The novel coronavirus (COVID-19) crisis has affected numerous industries around the world, but it has also had an impact on air pollution. The first day of the United Kingdom lockdown saw noticeable drops in nitrogen dioxide (NO2) emissions in cities across the country. The most dramatic decline was observed in Edinburgh. On March 26, 2020 the average daily NO2 emissions amounted to 28µg/m3, compared with 74µg/m3 recorded on the same day the previous year. This was followed by London Westminster, where emissions fell from 58µg/m3 to 30µg/m3. Not all cities saw as noticeable a decline, with daily emissions on this day in Manchester Piccadilly dropping by just 7µg/m3 from the previous year. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

One of the expected impacts of the coronavirus (COVID-19) that brought the world to a halt in the first quarter of 2020 is the disruption to normal business activities and supply chains. The effect spreads through various industries and with the assumption of a ********* delay in construction activities, the forecast suggests property completions planned for 2020 in cities in the United Kingdom (UK) could decrease by more than *********, leading up to more completions in 2021 than originally planned. For more information on the Statista coverage of the coronavirus in the UK, see our report.

Indicators from the Office for National Statistics (ONS) Opinions and Lifestyle Survey to understand the impacts of the coronavirus (COVID-19) pandemic on city regions in Great Britain, 2020 and 2021.

This briefing brings together a range of data published on the demographic impact of Covid19 to understand how the city has been affected, covering what is known about Covid-19 cases, before looking at mortality. It provides comparisons with other cities and explains some of the issues which affect the accuracy of such comparisons. And it summarises the emerging evidence of unequal impacts for different demographic groups, especially ethnicity and workers in particular occupations.

The coronavirus (COVID-19) outbreak has forced governments across the world to implement social distancing measures and lockdowns in order to reduce the number of new cases and deaths. Using data from their travel app, Citymapper were able to produce a Mobility Index to indicate the movements of certain European cities during the period from March 16-22, 2020. Countries hardest hit by the virus and where lockdowns are in places appeared to have the least amount of movement. In Milan, Italy, only **** percent of the city were moving and in Madrid, Spain, only **** percent according to the Index. However in other affected cities movement was still higher, such as in London where ** percent of the city were still moving in the week ending March 22; The next day, the UK govenrment implemented a lockdown with stricter regulations regarding when people can go out.

Food and drink venues, such as bars and restaurants, were allowed to resume trading in the United Kingdom (UK) in July 2020, following a nationwide lockdown due to the outbreak of coronavirus (COVID-19). As of August, the majority of establishments across Great Britain had resumed trading. In London, 71.2 percent of sites that were open pre-lockdown had re-opened to customers. This lagged behind other major cities, including Liverpool and Manchester.

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.

Roadside advertising impacts have been affected by the coronavirus outbreak in the United Kingdom (UK). However, in Edinburgh, Greater London, and Greater Manchester on the Thursday and Saturday before Easter, impacts increased compared to the week before. There was a decrease in roadside ad impacts on Good Friday and Easter Sunday. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context:

The fate of the world changed in 2020.

Daily activities were impacted, impeded, and wouldn't be the same forever.

In partnership with Microsoft and the University of Oxford, A Tale of Two Cities is a Data AI hackathon that aims to address trends during and after the pandemic.

I will present my work at this hackathon through my association with the University of Oxford as an AI Tutor for the Artificial Intelligence: Cloud and Edge Implementations course.

Acknowledgments:

I'd like to thank the original authors of these data sources!

Relevant data was extracted from these sources and split into two phases: - COVID era (before 1st February, 2022), and - Post COVID era (after 1st February, 2022)

This helped me to compare trends in New York and London over time. https://i.imgur.com/KFRaB51.png" alt="">

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.

Estimates of vaccine sentiment for city region geographies split by characteristics including age, employment, tenure, health and Index of Multiple Deprivation. Analysis based on the Opinions and Lifestyle Survey (OPN).

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39–4.13), indicating that 58–76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6–7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090–33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Consumer Price Index (CPI): National indices Components for COVID-19 testing. Monthly. National.

Values of parameters.

The growth of the online short-term rental market, facilitated by platforms such as Airbnb, has added to pressure on cities’ housing supply. Without detailed data on activity levels, it is difficult to design and evaluate appropriate policy interventions. Up until now, the data sources and methods used to derive activity measures have not provided the detail and rigour needed to robustly carry out these tasks. This paper demonstrates an approach based on daily scrapes of the calendars of Airbnb listings. We provide a systematic interpretation of types of calendar activity derived from these scrapes and define a set of indicators of listing activity levels. We exploit a unique period in short-term rental markets during the UK’s first COVID-19 lockdown to demonstrate the value of this approach.

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).

Multiple linear regression table with R2, coefficient and p value for input features (population density, normalized busy airport, pre-infected count, pre-death count) and observed factors (post-infected count and post-death count).

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. 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.

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. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.