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.

As of January 1, 2025, Rome (Lazio) was the Italian province which registered the highest number of coronavirus (COVID-19) cases in the country. Milan (Lombardy) came second in this ranking, while Naples (Campania) and Turin (Piedmont) followed. These four areas are also the four most populated provinces in Italy. The region of Lombardy was the mostly hit by the spread of the virus, recording almost one sixth of all coronavirus cases in the country. The provinces of Milan and Brescia accounted for a large part of this figure. For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Activation time (UTC): 2020-04-05 22:46:00
Event time (UTC): 2020-04-06 08:00:00
Event type: Epidemic (Viral disease)

Activation reason:
Italy is currently facing a serious situation related to the Covid-19. The Head of the Civil Protection Department has been nominated as national emergency Coordinator and the entire National System has been activated to face the Emergency. From the first day of March, the entire Italian territory has been put on lock-down and further initiatives are being implemented to limit the spread of the disease. The Civil Protection needs to map all the temporary health facilities (such as triage facilities, field hospitals and so on) as well the gathering places in order to have a clear understanding of the current situation of the territory for the subsequent monitoring of activities and public spaces during the emergency.

Reference products: 8
Delineation products: 7
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Case data from 02-24-2020 to 08-16-2020, this data repository stores COVID-19 virus case data for Italy, including daily case data, summary data, and base map. Each zip file contains weekly case data from Monday to Sunday.

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 image reports a Maximum Entropy model that estimates suitable locations for COVID-19 spread, i.e. places that could favour the spread of the virus just in terms of environmental parameters.

The model was trained just on locations in Italy that have reported a rate of new infections higher than the geometric mean of all Italian infection rates. The following environmental parameters were used, which are correlated to those used by other studies:

• Average Annual Surface Air Temperature in 2018 (NASA)
• Average Annual Precipitation in 2018 (NASA)
• CO2 emission (natural+artificial) averaged between January 1979 and December 2013 (Copernicus Atmosphere Monitoring Service)
• Elevation (NOAA ETOPO2)
• Population per 0.5° cell (NASA Gridded Population of the World)

A higher resolution map, the model file (in ASC format) and all parameters used are also attached.

The model indicates highest correlation with infection rate for CO2 around 0.03 gCm^−2day^−1, for Temperature around 11.8 °C, and for Precipitation around 0.3 kg m^-2 s^-1, whereas Elevation and Population density are poorly correlated with infection rate.

One interesting result is that the model indicates, among others, the Hubei region in China as a high-probability location, and Iran (around Teheran) as a suited location for virus' spread, but the model was not trained on these regions, i.e. it did not know about the actual spread in these regions.

Evaluation:

A risk score was calculated for each country/region reported by the JHU monitoring system (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). This score is calculated as the summed normalised probability in the populated locations divided by their total surface. This score represents how much the zone would potentially foster the virus' spread.

We assessed the reliability of this score, by selecting the country/regions that reported the highest rates of infection. These zones were selected as those with a rate higher than the upper confidence of a log-normal distribution of the rates.

The agreement between the two maps (covid_high_rate_vs_high_risk.png, where violet dots indicate high infection rates and countries' colours indicate estimated high risk score) is the following:

Accuracy (overall percentage of correctly predicted high-rate zones): 77.25%
Kappa (agreement between the two maps): 0.46 (Good, according to Fleiss' intepretation of the score)

This assessment demonstrates that our map can be used to estimate the risk of a certain country to have a high rate of infection, and indicates that the influence of environmental parameters on virus's spread should be further investigated.

This paper presents an empirical analysis that maps the short-term and middle-to-long-term responses of Italian universities to the COVID-19 crisis by focussing on teaching activities. A representative sample of eighteen public universities was analysed to evaluate how they planned their teaching activities for the 2020–21 and 2021/2022 academic years and whether and how the pandemic pushed them to change their strategic plans for the following year. Based on a specific survey, an in-depth analysis of documents, and interviews, the findings reveal very similar short-term responses but three different types of medium-to-long-term responses: rebounding, proactive, and innovative. This empirical evidence indicates that scholars in higher education should heed the ways in which universities react and adapt in turbulent times when they have to make decisions under conditions of high uncertainty and recognize when crises can also become opportunities for change.

Analysis of ‘COVID-19 in Turkey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gkhan496/covid19-in-turkey on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

COVID-19 data in Turkey. Daily Covid-19 data published by our health ministry.

Content

time_series_covid_19_confirmed_tr
time_series_covid_19_recovered_tr
time_series_covid_19_deaths_tr
time_series_covid_19_intubated_tr
time_series_covid_19_intensive_care_tr.csv 
time_series_covid_19_tested_tr.csv 
test_numbers : Number of test (daily)

Total data

covid_19_data_tr

Github

Github repo : https://github.com/gkhan496/Covid19-in-Turkey/

Acknowledgements

We would like to thank our health ministry and all health workers.

Country level datasets

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases France - https://www.kaggle.com/lperez/coronavirus-france-dataset Tunisia - https://www.kaggle.com/ghassen1302/coronavirus-tunisia Japan - https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2311214%2Feaf61a1cf97850b64aefd52d3de5890b%2FXMhaJ.png?generation=1586182028591623&alt=media" alt="">

Source : https://fastlifehacks.com/n95-vs-ffp/

https://covid19.saglik.gov.tr https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html?fbclid=IwAR0k49fzqTxI4HBBZF7n4hLX4Zj0Q2KII_WOEo7agklC20KODB3TOeF8RrU#/bda7594740fd40299423467b48e9ecf6 http://who.int/ --situation reports https://evrimagaci.org/covid19#turkey-statistics

--- Original source retains full ownership of the source dataset ---

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.

"Koronawirus w polsce" ("coronavirus in poland") was the most popular Google search in Poland related to the coronavirus (COVID-19) 2020. Internet users in Poland also searched for a map of the virus, its symptoms, and its development in Italy, Germany, and China.

For further information about the coronavirus (COVID-19) 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.