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

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.

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.

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.

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.

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.

A lookup file between 2020 Covid Infection Survey Geography to 2019 Regions in England, as at 1 October 2020. (File size - 16KB) Field Names - CIS20CD, RGN19CD, FIDField Types - Text, Text, NumericField Lengths - 9, 9,FID = 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/Covid_Infection_Survey_October_2020_to_Region_Lookup_in_England/FeatureServer

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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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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Ši kartografavimo priemonė leidžia jums pamatyti, kaip COVID-19 mirtys jūsų vietovėje gali būti susijusios su vietos gyventojų veiksniais, kurie, remiantis tyrimais, yra susiję su mirtingumu nuo COVID-19. Jame pateikiami nedidelių Londono vietovių (vadinamų MSOA) COVID-19 mirčių rodikliai ir suteikiama galimybė palyginti juos su daugeliu kitų veiksnių, įskaitant daugialypio nepritekliaus indeksą, vietos gyventojų amžių ir etninę kilmę, anksčiau buvusių vietos gyventojų sveikatos sąlygų mastą ir profesinius duomenis. Tyrimai parodė, kad mirtingumo nuo COVID-19 rizika yra didesnė vyresnio amžiaus žmonėms, vyrams, asmenims, turintiems sveikatos sutrikimų, ir žmonėms iš BAME šeimų. Remiantis 2020 m. balandžio 17 d. duomenimis, remiantis Nacionalinės statistikos biuro (ONS) duomenimis, Londono rajonai buvo vieni didžiausių mirtingumo nuo COVID-19 rodiklių.ONS analizė taip pat parodė, kaip mirtingumas taip pat susijęs su socialiniais ir ekonominiais klausimais, pvz., „rizikos“ priskiriamomis profesijomis ir nepritekliumi vietovėje. Daug kalbama apie su COVID-19 susijusį mirtingumą, kuris vis dar nėra visiškai suprantamas, įskaitant įvairių veiksnių, pvz., BAME grupių ir profesinės veiklos ryšio, sankirtą. Viena vertus, nė vienas iš šių atskirų veiksnių nėra glaudžiai susijęs su šių mažų vietovių mirtimi.Tai yra labiausiai tikėtina, nes svarbiausi veiksniai įvairiose srityse skirsis. Kai kuriais atvejais tai gali būti susiję su gyventojų amžiumi, kitais atvejais tai gali būti susiję su pagrindinių sveikatos sutrikimų paplitimu, vietovės nepritekliumi arba gyventojų, dirbančių rizikingomis profesijomis, dalimi, o kai kuriais atvejais – su šių ligų arba nė vienos iš jų deriniu. Išsamesnę aprašomąją šios priemonės veiksnių analizę galima rasti čia:

https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

Din l-għodda ta’ mmappjar tippermettilek tara kif l-imwiet tal-COVID-19 fiż-żona tiegħek jistgħu jkunu relatati ma’ fatturi fil-popolazzjoni lokali, li r-riċerka wriet li huma assoċjati mal-mortalità mill-COVID-19. Huwa jimmappja r-rati ta’ mwiet tal-COVID-19 għal żoni żgħar ta’ Londra (magħrufa bħala MSOAs) u jippermettilek tqabbelhom ma’ għadd ta’ fatturi oħra inkluż l-Indiċi tad-Deprivazzjoni Multipla, l-età u l-etniċità tal-popolazzjoni lokali, il-firxa tal-kundizzjonijiet tas-saħħa diġà eżistenti fil-popolazzjoni lokali, u d-data dwar ix-xogħol. Ir-riċerka wriet li r-riskju ta’ mortalità mill-COVID-19 huwa ogħla għal persuni ta’ età akbar, għall-irġiel, għal persuni b’kundizzjonijiet tas-saħħa preeżistenti, u għal persuni minn sfond ta’ BAME. Londra kellha wħud mill-ogħla rati ta’ mortalità mill-COVID-19 abbażi tad-data sas-17 ta’ April 2020, abbażi ta’ data mill-Uffiċċju għall-Istatistika Nazzjonali (ONS).L-analiżi mill-ONS uriet ukoll kif il-mortalità hija relatata wkoll ma’ kwistjonijiet soċjoekonomiċi bħall-okkupazzjonijiet ikklassifikati bħala “f’riskju” u l-privazzjoni taż-żona. Hemm ħafna dwar il-mortalità relatata mal-COVID-19 li għadha mhix mifhuma bis-sħiħ, inkluża l-intersezzjoni bejn il-fatturi differenti eż. ir-relazzjoni bejn il-gruppi tal-BAME u l-okkupazzjoni. Waħedhom, l-ebda wieħed minn dawn il-fatturi individwali ma jikkorrelataw b’mod qawwi mal-imwiet għal dawn iż-żoni żgħar.Dan huwa l-aktar probabbli minħabba li l-fatturi l-aktar rilevanti se jvarjaw minn żona għal oħra. F’xi każijiet din tista’ tkun relatata mal-età tal-popolazzjoni, f’oħrajn tista’ tkun relatata mal-prevalenza tal-kundizzjonijiet tas-saħħa sottostanti, il-privazzjoni taż-żona jew il-proporzjon tal-popolazzjoni li taħdem f’“okkupazzjonijiet f’riskju”, u f’xi każijiet kombinazzjoni ta’ dawn jew l-ebda waħda minnhom. Aktar analiżi deskrittiva tal-fatturi f’din l-għodda tista’ tinstab hawn:

https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

Tento mapovací nástroj vám umožňuje zistiť, ako môžu úmrtia na COVID-19 vo vašej oblasti súvisieť s faktormi v miestnom obyvateľstve, o ktorých sa zistilo, že sú spojené s úmrtnosťou na COVID-19. Mapuje mieru úmrtí na COVID-19 v malých oblastiach Londýna (známych ako MSOA) a umožňuje ich porovnať s viacerými faktormi vrátane indexu viacnásobnej deprivácie, veku a etnickej príslušnosti miestneho obyvateľstva, rozsahu už existujúcich zdravotných podmienok v miestnom obyvateľstve a údajov o zamestnaní.

Z výskumu vyplynulo, že riziko úmrtnosti spôsobené ochorením COVID-19 je vyššie v prípade ľudí starších vekových skupín, mužov, ľudí s už existujúcimi zdravotnými problémami a osôb z prostredia BAME. Londýnske štvrte mali na základe údajov do 17. apríla 2020 jednu z najvyšších mier úmrtnosti spôsobenej ochorením COVID-19 na základe údajov z Úradu pre národnú štatistiku (ONS). Analýza z ONS tiež ukázala, ako úmrtnosť súvisí aj so sociálno-ekonomickými otázkami, ako sú povolania klasifikované ako „rizikové“ a oblastná deprivácia. Pokiaľ ide o úmrtnosť súvisiacu s ochorením COVID-19, stále nie je úplne pochopená, a to vrátane priesečníka medzi rôznymi faktormi, napr. vzťahom medzi skupinami BAME a povolaním.

Na vlastnú päsť, žiadny z týchto individuálnych faktorov úzko koreluje s úmrtiami pre tieto malé oblasti. Je to s najväčšou pravdepodobnosťou preto, že najdôležitejšie faktory sa budú v jednotlivých oblastiach líšiť. V niektorých prípadoch sa môže týkať veku obyvateľstva, v iných sa môže týkať prevalencie základných zdravotných podmienok, deprivácie oblastí alebo podielu obyvateľstva pracujúceho v „rizikových povolaniach“ a v niektorých prípadoch ich kombinácie alebo žiadneho z nich.

Ďalšiu opisnú analýzu faktorov v tomto nástroji nájdete tu:

https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

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

See kaardistamisvahend võimaldab teil näha, kuidas COVID-19 surmajuhtumid teie piirkonnas võivad olla seotud kohaliku elanikkonna teguritega, mida uuringud on näidanud, et need on seotud COVID-19 suremusega. Selles kaardistatakse Londoni väikeste piirkondade (nn MSOAd) COVID-19 surmajuhtumite määrad ja võimaldatakse võrrelda neid mitmete muude teguritega, sealhulgas mitmekordse puuduse indeks, kohaliku elanikkonna vanus ja etniline päritolu, kohaliku elanikkonna varasemate terviseseisundite ulatus ja tööalased andmed.

Uuringud on näidanud, et COVID-19 põhjustatud suremusrisk on suurem vanematesse vanuserühmadesse kuuluvate inimeste, meeste, olemasolevate terviseprobleemidega inimeste ja BAME-taustaga inimeste puhul. Londoni linnaosades oli riikliku statistikaameti (ONS) andmete põhjal üks kõrgemaid COVID-19 suremuse määrasid, tuginedes andmetele kuni 17. aprillini 2020. ONSi analüüs on näidanud ka seda, kuidas suremus on seotud ka selliste sotsiaalmajanduslike küsimustega nagu „ohustatud kutsealad“ ja piirkondade puudus. COVID-19ga seotud suremuse kohta on palju, mida ei ole veel täielikult aru saadud, sealhulgas eri tegurite, nt BAME rühmade ja ameti vaheliste seoste ristumiskoht.

Iseenesest ei ole ükski neist üksikutest teguritest nende väikeste piirkondade surmadega tugevalt seotud. See on kõige tõenäolisem, sest kõige olulisemad tegurid on piirkonniti erinevad. Mõnel juhul võib see olla seotud elanikkonna vanusega, mõnel juhul võib see olla seotud aluseks olevate terviseprobleemide levimusega, piirkondade puudusega või ohustatud kutsealadel töötava elanikkonna osakaaluga ning mõnel juhul nende kombinatsiooniga või mitte ühegiga neist.

Selle vahendi tegurite täiendav kirjeldav analüüs on kättesaadav siin:

https://data.london.gov.uk/dataset/covid-19--socio-economic-risk-factors-briefing

A lookup file between 2011 Output Areas to 2020 Covid Infection Survey Geography in the United Kingdom, as at 1 October 2020. (File size - 16MB) Field Names - OA11CD, CIS20CD, FIDField Types - Text, Text, NumericField Lengths - 9, 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/Output_Area_to_Covid_Infection_Survey_October_2020_Lookup_for_the_United_Kingdom/FeatureServer