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.

These maps are published by ECDC every Thursday in support of the Council Recommendation on a coordinated approach to the restriction of free movement in response to the COVID-19 pandemic, which was adopted by EU Member States on 13 October 2020 and amended on 28 January 2021 and 14 June 2021. The maps are based on data reported by EU Member States to The European Surveillance System (TESSy) database by 23:59 every Tuesday.

Areas are marked in the following colours (note that as of 17 June 2021, regions are classified according to the criteria in the latest amendment of the Council Recommendation):

Green: if the 14-day notification rate is less than 50 and the test positivity rate is less than 4%; or if the 14-day notification rate is less than 75 and the test positivity rate less than 1% Orange: if the 14-day notification rate is less than 50 and the test positivity rate is 4% or more; or the 14-day notification rate is 50 or more and less than 75 and the test positivity rate is 1% or more; or the 14-day notification rate is between 75 and 200 and the test positivity rate is less than 4% Red: if the 14-day cumulative COVID-19 case notification rate ranges from 75 to 200 and the test positivity rate of tests for COVID-19 infection is 4% or more, or if the 14-day cumulative COVID-19 case notification rate is more than 200 but less than 500 Dark red: if the 14-day cumulative COVID-19 case notification rate is 500 or more Grey: if there is insufficient information or if the testing rate is lower than 300 cases per 100 000.

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.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, the country had to face four new harsh waves of contagion. As of January 1, 2025, the total number of cases reported by the authorities reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto and the southern region of Campania followed in the list. When adjusting these figures for the population size of each region, however, the picture changed, with the region of Veneto being the area where the virus had the highest relative incidence. Coronavirus in Italy Italy has been among the countries most impacted by the coronavirus outbreak. Moreover, the number of deaths due to coronavirus recorded in Italy is significantly high, making it one of the countries with the highest fatality rates worldwide, especially in the first stages of the pandemic. In particular, a very high mortality rate was recorded among patients aged 80 years or older. Impact on the economy The lockdown imposed during the Spring 2020, and other measures taken in the following months to contain the pandemic, forced many businesses to shut their doors and caused industrial production to slow down significantly. As a result, consumption fell, with the sectors most severely hit being hospitality and tourism, air transport, and automotive. Several predictions about the evolution of the global economy were published at the beginning of the pandemic, based on different scenarios about the development of the pandemic. According to the official results, it appeared that the coronavirus outbreak had caused Italy’s GDP to shrink by approximately nine percent in 2020.

The aim of the scoping review is to map out evidence based research on the Covid-19 pandemic impact on the European cities. The review questions touch three broad areas of interest:

1. the aspects of urban life described and analysed in publications on the impact of the pandemic on cities
2. the aspects of urban life that are described in terms of crisis, breakdown, turnaround, etc. (crisis, disruption, slump, shift…) in such studies
3. theoretical and methodological approaches applied in such studies

The search was conducted in June 2022, with the final body of literature consisting of 3,994 publication references from EBSCOhost, APA Psyc, Scopus, Web of Science, Proquest, Wiley, Sage, JSTOR, Tailor&Francis, Oxford Journals databases (Fig. 1). The following English words were searched for in titles, abstracts and keywords in the databases: (pandemic OR ‘Covid-19’) AND (city OR cities OR urban*). We used the following criteria for articles to be included in the study: 1) peer and non-peer-reviewed empirical papers in journals published in English from January 2019 to June 2022; 2) included studies where the impact of COVID-19 pandemic on European city/cities was an explicit variable of interest; 3) contained analysis of empirical data on cities or urban life retrieved or collected within and explicitly addressing the COVID-19 pandemic; 4) addressed the social, cultural, economic, political and socio-geographical aspects of a city. We excluded from our sample papers that were: 1) theoretical and opinion literature, media press releases, reports, MA dissertations and PhD theses; 2) secondary research papers (reviews, meta-analyses); 3) papers not in English; 4) studies about non-European cities; 5) studies which do not explicitly address the impact of the COVID-19 pandemic on cities; 6) studies addressing a city as a variable of secondary importance; 7) studies outside the scope of the COVID-19 pandemic, published before December 2019; 8) studies not addressing the social or human aspects of urban life.

The final database of coded documents consisted of 138 empirical articles presenting findings on the impact of the COVID-19 pandemic on European cities.

At the height of the coronavirus pandemic, on the last day of March 2020, Wikipedia in all languages broke a record for most traffic in a single day. Since the breakout of the Covid-19 pandemic at the start of January, tens if not hundreds of millions of people have come to Wikipedia to read - and in some cases also contribute - knowledge, information and data about the virus to an ever-growing pool of articles. Our study focuses on the scientific backbone behind the content people across the world read: which sources informed Wikipedia’s coronavirus content, and how was the scientific research on this field represented on Wikipedia. Using citation as readout we try to map how COVID-19 related research was used in Wikipedia and analyse what happened to it before and during the pandemic. Understanding how scientific and medical information was integrated into Wikipedia, and what were the different sources that informed the Covid-19 content, is key to understanding the digital knowledge echosphere during the pandemic. To delimitate the corpus of Wikipedia articles containing Digital Object Identifier (DOI), we applied two different strategies. First we scraped every Wikipedia pages form the COVID-19 Wikipedia project (about 3000 pages) and we filtered them to keep only page containing DOI citations. For our second strategy, we made a search with EuroPMC on Covid-19, SARS-CoV2, SARS-nCoV19 (30’000 sci papers, reviews and preprints) and a selection on scientific papers form 2019 onwards that we compared to the Wikipedia extracted citations from the english Wikipedia dump of May 2020 (2’000’000 DOIs). This search led to 231 Wikipedia articles containing at least one citation of the EuroPMC search or part of the wikipedia COVID-19 project pages containing DOIs. Next, from our 231 Wikipedia articles corpus we extracted DOIs, PMIDs, ISBNs, websites and URLs using a set of regular expressions. Subsequently, we computed several statistics for each wikipedia article and we retrive Atmetics, CrossRef and EuroPMC infromations for each DOI. Finally, our method allowed to produce tables of citations annotated and extracted infromations in each wikipadia articles such as books, websites, newspapers.Files used as input and extracted information on Wikipedia's COVID-19 sources are presented in this archive.See the WikiCitationHistoRy Github repository for the R codes, and other bash/python scripts utilities related to this project.

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.

Parallel to the dataset CORD-19 of scholarly articles, we provide the literature graph LG-covid19-HOTP composed of not only articles (graph nodes) that are relevant to the study of coronavirus, but also in and out citation links (directed graph edges) to base navigation and search among the articles. The article records are related and connected, not isolated. The graph has been updated weekly since March 26, 2020. The current graph includes 42,279 hot-off-the-press (HOTP) articles since January 2020. It contains 485,097 articles and 4,259,944 links. The link-to-node ratio is remarkably higher than some other existing literature graphs. In addition to the dataset we provide more functionalities at lg-covid-19-hotp.cs.duke.edu such as new articles, weekly meta-data analysis in terms of publication growth over time, ranking by citation, and statistical near-neighbor embedding maps by similarity in co-citation, and similarity in co-reference. Since April 11, we have enabled a novel functionality - self-navigated surf-search over the maps. At the site we also take courtesy input of COVID-19 articles that are missing from the current collection.

The economy of the European Union is set to grow by *** percent in 2026, according to forecasts by the European Commission. This marks a significant slowdown compared to previous years, when the EU member states grew quickly in the aftermath of the COVID pandemic. ***** is the country which is forecasted to grow the most in 2026, with an annual growth rate of **** percent. Many of Europe's largest economies, on the other hand, are set to experiencing slow growth or stagnation, with Germany, France, and Italy growing below *** percent.

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

Number of coronavirus publications in 2020.

Snapshot img

The automotive HD maps market has the potential to grow by USD 954.88 million during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 16.86%. This automotive HD maps market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. The market report also offers information on several market vendors, including Alphabet Inc., CARMERA, Civil Maps, DeepMap Inc., HERE Global BV, Intel Corp., Maxar Technologies Inc., NavInfo Co. Ltd., The Sanborn Map Co. Inc., and TomTom International BV among others. Furthermore, this report extensively covers market segmentation by application (passenger cars and commercial vehicles) and geography (North America, Europe, APAC, MEA, and South America).

What will the Automotive HD Maps Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Automotive HD Maps Market

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Automotive HD Maps Market: Key Drivers and Trends

Based on our research output, there has been a negative impact on the market growth during and post-COVID-19 era. The rising adoption of cloud-based HD maps is notably driving the market growth, although factors such as high cost associated with semi-autonomous and fully autonomous technologies may impede the market growth. To unlock information on the key market drivers and the COVID-19 pandemic impact on the market.

This market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth.

Who are the Major Automotive HD Maps Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alphabet Inc.
CARMERA
Civil Maps
DeepMap Inc.
HERE Global BV
Intel Corp.
Maxar Technologies Inc.
NavInfo Co. Ltd.
The Sanborn Map Co. Inc.
TomTom International BV

The automotive HD maps market is fragmented and the vendors are deploying various organic and inorganic growth strategies to compete in the market.

To make the most of the opportunities and recover from the post-COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Which are the Key Regions for Automotive HD Maps Market?

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41% of the market’s growth will originate from North America during the forecast period. The US and Canada are the key markets for automotive HD maps in North America.

The report offers an up-to-date analysis of the geographical composition of the market. North America has been recording a significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. The accuracy and precision offered by HD maps will facilitate market growth in North America over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors.

What are the Revenue-generating Application Segments in the Automotive HD Maps Market?

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The market share growth by the passenger cars segment has been significant. This report provides insights on the impact of the unprecedented outbreak of COVID-19 on market segments. Through these insights, you can safely deduce transformation patterns in consumer behaviour, which is crucial to gauge segment-wise revenue growth during 2021-2025 and embrace technologies to improve business efficiency.

This report provides an accurate prediction of the contribution of all the segments to the growth of the market size.

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What are the Key Factors Covered in this Automotive HD Maps Market Report?

CAGR of the market during the forecast period 2021-2025
Detailed information on factors that will drive market growth during the next five years
Precise estimation of the market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behaviour
The growth of the market across North America, Europe, APAC, MEA, and South America 
A thorough analysis of the market’s competitive landscape and detailed information on vendors
Comprehensive details of factors that will challenge the growth of market vendors

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    Automotive HD Maps Market Scope




    Report Coverage


    Details




    Page number


    120




    Base year


    2020




    Forecast period


    2021-2025




    Growth momentum & CAGR


    Accelerate at a CAGR of 17%

Network metrics in coronavirus research.

Collaboration rate in coronavirus research.

Negative binomial regression analysis of the relationship between team structure and citation impact of coronavirus publications in 2020.