 11 10 2 83% 8% 8% 2% 20 Nov 2020 162 5 8 4 91% 3% 4% 2% 27 Nov 2020 175 8 14 5 87% 4% 7% 2% 04 Dec 2020 190 10 13 10 85% 4% 6% 4% 11 Dec 2020 199 9 13 6 88% 4% 6% 3% 18 Dec 2020 267 15 25 4 86% 5% 8% 1% 25 Dec 2020 403 30 43 7 83% 6% 9% 1% 01 Jan 2021 677 35 109 28 80% 4% 13% 3% 08 Jan 2021 959 73 167 36 78% 6% 14% 3% 15 Jan 2021 1,125 84 165 39 80% 6% 12% 3% 22 Jan 2021 1,163 96 142 43 81% 7% 10% 3% 29 Jan 2021 863 82 101 28 80% 8% 9% 3% 05 Feb 2021 605 70 59 38 78% 9% 8% 5% 12 Feb 2021 439 29 49 14 83% 5% 9% 3% 19 Feb 2021 338 29 33 12 82% 7% 8% 3% 26 Feb 2021 214 19 19 11 81% 7% 7% 4% 05 Mar 2021 141 11 19 5 80% 6% 11% 3% 12 Mar 2021 99 9 7 1 85% 8% 6% 1% 19 Mar 2021 65 10 1 1 84% 13% 1% 1% 26 Mar 2021 41 9 4 2 73% 16% 7% 4% 02 Apr 2021 35 5 4 0 80% 11% 9% 0% 09 Apr 2021 29 2 3 0 85% 6% 9% 0% 16 Apr 2021 24 6 2 0 75% 19% 6% 0% 23 Apr 2021 14 1 0 0 93% 7% 0% 0% 30 Apr 2021 13 1 1 0 87% 7% 7% 0% 07 May 2021 14 3 0 0 82% 18% 0% 0% 14 May 2021 6 2 0 0 75% 25% 0% 0% 21 May 2021 8 1 1 0 80% 10% 10% 0% 28 May 2021 11 1 2 1 73% 7% 13% 7% 04 Jun 2021 9 0 0 0 100% 0% 0% 0% 11 Jun 2021 11 3 0 0 79% 21% 0% 0% 18 Jun 2021 11 4 2 1 61% 22% 11% 6% 25 Jun 2021 10 0 0 1 91% 0% 0% 9% 02 Jul 2021 14 1 2 0 82% 6% 12% 0% 09 Jul 2021 12 1 4 1 67% 6% 22% 6% 16 Jul 2021 18 3 2 0 78% 13% 9% 0% 23 Jul 2021 48 0 7 1 86% 0% 12% 2% 30 Jul 2021 49 2 4 4 83% 3% 7% 7% 06 Aug 2021 66 1 9 1 86% 1% 12% 1% 13 Aug 2021 60 1 12 1 81% 1% 16% 1% 20 Aug 2021 84 1 5 1 92% 1% 5% 1% 27 Aug 2021 78 3 10 3 83% 3% 11% 3% 03 Sep 2021 85 3 7 1 89% 3% 7% 1% 10 Sep 2021 83 2 10 2 86% 2% 10% 2% 17 Sep 2021 65 2 9 1 84% 3% 12% 1% 24 Sep 2021 76 5 5 0 88% 6% 6% 0% 01 Oct 2021 88 2 15 1 83% 2% 14% 1% 08 Oct 2021 65 2 7 1 87% 3% 9% 1% 15 Oct 2021 62 1 9 4 82% 1% 12% 5% 22 Oct 2021 64 2 11 2 81% 3% 14% 3% 29 Oct 2021 66 3 11 1 81% 4% 14% 1% 05 Nov 2021 67 3 10 5 79% 4% 12% 6% 12 Nov 2021 84 2 12 1 85% 2% 12% 1% 19 Nov 2021 63 2 2 0 94% 3% 3% 0% 26 Nov 2021 68 2 8 0 87% 3% 10% 0% 03 Dec 2021 72 2 10 1 85% 2% 12% 1% 10 Dec 2021 81 3 12 4 81% 3% 12% 4% 17 Dec 2021 91 1 12 3 85% 1% 11% 3% 24 Dec 2021 101 8 15 3 80% 6% 12% 2% 31 Dec 2021 129 11 19 6 78% 7% 12% 4% 07 Jan 2022 178 18 19 4 81% 8% 9% 2% 14 Jan 2022 194 23 16 14 79% 9% 6% 6% 21 Jan 2022 165 25 11 4 80% 12% 5% 2% 28 Jan 2022 119 20 13 5 76% 13% 8% 3% 04 Feb 2022 97 13 8 2 81% 11% 7% 2% 11 Feb 2022 51 10 6 6 70% 14% 8% 8% 18 Feb 2022 62 6 9 3 78% 8% 11% 4% 25 Feb 2022 55 2 2 1 92% 3% 3% 2% 04 Mar 2022 47 2 2 2 89% 4% 4% 4% 11 Mar 2022 48 3 4 0 87% 5% 7% 0% 18 Mar 2022 60 7 8 4 76% 9% 10% 5% 25 Mar 2022 51 11 5 2 74% 16% 7% 3% 01 Apr 2022 60 8 5 2 80% 11% 7% 3% 08 Apr 2022 78 4 7 3 85% 4% 8% 3% 15 Apr 2022 74 6 6 3 83% 7% 7% 3% 22 Apr 2022 58 10 7 6 72% 12% 9% 7% 29 Apr 2022 39 8 3 4 72% 15% 6% 7% 06 May 2022 44 3 4 0 86% 6% 8% 0% 13 May 2022 29 2 4 2 78% 5% 11% 5% 20 May 2022 16 4 0 2 73% 18% 0% 9% 27 May 2022 34 3 3 1 83% 7% 7% 2% 03 Jun 2022 18 1 1 0 90% 5% 5% 0% 10 Jun 2022 18 1 3 0 82% 5% 14% 0% 17 Jun 2022 22 1 2 0 88% 4% 8% 0% 24 Jun 2022 33 2 3 1 85% 5% 8% 3% 01 Jul 2022 33 2 2 0 89% 5% 5% 0% 08 Jul 2022 51 4 4 4 81% 6% 6% 6% 15 Jul 2022 60 5 4 2 85% 7% 6% 3% 22 Jul 2022 71 9 10 3 76% 10% 11% 3% 29 Jul 2022 48 7 9 0 75% 11% 14% 0% 05 Aug 2022 35 1 3 4 81% 2% 7% 9% Total 18,924 2,390 2,152 634 79% 10% 9% 3% Comparison with all cause mortality Comparison of data sources Note on data sources NHS England provides numbers of patients who have died in hospitals in England and had tested positive for COVID-19, and from 25 April, the number of patients where COVID-19 is mentioned on the death certificate and no positive COVID-19 test result was received. Figures are updated each day at 2pm with deaths reported up to 5pm the previous day. There is a delay between the occurrence of a death to it being captured in the

Abstract copyright UK Data Service and data collection copyright owner.

The UCL COVID-19 Social Study at University College London (UCL) was launched on 21 March 2020. Led by Dr Daisy Fancourt and Professor Andrew Steptoe from the Department of Behavioural Science and Health, the team designed the study to track in real-time the psychological and social impact of the virus across the UK.

The study quickly became the largest in the country, growing to over 70,000 participants and providing rare and privileged insight into the effects of the pandemic on people’s daily lives. Through our participants’ remarkable two-year commitment to the study, 1.2 million surveys were collected over 105 weeks, and over 100 scientific papers and 44 public reports were published.

During COVID-19, population mental health has been affected both by the intensity of the pandemic (cases and death rates), but also by lockdowns and restrictions themselves. Worsening mental health coincided with higher rates of COVID-19, tighter restrictions, and the weeks leading up to lockdowns. Mental health then generally improved during lockdowns and most people were able to adapt and manage their well-being. However, a significant proportion of the population suffered disproportionately to the rest, and stay-at-home orders harmed those who were already financially, socially, or medically vulnerable. Socioeconomic factors, including low SEP, low income, and low educational attainment, continued to be associated with worse experiences of the pandemic. Outcomes for these groups were worse throughout many measures including mental health and wellbeing; financial struggles;self-harm and suicide risk; risk of contracting COVID-19 and developing long Covid; and vaccine resistance and hesitancy. These inequalities existed before the pandemic and were further exacerbated by COVID-19, and such groups remain particularly vulnerable to the future effects of the pandemic and other national crises.

Further information, including reports and publications, can be found on the UCL COVID-19 Social Study website.

The study asked baseline questions on the following:

• Demographics, including year of birth, sex, ethnicity, relationship status, country of dwelling, urban/rural dwelling, type of accommodation, housing tenure, number of adults and children in the household, household income, education, employment status, pet ownership, and personality.
• Health and health behaviours, including pre-existing physical health conditions, diagnosed mental health conditions, pregnancy, smoking, alcohol consumption, physical activity, caring responsibilities, usual social behaviours, and social network size.

It also asked repeated questions at every wave on the following:

• COVID-19 status, including whether the respondent had had COVID-19, whether they had come into likely contact with COVID-19, current isolation status and motivations for isolation, length of isolation, length of time not leaving the home, length of time not contacting others, trust in government, trust in the health service, adherence to health advice, and experience of adverse events due to COVID-19 (including severe illness within the family, bereavement, redundancy, or financial difficulties).
• Mental health, including wellbeing, depression, anxiety, which factors were causing stress, sleep quality, loneliness, social isolation, and changes in health behaviours such as smoking, drinking and exercise.
• How people were spending their time whilst in isolation, including questions on working, functional household activities, care, and schooling of any children in the household, hobbies, and relaxation.

Certain waves of the study also included one-off modules on topics including volunteering behaviours, locus of control, frustrations and expectations, coping styles, fear of COVID-19, resilience, arts and creative engagement, life events, weight, gambling behaviours, mental health diagnosis, use of financial support, faith and religion, relationships, neighbourhood satisfaction, healthcare usage, discrimination experiences, life changes, optimism, long COVID and COVID-19 vaccination.

COVID-19 causes significant mortality in elderly and vulnerable people and spreads easily in care homes where one in seven individuals aged > 85 years live. However, there is no surveillance for infection in care homes, nor are there systems (or research studies) monitoring the impact of the pandemic on individuals or systems. Usual practices are disrupted during the pandemic, and care home staff are taking on new and unfamiliar roles, such as advanced care planning. Understanding the nature of these changes is critical to mitigate the impact of COVID-19 on residents, relatives and staff. 20 care homes staff members were interviewed using semi-structured interviews.

The COVID-19 pandemic poses a substantial risk to elderly and vulnerable care home residents and COVID-19 can spread rapidly in care homes. We have national, daily data on people with COVID-19 and deaths, but there is no similar data for care homes. This makes it difficult to know the scale of the problem, and plan how to keep care home residents safe. We also want to understand the impact of COVID-19 on care home staff and residents. Researchers from University College London (UCL) will measure the number of cases of COVID-19 in care homes, using data from Four Seasons Healthcare, a large care home chain. FSHC remove residents' names and addresses before sending the dataset to UCL, protecting resident's confidentiality. Since we cannot visit care homes during the pandemic, we will hold virtual (online) discussion meetings with care home stakeholders (staff, residents, relatives, General Practice teams) every 6-8 weeks, to learn rapid lessons about managing COVID-19 in care homes and identify pragmatic solutions. Our findings will be shared with FHSC, GPs and Public Health England, patients and the public, and support the national response to COVID-19. Patients and the public will be involved in all stages of the research.

According to a survey conducted in the United Kingdom (UK) as of April 2022, 246 thousand people in the South East of England were estimated to be suffering long COVID symptoms, the highest number across the regions in the UK. In the North West of England a further 218 thousand people were estimated to have long COVID.

Self-reported COVID-19 infections and other respiratory illnesses, including associated symptoms and health outcomes. Joint study with the UK Health Security Agency. These are official statistics in development.

This research project mapped and monitored responses to household food insecurity during the COVID-19 pandemic. During the COVID-19 pandemic, governments, local authorities, charities and local communities worked to ensure access to food for those facing new risks of food insecurity due to being unable to go out for food or due to income losses arising from the crisis. New schemes were developed, such as governments replacing incomes of people at risk of unemployment on account of lockdowns, providing food parcels for people asked to shield, referrals for people to receive voluntary help with grocery shopping, and free school meals replacement vouchers or cash transfers. These worked alongside existing provision for those unable to afford food – such as food banks – which have been adapting their services to continue to meet increasing demand from a range of population groups. This resulted in a complex set of support structures which developed and changed as the COVID-19 pandemic, and its impacts, evolved. About the project The project was funded by the Economic and Social Research Council (ESRC) through the UKRI Ideas to Address COVID-19 grant call and ran for two years from July 2020. The research aimed to provide collaborative monitoring and analysis of food support systems to inform food access policy and practice. The research team was led by the University of Sheffield and King’s College London alongside colleagues from Sustain: the alliance for better food and farming and Church Action on Poverty. Full details of the team are below. Collaboration with partners and stakeholders was at the heart of the project. The research team worked with stakeholders from national and local government, the civil service, third sector, NGOs as well as people who were accessing food and financial assistance during the pandemic. The End of project summary of key findings were published in August 2022. Details of the workpackages and research reports can be found below. Project work packages Work package 1: National level food access systems mapping and monitoring Looking at food access support across the UK during the COVID-19 pandemic, national level mapping and monitoring was undertaken in England, Northern Ireland, Scotland and Wales as well as at a UK level. National level stakeholders (for example from devolved governments and national voluntary organisations) from across the four nations worked with us to understand and monitor how support for food access has operated and evolved across the UK. Work package 1 publications: Mapping responses to the risk of rising food insecurity during the COVID-19 crisis across the UK (published August 2020) Monitoring responses to the risk of rising food insecurity during the COVID-19 crisis across the UK (published December 2020) Mapping and monitoring responses to the risk of rising food insecurity during the COVID-19 crisis across the UK - Autumn 2020 to Summer 2021 (published August 2022) Work package 2: Participatory Policy Panel To fully understand food access responses, it was crucial to hear directly from those with lived experience of food insecurity during the pandemic. In partnership with Church Action on Poverty, we convened a participatory policy panel made up of people who have direct experience of a broad range of support to access food. Meeting regularly throughout the project (Oct 2020-Dec 2021), the panel used a range of participatory and creative methods to share and reflect on their experiences and contribute these to policy recommendations. Work package 2 publications: Navigating Storms (published October 2021) Food Experiences During COVID-19 Participatory Panel Deliberative Policy Engagement (published August 2022) Food Experiences During COVID-19 - Participatory Methods in Practice: Key Learning (published August 2022) Work package 3: Local area case studies Fourteen local areas across the UK were the focus for more in depth case study research. Working with local stakeholders in each area, the research mapped what local responses looked like and how they operated. The research followed the developments in these areas throughout the duration of the project. Work package 3 publications: Comparing local responses to household food insecurity during COVID-19 across the UK (March – August 2020) – Executive Summary (published July 2021) Comparing local responses to household food insecurity during COVID-19 across the UK (March – August 2020) (published July 2021). Eight local case study reports covering responses in those areas between March and August 2020: Argyll and Bute, Belfast, Cardiff, Derry and Strabane, Herefordshire, Moray, Swansea, West Berkshire (published July 2021). Local Area Case Studies – Methodological Appendix (published July 2021) Local responses to household food insecurity during COVID-19 across the UK (March – August 2020): Full report (published July 2021) Local responses to household food insecurity across the UK during COVID-19 (September 2020 – September 2021) (published February 2022) Local responses to household food insecurity across the UK during COVID-19 (September 2020 – September 2021) - Executive Summary (published February 2022) The project was undertaken with ethical approval from the University of Sheffield.

Russia had over 23 million COVID-19 cases as of October 22, 2023. Over the past week, that figure increased by nearly 20 thousand. Russia had the 10th-highest number of coronavirus (COVID-19) cases worldwide. Debate about COVID-19 deaths in Russia The number of deaths from the disease was lower than in other countries most affected by the pandemic. Several foreign media sources, including New York Times and Financial Times, published articles suggesting that the official statistics on the COVID-19 death toll in Russia could be lowered. A narrow definition of a death from COVID-19 and a general increase in mortality in Moscow were pointed out while suggesting why actual death figures could be higher than reported. Russian explanation of lower COVID-19 deaths Experts and lawmakers from Russia provided several answers to the accusations. Among them were the fact that Russians timely reported symptoms to doctors, a high number of tests conducted, as well as a higher herd immunity of the population compared to other countries. In a letter to the New York Times, Moscow’s health department head argued that even if all the additional death cases in the Russian capital in April 2020 were categorized as caused by the COVID-19, the city’s mortality rate from the disease would still be lower than in cities like New York or London.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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.

Provisional counts of deaths in care homes caused by coronavirus (COVID-19) by local authority. Published by the Office for National Statistics and Care Quality Commission.

This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. F1000Research 2021, 10:1312, DOI: 10.12688/f1000research.75891.1). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..

We also provide a guide which provides a general description of the contents of the four sections in this archive (Documentation_for_Sections_of_Zenodo_Archive.docx). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.

Data Files:

Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"

This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.

Section 2. "Section_2.Localized_Hotspot_Lists.zip"

All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.

Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"

Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).

Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"

Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan).

Software:

This Zenodo archive also provides all program files pertaining to the Geostatistical Epidemiology Toolbox (Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries.

This distribution of the Geostatistical Epidemiology Toolbox does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from DMTI (https://www.dmtispatial.com/canmap/) through the Scholar's Geoportal at the University of Western Ontario (http://geo2.scholarsportal.info/). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).

Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"

The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes.

The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images.

This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).

Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"

In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:

“Ontario_City_Closest_Postal_Code_Identification.pl”

Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.

The output of this program (for the same municipal region being evaluated) is required for the following two Perl

Parameter tuning in the UK case.

The coronavirus (COVID-19) outbreak has forced governments across the world to implement social distancing measures and lockdowns in order to reduce the number of new cases and deaths. Using data from their travel app, Citymapper were able to produce a Mobility Index to indicate the movements of certain European cities during the period from March 16-22, 2020. Countries hardest hit by the virus and where lockdowns are in places appeared to have the least amount of movement. In Milan, Italy, only four percent of the city were moving and in Madrid, Spain, only five percent according to the Index. However in other affected cities movement was still higher, such as in London where 36 percent of the city were still moving in the week ending March 22; The next day, the UK govenrment implemented a lockdown with stricter regulations regarding when people can go out.