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.

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.

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

This file contains the digital vector boundaries for Covid Infection Survey Geography, in the United Kingdom, as at December 2020.The boundaries available are: (BGC) Generalised (20m) - 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_BGC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Covid_Infection_Survey_Dec_2020_UK_BGC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Covid_Infection_Survey_Dec_2020_UK_BGC/MapServer

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.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

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 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.

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

As of March 13, Austria had the highest rate of coronavirus (COVID-19) cases reported in the previous seven days in Europe at 224 cases per 100,000. Luxembourg and Slovenia have recorded 122 and 108 cases per 100,000 people respectively in the past week. Furthermore, San Marino had a rate of 97 cases in the last seven days.
Since the pandemic outbreak, France has been the worst affected country in Europe with over 38.3 million cases as of January 13. The overall incidence of cases in every European country can be found here.

For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

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.

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" class="govuk-link">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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The project had Four Research Stages

Stage 1 – Global Mapping Exercise Aim: Map and develop typologies of the pandemic’s impact on the food/education/play-leisure nexus, with a focus on young people’s vulnerabilities globally, based on an international, integrative review of research and policy literatures. Stage 2: – National and Regional Mapping (Brazil, South Africa, UK) Aim: Examine key impacts of pandemic-related policy on young people’s access to and adaptations around food, education and play/leisure at the national, regional and local scale. Stage 3: Zooming in on local adaptations of young people in monetary-poor households Aim: In-depth research with professional stakeholders and young people in each case study region, with a focus on incremental and innovative strategies and the impact of those adaptations on everyday survival and recovery. In England, this research took place in Birmingham and the West Midlands. In total, we worked with 87 young people, using qualitative methods such as interviews and visual mapping. The research was co-produced with young people: we worked with a core group of ten young people from Birmingham City Council’s Youth Voice team, who co-designed some of the methods, undertook peer research with some of the young people in our sample, and co-analysed data. Stage 4: Co-design of solutions to foster young people’s recovery and resilience Aim: Co-design solutions with our community of young people and key professionals that will help vulnerable young people to recover and be prepared in the eventuality of future major health and socio-economic crises. In England, this process took place in Birmingham and the West Midlands and involved the same core group discussing the project’s main findings. Through a series of workshops, young people’s recommendations were created and tested with us and a selected group of professional stakeholders.

Stage 1 - Interviews with key organisations working in the food/education/play sector and with children and youth.

The team conducted 32 interviews with key organisations between February and June 2023. The aim was to situate and identify what had been the key impacts of pandemic-related policy towards the food, education, play/leisure nexus of issues facing young people during and after COVID-19, in England. It also sought to examine what policy/programmes/initiatives were developed, and how local places mattered (including home life/household contexts). To do so, we identified representatives from a range of organisations that played a key role in supporting young people and/ or in assessing the impacts of the pandemic on them.

Sampling was done through desk-based research based on a review of national and regional review of the literature and reports and further on snowballing, we identified non-governmental and non-profit organisations that played a key contribution in supporting young people and/or assessing the impact and repercussions of the pandemic on them. Selection of the interviews was made either through their role across the country or because of their contribution at regional and city levels. The number of 30 was considered as commensurate with the methods used in similarly-sized comparative projects of similar scale. This included representatives from the following types of organisations:

• Charities (incl. Foundations and Think-Tanks) working either across England or in specific English regions, and specialized in the following sectors: food education, food policy, food provision (including food banks) and healthy food; education provision, education and digital technology, education policy, education and youth, social mobility and educational disadvantage; play provision, play policy; support to disadvantaged and vulnerable young people. • Not-for profit social enterprises focusing on youth education, youth employment, food and nutrition. • Schools/Colleges. • Private Companies specialized in supporting education organisations and play provision. • Research Institutions with specific expertise in education, food and health and children/young people. • Local and Combined Authorities. • Diocesan and Faith groups. • National networks representing community organisations in the faith and play sector. • Young People Ambassadors.

While looking at England as a whole, we also zoomed on West Midlands/Birmingham. The West Midlands was one of the hardest-hit parts of the UK during COVID-19. The region includes some of the most deprived neighbourhoods and a younger than average population. The intent of the interviews was twofold: 1) to understand each organisation’s response to supporting young people during/after COVID-19, and 2) from the organisation’s views, to identify what adaptations and tactics young people used to deal with the challenges that COVID-19 and associated lockdowns presented. Interview questions focused on the following themes: The role of the organisation and how they engaged with young people, the...

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and local ‘A’ roads in England, in 2022.

On the Strategic Road Network (SRN) for 2022, the average delay is estimated to be 9.3 seconds per vehicle per mile (spvpm), compared to free flow, a 9.4% increase on 2021 and a 2.1% decrease on 2019.

The average speed is estimated to be 58.1 mph, down 1.4% from 2021 and up 0.2% from 2019.

On local ‘A’ roads for 2022, the average delay was estimated to be 45.5 seconds per vehicle per mile compared to free flow, up 2.5% from 2021 and down 2.8% from 2019 (pre-coronavirus)

The average speed is estimated to be 23.7 mph, down 1.7% from 2021 and up 2.2% from 2019 (pre-coronavirus).

Average speeds in 2022 have stabilised towards similar trends observed before the effects of the coronavirus pandemic.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As some of these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with other time periods. Additional http://bit.ly/COVID_Congestion_Analysis" class="govuk-link">analysis on the impact of the coronavirus pandemic on road journeys in 2020 is also available. This Storymap contains charts and interactive maps for road journeys in England in 2020.

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A lookup file between 2011 Middle Layer Super Output Areas (MSOA) to 2020 Covid Infection Survey Geography in the United Kingdom, as at 1 October 2020. (File size - 920KB) Field Names - MSOA11CD, MSOA11NM, MSOA11NMW, CIS20CD, FIDField Types - Text, Text, Text, Text, NumericField Lengths - 9, 255, 255, 9FID = 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/MSOA11_to_CIS20_Lookup_131d08fc16814c5d8113175e0c9c6de2/FeatureServer

🇬🇧 영국 English 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.

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

An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 170,000 people since the end of 2019, killed over 7,400, and caused worldwide social and economic disruption. SARS-CoV-2 infection has a mortality rate of 3.4% among confirmed cases, and there are currently no effective antiviral molecules or vaccines for its treatment or prevention. The search for effective antiviral treatments has recently highlighted host-directed strategies, however besides data describing viral interactions with cell surface receptors and activating proteases, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To shed light on the mechanisms used by SARS-CoV-2 to infect human cells, we have utilized affinity-purification mass spectrometry to globally profile physical host protein interactions for 26 viral proteins encoded in the SARS-CoV-2 genome, identifying 332 high confidence interactions. Among the human proteins, we identify many druggable human proteins targeted by existing FDA approved drugs that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host-dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral targets against SARS-CoV-2 and other deadly coronavirus strains.