COVID-19 is a infectious Disease which has infected more than 500 people in UK and many more people world-wide.

Acknowledgements Sincere thanks to Public Health England and Local governments. Source of Data: UK Government and Public Health UK

****Notes on the methodology**** This service shows case numbers as reported to Public Health England (PHE), matched to Administrative Geography Codes from the Office of National Statistics. Cases include people who have recovered.

Events are time-stamped on the date that PHE was informed of the new case or death.

The map shows circles that grow or shrink in line with the number of cases in that geographic area.

Data from Scotland, Wales and Northern Ireland is represented on the charts, total indicators and on the country level map layer.

Contains Ordnance Survey data © Crown copyright and database right 2020. Contains National Statistics data © Crown copyright and database right 2020.

Terms of Use No special restrictions or limitations on using the item’s content have been provided.

This feature service contains COVID-19 data automatically updated from the Public Health England (PHE) API service, daily. Using this API, this service takes the current day request minus two days. Therefore the data will always be two days behind. This is a result of the delay between PHE's specimen date and reporting date.The Polygon Layers, which all contain spatial data, provide information about the latest cumulative figures at three geographies; Local Authority, Regions and Nations. The Tables, which are not spatially aware, provide historical data for each feature. The format of these tables allow you to use the Join tool with the Polygon Layers and create a time enabled layer. This can be used within a dashboard or on the animation tool to view patterns over time.

This mapping tool enables you to see how COVID-19 deaths in your area may relate to factors in the local population, which research has shown are associated with COVID-19 mortality. It maps COVID-19 deaths rates for small areas of London (known as MSOAs) and enables you to compare these to a number of other factors including the Index of Multiple Deprivation, the age and ethnicity of the local population, extent of pre-existing health conditions in the local population, and occupational data. Research has shown that the mortality risk from COVID-19 is higher for people of older age groups, for men, for people with pre-existing health conditions, and for people from BAME backgrounds. London boroughs had some of the highest mortality rates from COVID-19 based on data to April 17th 2020, based on data from the Office for National Statistics (ONS). Analysis from the ONS has also shown how mortality is also related to socio-economic issues such as occupations classified ‘at risk’ and area deprivation. There is much about COVID-19-related mortality that is still not fully understood, including the intersection between the different factors e.g. relationship between BAME groups and occupation. On their own, none of these individual factors correlate strongly with deaths for these small areas. This is most likely because the most relevant factors will vary from area to area. In some cases it may relate to the age of the population, in others it may relate to the prevalence of underlying health conditions, area deprivation or the proportion of the population working in ‘at risk occupations’, and in some cases a combination of these or none of them. Further descriptive analysis of the factors in this tool can be found here: https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

A joint map of resources targeted towards the remedy and recovery during and after the COVID 19 crisis. Information about resources and support services provided by a number of organisations across the city.If you are a provider of services and resources, your information can be added and made public via the form available from here.If you have any questions about this dataset please email smart@leicester.gov.uk or complete the form available from here.

This file contains the digital vector boundaries for Covid Infection Survey Geography, in the United Kingdom, as at December 2020.The boundaries available are: (BFE) Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands).Contains both Ordnance Survey and ONS Intellectual Property Rights.

REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_Dec_2020_UK_BFE/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Covid_Infection_Survey_Dec_2020_UK_BFE/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_Dec_2020_UK_BFE/MapServer

A lookup file between 2020 Covid Infection Survey Geography to 2020 Local Authority Districts to 2019 Regions in the United Kingdom, as at 1 October 2020. (File size - 56KB) Field Names - CIS20CD, LAD20CDS, RGN19CD, RGN19NM, FIDField Types - Text, Text, Text, Text, NumericField Lengths - 9, 255, 9, 255FID = The FID, or Feature ID is created by the publication process when the names and codes / lookup products are published to the Open Geography portal. REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/CIS20_to_RGN19_Lookup_b5eba17b771a43d7a6b956376b274c8f/FeatureServer

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.

As of January 13, 2023, Bulgaria had the highest rate of COVID-19 deaths among its population in Europe at 548.6 deaths per 100,000 population. Hungary had recorded 496.4 deaths from COVID-19 per 100,000. Furthermore, Russia had the highest number of confirmed COVID-19 deaths in Europe, at over 394 thousand.

Number of cases in Europe During the same period, across the whole of Europe, there have been over 270 million confirmed cases of COVID-19. France has been Europe's worst affected country with around 38.3 million cases, this translates to an incidence rate of approximately 58,945 cases per 100,000 population. Germany and Italy had approximately 37.6 million and 25.3 million cases respectively.

Current situation In March 2023, the rate of cases in Austria over the last seven days was 224 per 100,000 which was the highest in Europe. Luxembourg and Slovenia both followed with seven day rates of infections at 122 and 108 respectively.

This file contains the digital vector boundaries for Covid Infection Survey Geography, in the United Kingdom, as at December 2020.The boundaries available are: (BUC) Ultra generalised (500m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.

REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_Dec_2020_UK_BUC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Covid_Infection_Survey_Dec_2020_UK_BUC/WFSServerREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_Dec_2020_UK_BUC/MapServer

The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

The Community Engagement team at the Greater London Authority (GLA) commissioned this report to identify and examine past and present projects which involve collecting Londoners experiences of COVID-19 through a variety of creative and non-traditional materials. The purpose of the report is to: provide an overview of projects and activities which record Londoners COVID-19 stories and experiences. outline who is responsible for these projects and activities (individuals, museums, community groups, charities, community interest groups, non-profits, other institutions and organisations). analyse the voices of individuals/groups/communities targeted in the projects and activities. highlight obvious gaps in the collected data which can inform future programmes geographically map out projects and other activities which record COVID-19 stories and experiences across Greater London. The data provides insight into trends and patterns in COVID-19 collecting projects and activities that have been carried out in London from March 2020 to March 2021. Reflections and final suggestions on how to navigate these projects and activities for specific next steps in the Community-Led Recovery Programme, targeted missions, suggestions etc. will be discussed later in this report. In particular, this report provides information relevant to the London Community Story (LCS) Programme, one of the two strands of the Community-Led Recovery programme. Alongside this report is a dataset outlining 160 COVID-19 collecting projects that took place in London. The dataset gives project names, boroughs, material types, collecting organisation type and organisation names. We encourage you to use this dataset as a starting point and then do your own additional research on the 160 projects. If you are aware of a project that has not been included, please let us know and we can add it.

CRUSH Covid maps outbreaks in Uppsala County by visualising the number of cases, test positivity, and geographic distribution, among other things. Data for each postal code is available for download and reuse.

This map shows where the adult population (population 18+) with limited English ability. This is shown by the percent and count of adult population. The pop-up provides more information about the breakdown of language in the area.The pattern is shown by states, counties, and tracts. There are bookmarks in the map to help you jump to different cities. You can also search for any city in the Untied States to learn more about that area. The data is from the U.S. Census Bureau's American Community Survey (ACS) most current 5-year estimates, Table B16004. Layers also contain data from Table B16003. The data is updated annually when the Census releases their newest estimates. To learn more about the ACS tables used in this layer and additional information about the layer, visit the layer metadata here.

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and Local ‘A’ Roads in England, in 2020.

Additional http://bit.ly/COVID_Congestion_Analysis">Analysis on the impact of the Coronavirus (COVID-19) pandemic on the road journeys is also available. This story map contains charts and interactive maps for road journeys in England.

On the Strategic Road Network (SRN) for 2020, the average delay is estimated to be 6.7 seconds per vehicle per mile compared to speed limits travel times, a 29.5% decrease compared to 2019.

The average speed is estimated to be 61.8mph, 5.1% up on 2019.

In 2020, on average 42.1% of additional time was needed compared to speed limits travel times, on individual road sections of the SRN to ensure on time arrival. This is down 25.2 percentage points compared to 2019, so on average a lower proportion of additional time is required.

On local ‘A’ roads for 2020, the average delay is estimated to be 33.9 seconds per vehicle per mile compared to free flow travel times. This is a decrease of 22.8% on 2019.

The average speed is estimated to be 27.3 mph. This is an increase of 8.2% on 2019.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with previous time periods. While values had previously been moving towards their pre-lockdown levels, this trend appears to have reversed in the months following September 2020.

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As of January 18, 2023, Portugal had the highest COVID-19 vaccination rate in Europe having administered 272.78 doses per 100 people in the country, while Malta had administered 258.49 doses per 100. The UK was the first country in Europe to approve the Pfizer/BioNTech vaccine for widespread use and began inoculations on December 8, 2020, and so far have administered 224.04 doses per 100. At the latest data, Belgium had carried out 253.89 doses of vaccines per 100 population. Russia became the first country in the world to authorize a vaccine - named Sputnik V - for use in the fight against COVID-19 in August 2020. As of August 4, 2022, Russia had administered 127.3 doses per 100 people in the country.

The seven-day rate of cases across Europe shows an ongoing perspective of which countries are worst affected by the virus relative to their population. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Yearly citation counts for the publication titled "Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study".

ClinSpEn Parallel Corpus Collection

This repository contains the complete ClinSpEn corpus collection, which was used for the ClinSpEn shared task at Biomedical WMT 2022.

ClinSpEn is a collection of Gold Standard EN-ES parallel corpora of different types of clinical data: case reports, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. It includes development and test data translated by professional medical translators that can be used to train and benchmark clinical EN-ES machine translation systems. Additionally, monolingual background data is provided so that the systems' performance can be analyzed in unseen data.

If you use this dataset, please cite:

inproceedings{biowmt22,
 title={Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports},
 author={Neves, Mariana and Yepes, Antonio Jimeno and Siu, Amy and Roller, Roland and Thomas, Philippe and Navarro, Maika Vicente and Yeganova, Lana and Wiemann, Dina and Di Nunzio, Giorgio Maria and Vezzani, Federica and others},
 booktitle={WMT22-Seventh Conference on Machine Translation},
 pages={694--723},
 year={2022}
}

Data Description

ClinSpEn proposes three different sub-tracks, each based on a different type of clinical data:

1. Clinical Cases:

Parallel EN-ES COVID-19 clinical case reports. The direction of this sub-track is EN>ES.

The dataset’s case reports were carefully selected to cover a wide range of aspects related to the disease: different types of patients (children, adults, elderly and pregnant people, babies), different comorbidities (cancer, mental health issues, immunosuppressed patients) and symptomatology (mild and severe presentations, dermatologic, immunologic and psychiatric manifestations, thrombosis, ...). The reports were translated from English to Spanish by a professional medical translator on a first step and revised by a clinical expert on a second step.

The sample (dev) set and test set are made up of parallel txt files (50 and 152 documents each, respectively), with the Spanish version having a “.es” extension and the English files having a “.en” extension. Each report has been parallelized so that every sentence’s line number corresponds to the same sentence’s line number in both languages.

The background data (9,804 files) is made up of a TSV file with four columns: filename, document number, line number and English line. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.

If you need to map the entries in the join test + background document provided in earlier versions, you may use the "clinspen_clinicalcases_test-set_filename_mapping.tsv" file.

2. Clinical Terminology:

Parallel EN-ES clinical terms extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions and translated and revised by professional medical translators. The direction of this sub-track is ES>EN.

The sample (dev) set contains 7,000 terms as a tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms.

The test data (12,128 terms) is made up of a TSV file with three columns: term number, English term and Spanish term.

The background data (201,890 terms) is made up of a TSV file with two columns: term number and Spanish term.

The term number columns can be used to map the entries in the join test + background document provided in earlier versions.

3. Ontology Concepts:

Parallel EN-ES concepts extracted from various open biomedical ontologies and taxonomies and then manually translated by a professional medical translator. The direction of this sub-track is EN>ES.

The sample (dev) data includes 400 concepts. The terms are presented as tab-separated file (TSV), with the first column corresponding to English terms and the second column to Spanish terms. The third column includes the term’s origin ontology and its correspondent ID (separated by an underscore), while the fourth one includes a link to the concept in OBO Library.

The test data (1,789 concepts) is made up of a TSV file with five columns: term number, English term, Spanish term, ontology id and OBO library URL.

The background data (299,408 concepts) is made up of a TSV file with four columns: term number, English term, ontology id and OBO library URL.

The term number columns can be used to map the entries in the join test + background document provided in earlier versions.

Related Links:

• ClinSpEn website with more information: https://temu.bsc.es/clinspen/

• WMT website: https://www.statmt.org/wmt22/

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Contact

If you have any question or suggestion, please contact us at the following addresses:

- Salvador Lima-López (

The data collection consists of 40 qualitative interviews with Polish migrant essential workers living in the UK and 10 in-depth expert interviews with key stakeholders providing information and support to migrant workers in the UK. All migrant interviews are in Polish. Six of the expert interviews with key stakeholders are in English and four are in Polish. Fieldwork was conducted fully online during the Covid-19 pandemic between March and August 2021, following the third UK-wide Covid-19 lockdown. Restrictions were still in place in some localities. Interviews took place shortly after the end of the transition period concluding the UK’s European Union exit on 1 January 2021. All Polish migrant worker interviewees entered the UK before 1 January 2021 and had the option to apply to the EU Settlement Scheme.

The objectives of the qualitative fieldwork were to: 1. To synthesise empirical and theoretical knowledge on the short- and long-term impacts of COVID-19 on migrant essential workers. 2. To establish how the pandemic affected Polish migrant essential worker's lives; and expert interviews with stakeholders in the public and third/voluntary sector to investigate how to best support and retain migrant essential workers in COVID-19 recovery strategies. The project also involved: - co-producing policy outputs with partner organisations in England and Scotland; and - an online survey to measure how Polish migrant essential workers across different roles and sectors were impacted by COVID-19 in regard to health, social, economic and cultural aspects, and intentions to stay in the UK/return to Poland (deposited separately to University of Sheffield). Key findings included significant new knowledge about the health, social, economic and cultural impacts of Covid-19 on migrant essential workers. Polish essential workers were severely impacted by the pandemic with major mental health impacts. Mental health support was insufficient throughout the UK. Those seeking support typically turned to private (online) services from Poland as they felt they could not access them in the UK because of language or cultural barriers, lack of understanding of the healthcare system and pathways to mental health support, support being offered during working hours only, or fear of the negative impact of using mental health services on work opportunities. Some participants were in extreme financial hardship, especially those with pre-settled status or those who arrived in the UK during the pandemic. The reasons for financial strain varied but there were strong patterns linked to increased pressure at work, greater exposure to Covid-19 as well as redundancies, pay cuts and rejected benefit applications. There was a tendency to avoid applying for state financial support. These impacts were compounded by the sense of isolation, helplessness, or long-distance grief due to inability to visit loved ones in Poland. Covid-19 impacted most detrimentally on women with caring responsibilities, single parents and people in the health and teaching sectors. The most vulnerable Polish migrant essential workers - e.g. those on lower income, with pre-existing health conditions, restricted access to support and limited English proficiency - were at most risk. Discrimination was reported, including not feeling treated equally in the workplace. The sense of discrimination two-fold: as essential workers (low-paid, low-status, unsafe jobs) and as Eastern Europeans (frequent disciplining practices, treated as threat, assumed to be less qualified). In terms of future plans, some essential workers intended to leave the UK or were unsure about their future place of residence. Brexit was a major reason for uncertain settlement plans. Vaccine hesitancy was identified, based on doubts about vaccination, especially amongst younger respondents who perceived low risks of Covid-19 for their own health, including women of childbearing age, who may have worries over unknown vaccine side-effects for fertility. Interview participants largely turned to Polish language sources for vaccination information, especially social media, and family and friends in Poland. This promoted the spread of misinformation as Poland has a strong anti-vaccination movement.

COVID-19 has exposed the UK's socio-economic dependence on a chronically insecure migrant essential workforce. While risking their lives to offset the devastating effects of the pandemic, migrant workers reportedly find themselves in precarious professional and personal circumstances (temporary zero-hours contracts, work exploitation, overcrowded accommodation, limited access to adequate health/social services including Universal Credit). This project will investigate the health, social, economic and cultural impacts of COVID-19 on the migrant essential workforce and how these might impact on their continued stay in the UK. It will focus on the largest non-British nationality in the UK, the Polish community, who - while employed across a range of roles and sectors - are overrepresented in lower-paid essential work. We will use this group as an illustrative case study to make wider claims and policy recommendations about migrant work during the pandemic. Using a mixed-methods approach, we will conduct: an online survey to map COVID-19 impacts; in-depth qualitative interviews to establish how the pandemic has affected worker's lives; and expert interviews with stakeholders to investigate how to best support and retain migrant essential workers in COVID-19 recovery strategies. The results will generate the first comprehensive UK-wide dataset on the experiences of migrant essential workers against the backdrop of COVID-19. The research, co-produced with partner organisations (Polish Expats Associations, Fife Migrants Forum, PKAVS Minority Communities Hub and Polish Social and Cultural Association), will generate a policy briefing, a toolkit for employers in the essential work sectors, information resources for migrant workers, alongside media and academic outputs.