There were 11,480 deaths registered in England and Wales for the week ending November 14, 2025, compared with 11,297 in the previous week. During this time period, the two weeks with the highest number of weekly deaths were in April 2020, with the week ending April 17, 2020, having 22,351 deaths, and the following week 21,997 deaths, a direct result of the COVID-19 pandemic in the UK. Death and life expectancy As of 2022, the life expectancy for women in the UK was just over 82.5 years, and almost 78.6 years for men. Compared with 1765, when average life expectancy was under 39 years, this is a huge improvement in historical terms. Even in the more recent past, life expectancy was less than 47 years at the start of the 20th Century, and was under 70 as recently as the 1950s. Despite these significant developments in the long-term, improvements in life expectancy stalled between 2009/11 and 2015/17, and have even gone into decline since 2020. Between 2020 and 2022, for example, life expectancy at birth fell by 23 weeks for females, and 37 weeks for males. COVID-19 in the UK The first cases of COVID-19 in the United Kingdom were recorded on January 31, 2020, but it was not until a month later that cases began to rise exponentially. By March 5 of this year there were more than 100 cases, rising to 1,000 days later and passing 10,000 cumulative cases by March 26. At the height of the pandemic in late April and early May, there were around six thousand new cases being recorded daily. As of January 2023, there were more than 24.2 million confirmed cumulative cases of COVID-19 recorded in the United Kingdom, resulting in 202,156 deaths.

On January 12, 2021, over 4.5 thousand individuals in the UK were admitted to hospital with coronavirus (COVID-19), the highest single amount since the start of the pandemic. The daily hospital cases started to rise significantly at the end of 2020 and into January 2021, however since then the number of hospitalizations fell dramatically as the UK managed to vaccinate millions against COVID-19. Overall, since the pandemic started around 994 thousand people in the UK have been hospitalized with the virus.

The total number of cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of October 6, 2022, 11,641 confirmed COVID-19 patients were in hospital in the United Kingdom. The number of COVID patients in hospitals first peaked at over 21.6 thousand on April 12, 2020 and dropped as low as 772 on September 11, 2020. However, the number of patients reached a new peak in the winter of 2020/21 with over 39.2 thousand patients in hospital on January 18, 2021.

The total number of cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

First reported in Wuhan, China, in December 2019, now more than 846,200 confirmed cases of COVID-19 are spread across 187 countries worldwide. The US and several countries in Europe such as Italy, Spain, and Belgium have continued to see a decrease in daily cases. Russia, Brazil, and Latin American countries are seeing increasing trends. India has also seen an increase in the number of new cases reported despite strict distancing measures taken early on.
Special populations analysis covered in the report include the following:
COVID-19 in children may result in systemic multisystem syndrome with severe outcomes.
Childhood routine vaccination rates drop during pandemic.
COVID-19’s impact in pregnant women unclear, though most cases are asymptomatic.
The COVID-19 pandemic could cause an increase in the prevalence of post-traumatic stress disorder (PTSD).
Complications of opioid addiction will be challenging for the management of disease during the COVID-19 pandemic. Read More

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

Contents

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

Code Base

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

Citations:

Please cite.

@article{coppock2022,

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

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

journal = {arXiv},

year = {2022},

doi = {10.48550/ARXIV.2212.08570},

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

}

@article{budd2022,

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

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

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.07738}

}

@article{Pigoli2022,

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

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

year={2022},

journal={arXiv},

doi = {10.48550/ARXIV.2212.08571}

}

The Dublin Core™ Metadata Initiative

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

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

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

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

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

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

- Date: 2021-03/2022-03

- Type: Dataset

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

- Identifier: 10.5281/zenodo.10043978

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

- Language: eng

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

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

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

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

Recent months have been unprecedented for cruise liners. Travel restrictions, sailing suspensions, falling consumer confidence and a slew of negative press are just some of the issues cruise operators are having to contend with. As the industry’s largest player, Carnival is severely impacted. Read More

Recent months have been unprecedented in the tourism sector. Travel restrictions, flight suspensions, falling consumer confidence and a slew of negative press are just some of the issues airlines are having to contend with. As one of the airline industry's leading players, Lufthansa has been severely impacted. Read More

Network metrics for USA, UK, and China, pre-COVID and COVID.

GlobalData expects the construction industry to contract by 5.5% in 2020, compared to a forecast expansion of 1.6% predicted before the COVID-19 crisis. Construction activity in the country is expected to recover in the near term in line with declining COVID-19 cases. Read More

Local authority level vaccine coverage estimates for the school-based meningococcal ACWY adolescent vaccination programme for 14 to 15 year olds.

Rationale The MenACWY vaccination was introduced into the national immunisation programme in autumn 2015 to respond to a rapid and accelerating increase in cases of invasive meningococcal group W (MenW) disease, which was declared a national incident. The MenACWY conjugate vaccine provides direct protection to the vaccinated cohort and, by reducing MenW carriage, will also provide indirect protection to unvaccinated children and adults. This follows advice from the Joint Committee on Vaccination and Immunisation (JCVI). It is routinely offered through schools in academic school Years 9 and 10 (rising 14 and rising 15 year olds). The indicator measures local authority level MenACWY vaccine coverage for students at the end of school Yr 10. Vaccination coverage is the best indicator of the level of protection a population will have against vaccine preventable communicable diseases. Coverage is closely correlated with levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise. Previous evidence shows that highlighting vaccination programmes encourages improvements in uptake levels. May also have relevance for NICE guidance PH21: Reducing differences in the uptake of immunisations (The guidance aims to increase immunisation uptake among those aged under 19 years from groups where uptake is low).

Definition of numerator Total number of adolescents in LA responsible population whose 15th birthday falls within the time period who have ever received MenACWY vaccine.

Definition of denominator Total number of adolescents attending school in LA plus adolescents resident in the LA not linked to any school whose 15th birthday falls within the time period.

Caveats On 23 March 2020, all educational settings in England were advised to close by the UK Government as part of COVID-19 pandemic measures. Although the importance of maintaining good vaccine uptake was impressed, operational delivery of all school-aged immunisation programmes was paused for a short period of time as a consequence of school closures limiting access to venues for providers and children who were eligible for vaccination and to ensure that lockdown regulations were not breached.

The NHSEI central public health commissioning and operations team rapidly established an Immunisation Task and Finish Group, with regional NHSEI and UKHSA representation. The group was established to:

assess the impact of COVID-19 on all immunisation programmes, including school-aged programmes develop technical guidance and a plan for restoration and recovery of school-aged programmes, once education settings were reopened

From 1 June 2020, some schools partially reopened for some year groups for a mini summer term. NHSEI published clinical guidance for healthcare professionals on maintaining immunisation programmes during COVID-19, and the Department of Education published further guidance which led to schools allowing vaccination sessions to resume on site.

NHSEI commissioned, school-aged immunisation providers were able to implement their restoration and recovery plans to commence catch-up during the summer of 2020. This included delivery of programmes in school and community settings following a robust risk assessment and in line with UK Government Public Health COVID-19 guidance.

In September 2020, schools across the UK reopened for general in-person attendance. During the 2020 to 2021 academic year, students were required to stay at home and learn remotely if they tested positive for COVID-19 or if they were a contact of a confirmed COVID-19 case, and so school attendance rates in England were lower than normal, especially in areas with very high COVID-19 incidence rates. In England, as part of a wider national lockdown in January 2021, schools were closed to all except children of keyworkers and vulnerable children. From early March 2021, primary schools reopened, with a phased reopening of secondary schools.

Although this led to some disruption of school-based elements of programme delivery in the 2020 to 2021 academic year, NHSEI Regional Public Health Commissioning teams worked with NHSEI commissioned school-aged immunisation providers to maintain the delivery of the routine programme and catch-up. As the routine programme is commissioned for a school-aged cohort rather than a school-based cohort, providers were able to build on existing arrangements such as community-based clinics in place for children not in mainstream education. A wide variety of local arrangements were established to ensure programme delivery continued effectively and safely in the school and community premises, during the term time and school breaks.

As of April 17, 2020, it was found 13 percent of British population thought that the government was handling the coronavirus (COVID-19) outbreak very well, this is a fall from a high point of 22 percent of Brits believing the situation was being handled very well at the end of March. According to the latest survey wave, the majority of respondents still feel the government is handling the crisis well, with 53 percent believing this to be the case.

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. 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.

Number of coronavirus publications in 2020.

In Great Britain, the share of people who have been only leaving their house for essential reasons* during the coronavirus lockdown has been decreasing since the start of April 2020. In the first survey wave, 85 percent of respondents said they only left for essential reasons, by May 17 this share had dropped to 80 percent. The latest number of cases in the UK can be found here. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Network metrics in coronavirus research.