As of February 17, 2022, there had been approximately 139.5 thousand deaths due to COVID-19 recorded in England. When broken down by age, almost 37 percent of these deaths occurred in the age group 80 to 89 years, while a further fifth of deaths were recorded among over 90 year olds. For further information about the COVID-19 pandemic, please visit our dedicated Facts and Figures page.

As of February 4, 2022, in the age group 75 to 84 years old COVID-19 was involved in the deaths of 32,780 males and 23,390 females in the United Kingdom. Furthermore, since the pandemic started over 72 thousand deaths in the UK among those aged 85 years and above involved COVID-19. For further information about the COVID-19 pandemic, please visit our dedicated Facts and Figures page.

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.

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.

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: https://blog.ukdataservice.ac.uk/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Dataset description

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

Region files

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

Country files

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

NHS Region files

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

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

Between January 1, 2021 and May 31, 2022, there were approximately 30.6 thousand deaths involving COVID-19 among 80 to 89 year olds in England, with over 14 thousand deaths occurring among unvaccinated people in this age group. Across all the age groups in the provided time interval, deaths involving COVID-19 among the unvaccinated population was around double the amount of people who received at least two doses of a vaccine. For further information about the COVID-19 pandemic, please visit our dedicated Facts and Figures page.

In 2023 life expectancy for both males and females at birth rose when compared to 2022. Male life expectancy increased from 78.58 years to 78.82 years, and from 82.57 years to 82.77 years for females. Throughout most of this period, there is a steady rise in life expectancy for both males and females, with improvements in life expectancy beginning to slow in the 2010s and then starting to decline in the 2020s. Life expectancy since the 18th Century Although there has been a recent dip in life expectancy in the UK, long-term improvements to life expectancy stretch back several centuries. In 1765, life expectancy was below 39 years, and only surpassed 40 years in the 1810s, 50 years by the 1910s, 60 years by the 1930s and 70 by the 1960s. While life expectancy has broadly improved since the 1700s, this trajectory was interrupted at various points due to wars and diseases. In the early 1920s, for example, life expectancy suffered a noticeable setback in the aftermath of the First World War and Spanish Flu Epidemic. Impact of COVID-19 While improvements to UK life expectancy stalled during the 2010s, it wasn't until the 2020s that it began to decline. The impact of COVID-19 was one of the primary factors in this respect, with 2020 seeing the most deaths in the UK since 1918. The first wave of the pandemic in Spring of that year was a particularly deadly time, with weekly death figures far higher than usual. A second wave that winter saw a peak of almost 5,700 excess deaths a week in late January 2021, with excess deaths remaining elevated for several years afterward.

As of January 11, 2023, the highest number of deaths due to the coronavirus in Sweden was among individuals aged 80 to 90 years old. In this age group there were 9,124 deaths as a result of the virus. The overall Swedish death toll was 22,645 as of January 11, 2023.

The first case of coronavirus (COVID-19) in Sweden was confirmed on February 4, 2020. The number of cases has since risen to over 2.68 million, as of January 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

As of January 31, 2022, COVID-19 was the underlying cause for the deaths of 4,251 people in Scotland between 80 and 89 years of age. In that age group, there were 2,172 male deaths and 2,079 female deaths. A further 2,131 deaths involving COVID-19 were recorded among over 90 year olds. In England, the age group 80 to 89 years also had the highest number of deaths involving COVID-19. For further information about the 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

COVID-19 Dataset

Coronavirus-19 pandemic has spread widely in a short time and causing thousands of deaths across the world. Coronavirus disease 2019 caused by (SARS-CoV-2) which stands for severe acute respiratory syndrome coronavirus 2. Coronavirus disease 2019 caused by (SARS-CoV-2) which stands for severe acute respiratory syndrome coronavirus 2. The clinical symptoms of COVID-19 are sore throat, fever, cough, asthenia, myalgia, rhinorrhea, and headache. in addition, loss of smell and gustatory (taste) dysfunction [1]. The diagnosis and detection are confirmed utilizing reverse transcriptase-polymerase chain reaction (RT-PCR) test. But, RT-PCR has low sensitivity. So, towards alternative tests such as CT scans and chest X-rays (CXR) images. The chest X-rays characterizes by the most widespread, cheaper, and faster than CT scans.

Content

The COVID-19 with Chest X-rays images dataset composed of 299 confirmed cases of COVID-19 and 349 normal images. The 299 images of COVID-19 includes 145 CXR images collected from several medical websites [2-8] and 149 CXR images adapted from [9] and normal images adapted from the Kaggle Pneumonia dataset [10].

COVID-19 CXR dataset divided and labeled into two folders (Train-Images and Test-images). Train images include ( 239 confirmed COVID-19 and 289 normal cases). Test images include (60 COVID-19 and 60 normal).

Continuously, the dataset will be provided with new cases of COVID-19.

Acknowledgements

Many thanks prof. Joseph Cohen and your team, and all medical websites that published CXR and CT images of confirmed COVID-19 Also, thanks a lot for Paul Mooney to available normal cases. To collect this COVID-19 dataset with the aim of facilitating and saving time for researchers and developers.

Inspiration

This data is for the purposes of scientific research and a development deep learning / machine learning models. To assist radiologists in diagnosing and detection COVID-19 disease in early phases of patients, curing them and reducing the prevention of the spread of infection.

References

[1] https://www.ecdc.europa.eu/en/covid-19/latest-evidence/clinical [2] https://www.sirm.org/en/category/articles/covid-19-database/ [3] https://www.eurorad.org/advanced-search?search=COVID [4] https://radiopaedia.org/search?utf8=%E2%9C%93&q=covid&scope=all&lang=us [5] https://cases.rsna.org/coronavirus [6] https://www.nejm.org/coronavirus?query=main_nav_lg [7] https://radiologyassistant.nl/chest/covid-19-ct-findings-in-25-patients [8] https://www.radiologymasterclass.co.uk/gallery/covid-19/covid-19-image-gallery [9] https://github.com/ieee8023/covid-chestxray-dataset [10] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

In 2022 life expectancy for both males and females at birth fell when compared to 2021. Male life expectancy fell from 78.71 years to 78.57 years, and from 82.68 years to 82.57 years for women. Throughout most of this period, there is a steady rise in life expectancy for both males and females, with improvements in life expectancy beginning to slow in the 2010s and then starting to decline in the 2020s. Life expectancy since the 18th Century Although there has been a recent dip in life expectancy in the UK, long-term improvements to life expectancy stretch back several centuries. In 1765, life expectancy was below 39 years, and only surpassed 40 years in the 1810s, 50 years by the 1910s, 60 years by the 1930s and 70 by the 1960s. While life expectancy has broadly improved since the 1700s, this trajectory was interrupted at various points due to wars and diseases. In the early 1920s, for example, life expectancy suffered a noticeable setback in the aftermath of the First World War and Spanish Flu Epidemic. Impact of COVID-19 While improvements to UK life expectancy stalled during the 2010s, it wasn't until the 2020s that it began to decline. The impact of COVID-19 was one of the primary factors in this respect, with 2020 seeing the most deaths in the UK since 1918. The first wave of the pandemic in Spring of that year was a particularly deadly time, with weekly death figures far higher than usual. A second wave that winter saw a peak of almost 5,700 excess deaths a week in late January 2021, with excess deaths remaining elevated for several years afterward.

In 2023, the rate of suicides among both men and women in England was at their highest recorded rates. The rate of among males was 17.4 per 100,000 population and among females it was 5.7 per 100,000. Recent years have seen an increase again for both genders, however, the rate of suicide for men has remained significantly higher than for women. Individuals seeking help for mental health issuesIn Great Britain, almost 70 percent have never visited a mental health professional, while eighteen percent consult with one at least once a year. Additionally, almost 60 percent of those with a psychiatric condition do not take any medication to control their condition. Mental health of young peopleThe COVID-19 pandemic had a huge impact of the mental health of many people, particularly young people. The share of all adults reporting to having experienced symptoms of depression doubled during the pandemic compared to before. Although for those in the age group 16 to 39 years, depression prevalence tripled. Among young people that had mental health concerns prior to the pandemic, a significant majority of those surveyed reported that their life had become worse due to the impact of the pandemic and subsequent restrictions.