In a survey carried out in the United Kingdom in September 2021, five percent of respondents said that they had encountered news or information about the coronavirus that they believed to be false or misleading ** times or more per day in the last week. This marked an increase of *** percent from the share who said the same in the survey wave held in September 2020. Meanwhile, ** percent of respondents believed they had seen fake news about COVID-19 a few times a week in September 2021.

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

Due to the unprecedented circumstances we are facing during the Coronavirus (COVID-19) pandemic, it may not be possible for us to meet the timescales stipulated with regard to Council Spend Data published monthly. Thank you for your understanding and patience during this difficult and unprecedented period. You can find advice and guidance about accessing information from public bodies from the Information Commissioner’s Office (ICO) at www.ico.org.uk/your-data-matters/official-information Please also see the following information from the ICO at www.ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2020/03/coronavirus-and-personal-data/ Details of Council spending from April 2016 onward. Please click 'Download' to view this data in a spreadsheet. This will enable you to filter the information more easily, for example by month. This dataset is updated monthly.

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

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

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


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

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

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

Weekly Key Points

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

Recently announced deaths in Hospitals

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

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

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

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

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

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

Comparison of data sources

Note on data sources

NHS England provides numbers of patients who have died in hos

In recent years behavioural science has quickly become embedded in national level governance. As the contributions of behavioural science to the UK's COVID-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in a fast-track, high-stake context. We aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making. With this in mind, in Study 1 we use corpus linguistics and network analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 647 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown of 2020. We report and discuss (1) the salience of key concepts and actors as the debate unfolded, (2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and (3) patterns of co-occurrence via network analyses. To establish public discourse surrounding identified themes, in Study 2 we investigate how salience and sentiment of key themes and relations to policy were discussed in original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.

2020 coronavirus pandemic in Denmark

Update: Denmark becomes the second country in Europe to go on coronavirus lockdown

Background

According to WHO, Europe is now the 'epicenter' of the coronavirus pandemic. Among those EU countries, Denmark has attracted significant attention from other countries. Within two days, the confirmed cases jumped from 92 to 516. As of 15 March, among nations with at least one million citizens, Denmark has the world's sixth-highest per-capita rate of positive coronavirus cases at 144.3 cases per million people.

The Danish government has introduced stringent restrictions such as:

• From 14 March to 13 April, all Danish borders will be closed.

• Denmark's parliament has passed an emergency coronavirus law which gives health authorities powers to force testing, treatment a and quarantine with the backing of the police.

How to use it?

The dataset (by Mar.14) includes four columns: date, confirmed cases, increased percent by day, and deaths. It is a good source for data visualization or exploring different models of disease transmission, such as exponential and logistic regression. To learn more about the exponential model, please watch this video which is short enough but well explained.

Source:

Danish Health Authority (Sundhedsstyrelsen)

India marks one COVID-19 death every 5 minutes

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Content

People across India scrambled for life-saving oxygen supplies on Friday and patients lay dying outside hospitals as the capital recorded the equivalent of one death from COVID-19 every five minutes.

For the second day running, the country’s overnight infection total was higher than ever recorded anywhere in the world since the pandemic began last year, at 332,730.

India’s second wave has hit with such ferocity that hospitals are running out of oxygen, beds, and anti-viral drugs. Many patients have been turned away because there was no space for them, doctors in Delhi said.

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Mass cremations have been taking place as the crematoriums have run out of space. Ambulance sirens sounded throughout the day in the deserted streets of the capital, one of India’s worst-hit cities, where a lockdown is in place to try and stem the transmission of the virus. source

Dataset

The dataset consists of the tweets made with the #IndiaWantsOxygen hashtag covering the tweets from the past week. The dataset totally consists of 25,440 tweets and will be updated on a daily basis.

The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #IndiaWantsOxygen | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Acknowledgements

https://globalnews.ca/news/7785122/india-covid-19-hospitals-record/ Image courtesy: BBC and Reuters

Inspiration

The past few days have been really depressing after seeing these incidents. These tweets are the voice of the indians requesting help and people all over the globe asking their own countries to support India by providing oxygen tanks.

And I strongly believe that this is not just some data, but the pure emotions of people and their call for help. And I hope we as data scientists could contribute on this front by providing valuable information and insights.

Objectives: To investigate changes in beliefs and behaviours following news of the Omicron variant and changes to guidance understanding of Omicron-related guidance, and factors associated with engaging with protective behaviours.Design: Series of cross-sectional surveys (1 November to 16 December 2021, five waves of data collection).Setting: Online.Participants: People living in England, aged 16 years or over (n=1622–1902 per wave).Primary and secondary outcome measures: Levels of worry and perceived risk, and engagement with key behaviours (out-of-home activities, risky social mixing, wearing a face covering and testing uptake).Results: Degree of worry and perceived risk of COVID-19 (to oneself and people in the UK) fluctuated over time, increasing slightly around the time of the announcement about Omicron (p

GlobalData’s clinical trial report, “Coronavirus Disease 2019 (COVID-19) Global Clinical Trials Review, H1, 2020″ provides an overview of Coronavirus Disease 2019 (COVID-19) Clinical trials scenario. This report provides top line data relating to the clinical trials on Coronavirus Disease 2019 (COVID-19). Report includes an overview of trial numbers and their average enrollment in top countries conducted across the globe. The report offers coverage of disease clinical trials by region, country (G7 & E7), phase, trial status, end points status and sponsor type. Report also provides prominent drugs for in-progress trials (based on number of ongoing trials). GlobalData Clinical Trial Reports are generated using GlobalData’s proprietary database – Pharma – Clinical trials database. Clinical trials are collated from 80+ different clinical trial registries, conferences, journals, news etc across the globe. Clinical trials database undergoes periodic update by dynamic process. Read More

National and local restrictions and policies affecting London, by date.

Supplied as an experimental dataset to provide context for analysis of other social or economic datasets, for instance, footfall and spend data timeseries.

Information was mainly gathered from government announcements published by the Prime Minister's Office.

The restrictions and policies included are:

• School closures - complete closures only
• Pub closures - excluding pubs that serve food
• Shop closures - non-essential
• Eating Places closures - including pubs that serve food
• Stay at home orders
• Household mixing indoors banned
• Working from home encouraged
• Rule of 6 indoors
• 10pm curfew on hospitality
• Eat Out to Help Out scheme

COVID-19 conspiracy theories have proliferated during the global pandemic, and their rapid spread among certain groups may jeopardize the public health response (e.g., undermining motivation to engage in social distancing and willingness to vaccinate against the virus). Using survey data from two waves of a nationally representative, longitudinal study of life in lockdown in the United Kingdom (N = 1,406), we analyze the factors associated with belief in three origin theories related to COVID-19, namely that it 1) originated in a meat market in Wuhan, China; 2) was developed in a lab in Wuhan, China; and 3) is caused by 5G mobile networks. Our findings suggest that political-psychological predispositions are strongly associated with belief in conspiracy theories about the virus, though the direction and effect sizes of these predictors vary depending on the specific content of each origin theory. For instance, belief in the Chinese lab conspiracy theory is strongly associated with right-wing authoritarianism (RWA), social dominance orientation (SDO), and general conspiracy ideation, as well as less reliable news sources, distrust in scientists, and anxiety about the pandemic. Belief in the 5G network conspiracy theory is strongly associated with SDO, distrust in scientists, while less strongly with conspiracy ideation and information from social networks/media; RWA is strongly negatively associated with belief in the 5G conspiracy theory, with older and more wealthy individuals somewhat less likely to endorse it. The meat market origin theory is predicted by intolerance of uncertainty, ethnocentrism, COVID-19 anxiety, and less so by higher income, while distrust in scientists is negatively associated with this origin story. Finally, belief in COVID-19 conspiracy theories is associated with negative public health behaviors such as unwillingness to social distance and vaccinate against the virus. Crucially, our findings suggest that the specific content of COVID-19 conspiracy theories likely determines which individuals may be most likely to endorse them.

Characteristics and outcomes of different ethnic groups among adults.

Update 2 March 2023: Following the merger of NHS Digital and NHS England on 1st February 2023 we are reviewing the future presentation of the NHS Outcomes Framework indicators. As part of this review, the annual publication which was due to be released in March 2023 has been delayed. Further announcements about this dataset will be made on this page in due course. This indicator measures the weighted percentage of people who report their overall experience of GP services as ‘fairly good’ or ‘very good’. This indicator aims to capture the experience of patients of their GP. The vast majority of the population visit their GP each year and often it is the experience people have of primary care that determines their overall view of the NHS. Some different patterns have been observed in the 2020/21 GP Patient Survey (GPPS) data which are likely to have been impacted by the coronavirus (COVID-19) pandemic. Statistics from this period should also be interpreted with care. Legacy unique identifier: P01771

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

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

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

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

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

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