As of July 30, 2020, there had been more confirmed cases of coronavirus (COVID-19) among women in England compared to men. The data shows that there are few confirmed cases among children, while there have been approximately nine thousand confirmed cases for both men and women aged 80 to 84 years.

As of July 30, there have been 302,301 confirmed coronavirus cases in the UK, and the regional breakdown of cases can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

During the fourth quarter of 2024, data breaches exposed more than a million user data records in the United Kingdom (UK). The figure decreased significantly from nearly 41 million in the quarter prior. Overall, the time between the first quarter of 2022 and the fourth quarter of 2023, saw the lowest number of exposed user data accounts.

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

United Kingdom recorded 24603076 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, United Kingdom reported 225324 Coronavirus Deaths. This dataset includes a chart with historical data for the United Kingdom Coronavirus Cases.

In early-February 2020, the first cases of COVID-19 in the United Kingdom (UK) were confirmed. The number of cases in the UK increased significantly at the end of 2021. On January 13, 2023, the number of confirmed cases in the UK amounted to 24,243,393. COVID deaths among highest in Europe There were 202,157 confirmed coronavirus deaths in the UK as of January 13, 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Current infection rate in Europe The current infection rate in the UK was 50 cases per 100,000 population in the last seven days as of January 16. San Marino had the highest seven day rate of infections in Europe at 336.

Provisional counts of the number of deaths registered in England and Wales, including deaths involving coronavirus (COVID-19), by local authority, health board and place of death in the latest weeks for which data are available. The occurrence tabs in the 2021 edition of this dataset were updated for the last time on 25 October 2022.

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.

[The spreadsheet is organised into two parts. The first contains a broad set of annual data covering the UK national accounts and other financial and macroeconomic data stretching back in some cases to the late 17th century. The second and third sections cover the available monthly and quarterly data for the UK to facilitate higher frequency analysis on the macroeconomy and the financial system. The spreadsheet attempts to provide continuous historical time series for most variables up to the present day by making various assumptions about how to link the historical components together. But we also have provided the various chains of raw historical data and retained all our calculations in the spreadsheet so that the method of calculating the continuous times series is clear and users can construct their own composite estimates by using different linking procedures., This dataset contains a broad set of historical data covering the UK national accounts and other financial and macroeconomic data stretching back in some cases to the late 17th century.]

This dataset of historical poor law cases was created as part of a project aiming to assess the implications of the introduction of Artificial Intelligence (AI) into legal systems in Japan and the United Kingdom. The project was jointly funded by the UK’s Economic and Social Research Council, part of UKRI, and the Japanese Society and Technology Agency (JST), and involved collaboration between Cambridge University (the Centre for Business Research, Department of Computer Science and Faculty of Law) and Hitotsubashi University, Tokyo (the Graduate Schools of Law and Business Administration). As part of the project, a dataset of historic poor law cases was created to facilitate the analysis of legal texts using natural language processing methods. The dataset contains judgments of cases which have been annotated to facilitate computational analysis. Specifically, they make it possible to see how legal terms have evolved over time in the area of disputes over the law governing settlement by hiring.

A World Economic Forum meeting at Davos 2019 heralded the dawn of 'Society 5.0' in Japan. Its goal: creating a 'human-centred society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space.' Using Artificial Intelligence (AI), robotics and data, 'Society 5.0' proposes to '...enable the provision of only those products and services that are needed to the people that need them at the time they are needed, thereby optimizing the entire social and organizational system.' The Japanese government accepts that realising this vision 'will not be without its difficulties,' but intends 'to face them head-on with the aim of being the first in the world as a country facing challenging issues to present a model future society.' The UK government is similarly committed to investing in AI and likewise views the AI as central to engineering a more profitable economy and prosperous society.

This vision is, however, starting to crystallise in the rhetoric of LegalTech developers who have the data-intensive-and thus target-rich-environment of law in their sights. Buoyed by investment and claims of superior decision-making capabilities over human lawyers and judges, LegalTech is now being deputised to usher in a new era of 'smart' law built on AI and Big Data. While there are a number of bold claims made about the capabilities of these technologies, comparatively little attention has been directed to more fundamental questions about how we might assess the feasibility of using them to replicate core aspects of legal process, and ensuring the public has a meaningful say in the development and implementation.

This innovative and timely research project intends to approach these questions from a number of vectors. At a theoretical level, we consider the likely consequences of this step using a Horizon Scanning methodology developed in collaboration with our Japanese partners and an innovative systemic-evolutionary model of law. Many aspects of legal reasoning have algorithmic features which could lend themselves to automation. However, an evolutionary perspective also points to features of legal reasoning which are inconsistent with ML: including the reflexivity of legal knowledge and the incompleteness of legal rules at the point where they encounter the 'chaotic' and unstructured data generated by other social sub-systems. We will test our theory by developing a hierarchical model (or ontology), derived from our legal expertise and public available datasets, for classifying employment relationships under UK law. This will let us probe the extent to which legal reasoning can be modelled using less computational-intensive methods such as Markov Models and Monte Carlo Trees.

Building upon these theoretical innovations, we will then turn our attention from modelling a legal domain using historical data to exploring whether the outcome of legal cases can be reliably predicted using various technique for optimising datasets. For this we will use a data set comprised of 24,179 cases from the High Court of England and Wales. This will allow us to harness Natural Language Processing (NLP) techniques such as named entity recognition (to identify relevant parties) and sentiment analysis (to analyse opinions and determine the disposition of a party) in addition to identifying the main legal and factual points of the dispute, remedies, costs, and trial durations. By trailing various predictive heuristics and ML techniques against this dataset we hope to develop a more granular understanding as to the feasibility of predicting dispute outcomes and insight to what factors are relevant for legal decision-making. This will allow us to then undertake a comparative analysis with the results of existing studies and shed light on the legal contexts and questions where AI can and cannot be used to produce accurate and repeatable results.

Trends in Crime Survey for England and Wales (CSEW) crime and Home Office police recorded crime for England and Wales, by offence type. Also includes more detailed data on crime such as violence, fraud and anti-social behaviour.

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

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

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

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

The number of deaths registered in England and Wales due to and involving coronavirus (COVID-19). Breakdowns include age, sex, region, local authority, Middle-layer Super Output Area (MSOA), indices of deprivation and place of death. Includes age-specific and age-standardised mortality rates.

Context

Within the current response of a pandemic caused by the SARS-CoV-2 coronavirus, which in turn causes the disease, called COVID-19. It is necessary to join forces to minimize the effects of this disease.

Therefore, the intention of this dataset is to save data scientists time:

• Gather the data at the country level, encoding the country with its ISO code to allow easy access to other data
• Perform pre-processing of data, calculations of increments and other indicators that can facilitate modeling.
• Add the response of the governments over time so that it can be taken into account in the modeling.
• Daily update.

This dataset is not intended to be static, so suggestions for expanding it are welcome. If someone considers it important to add information, please let me know.

Content

The data contained in this dataset comes mainly from the following sources:

Source: Center for Systems Science and Engineering (CSSE) at Johns Hopkins University https://github.com/CSSEGISandData/COVID-19 Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Source: OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker Hale, Thomas and Samuel Webster (2020). Oxford COVID-19 Government Response Tracker. Data use policy: Creative Commons Attribution CC BY standard.

The original data is updated daily.

The features it includes are:

• Country Name

• Country Code ISO 3166 Alpha 3

• Date

• Incidence data:

  • confirmed
  • deaths
  • recoveries

• Daily increments:

  • confirmed_inc
  • deaths_inc
  • recoveries_inc

• Empirical Contagion Rate - ECR

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3508582%2F3e90ecbcdf76dfbbee54a21800f5e0d6%2FECR.jpg?generation=1586861653126435&alt=media" alt="">

• GOVERNMENT RESPONSE TRACKER - GRTStringencyIndex

  OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER - Stringency Index

• Indices from Start Contagion

  • Days since the first case of contagion is overcome
  • Days since 100 cases are exceeded

• Percentages over the country's population:

  • confirmed_PopPct
  • deaths_PopPct
  • recoveries_PopPct

The method of obtaining the data and its transformations can be seen in the notebook:

Notebook COVID-19 Data by country with Government Response

Photo by Markus Spiske on Unsplash

This dataset contains daily data trackers for the COVID-19 pandemic, aggregated by month and starting 18.3.20. The first release of COVID-19 data on this platform was on 1.6.20. Updates have been provided on a quarterly basis throughout 2023/24. No updates are currently scheduled for 2024/25 as case rates remain low. The data is accurate as at 8.00 a.m. on 8.4.24. Some narrative for the data covering the latest period is provided here below: Diagnosed cases / episodes • As at 3.4.24 CYC residents have had a total 75,556 covid episodes since the start of the pandemic, a rate of 37,465 per 100,000 of population (using 2021 Mid-Year Population estimates). The cumulative rate in York is similar to the national (37,305) and regional (37,059) averages. • The latest rate of new Covid cases per 100,000 of population for the period 28.3.24 to 3.4.24 in York was 1.49 (3 cases). The national and regional averages at this date were 1.67 and 2.19 respectively (using data published on Gov.uk on 5.4.24).

Number of confirmed cases of FSE (Feline Spongiform Ecephalopathy) in domestic cats by year of birth. This dataset includes the following fields: Year of Birth (of the cat); Number of cases (born in that year). Please note: this data is available as part of a wider report on TSE surveillance, published on gov.uk.
Please note: this dataset records no data after 1996, as no confirmed cases of FSE have been reported since then.

Table showing the number of animal BSE (Bovine Spongiform Encephalopathy) cases, and the number of farms with BSE cases. The table also breaks down the type of farms where cases have been detected, and includes age of the oldest and youngest animals with detected cases. The dataset includes the following fields: Total farms (number of farms with cases); Total cases (of BSE); 'Dairy Farms, Suckler Farms, Mixed Farms, Not Recorded' (Column B gives the number of farms in each type with cases, and Column C gives the percentage of these farms in each category); 'Dairy Farms, Suckler Farms, Mixed cases, Not recorded' (Column B gives the number of farms in each type with cases, and Column C gives the percentage of these farms in each category); Purchased cases (how many cases were from animals purchased); Homebred cases (how many cases were from homebred animals), Not recorded (Column B gives the number of cases where the purchase/homebred data was not record, and Column C gives the percentage); Confirmed dairy herd incidence (as a % of total); Confirmed suckler herd incidence (as a % of the total); Confirmed total herd incidence (as a % of the total); youngest confirmed case (age in months); Oldest confirmed case (age in months).

Please note: this data is available as part of a wider report on TSE surveillance, published on gov.uk. Attribution statement:

This statistical press release provides statistics for writs and originating summonses issued, cases disposed and orders made in respect of mortgages in the Chancery Division of the Northern Ireland High Court.

Source agency: Northern Ireland Statistics and Research Agency

Designation: National Statistics

Language: English

Alternative title: Mortgage Press Release

According to a survey carried out in the United Kingdom (UK) in March 2020, 42 percent of British people trust the released figures of coronavirus (COVID-19) infection and mortality rates, while 15 percent do not trust the figures. Although, 40 percent are unsure whether to trust the figures or not.

As of March 22, the UK had 5,683 confirmed cases of coronavirus. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Our Europe B2B Data is a powerhouse of business intelligence, offering a comprehensive repository of over 52 million contacts, comprising decision-makers, owners, and founders. Delving into the intricacies of our dataset, here's what makes it a cut above the rest:

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