Context

This is the Italian Coronavirus data repository from the Dipartimento della Protezione Civile . This dataset was created in response to the Coronavirus public health emergency in Italy and includes COVID-19 cases reported by region

Sample Queries

Dati Italia COVID-19: Which provinces in Italy have the most confirmed cases? Find which Italian provinces have the highest number of confirmed COVID-19 cases as of yesterday. SELECT covid19.province_name AS province, covid19.region_name AS region, confirmed_cases FROM bigquery-public-data.covid19_italy.data_by_province covid19 WHERE EXTRACT(date from DATE) = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) ORDER BY confirmed_cases desc

Sample Query 2

What percentage of tests performed have resulted in confirmed cases by region? This query determines what percent of tests performed are made up by confirmed cases. SELECT covid19.region_name AS region, total_confirmed_cases, tests_performed, ROUND(total_confirmed_cases/tests_performed*100,2) AS percent_tests_confirmed_cases FROM bigquery-public-data.covid19_italy.data_by_region covid19 WHERE EXTRACT(date from DATE) = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) ORDER BY percent_tests_confirmed_cases desc

Italy went through five coronavirus waves during the pandemic. As of January 8, 2025, the number of active coronavirus cases in the country was equal to approximately 203,305. On January 23, 2022, there were 2,734,906 active infections in Italy, the highest figure since the start of the pandemic. Furthermore, the total number of cases (including active cases, recoveries, and deaths) in Italy reached 26.9 million, with the region mostly hit by the virus in the country being Lombardy. Despite this notably high number of infections, deaths and hospitalizations remain rather low, thanks to a very high vaccination rate. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Since the spread of the coronavirus (COVID-19) in Italy started in February 2020, the number of cases has increased daily. However, the vast majority of people who contracted the virus have recovered. As of January 8, 2025, the number of individuals who recovered from coronavirus in Italy reached over 26.5 million. Conversely, the number of deaths also kept increasing, reaching over 198.6 thousand. When looking at the regional level, the region with the highest number of recoveries was Lombardy. The region, however, registered the highest number of coronavirus cases in the country. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

The data are related to Tuscany and its provinces. They covered the period from 24/2/2020 to 15/6/2022 and they were updated daily.

Two tables were created: one with data from the entire Tuscany and the other with data from each province within Tuscany (AR, FI, GR, LI, LU, MS, PI, PO, PT, SI) and each medical district of the region (aslCENTRO,aslNO,aslSE).

Inspiration

You can perform an exploratory data analysis of the data, working with Pandas or Numpy.

Interesting visualizations can be performed too using, for instance, Python libraries to plot the data of the number of deaths, dismissed patients, total and current positives, recoveries etc.

It might be useful to plot the data in time, working with different date formats too and conducting a time series analysis.

Moreover, this dataset is very good to practice queries using SQL or Pandas.

Remember to upvote if you found the dataset useful :).

Collection methodology

The data were fetched from the following link: https://dati.toscana.it/dataset/open-data-covid19.

The rows from provinces were separated from the rows related to Tuscany region and some columns were removed from the catalogue since they didn't contain any data. Furthermore, some columns were transformed from floats to integers, missing values were filled with the integer '0' and the headers were translated to English.

In past 24 hours, Italy, Europe had N/A new cases, N/A deaths and N/A recoveries.

Daily information on the spread of the COVID-19 epidemic in Italy (over time and by location) and on the characteristics of the reported cases. They are provided in the form of charts, maps and tables, or in the form of bulletins providing a more in-depth analysis of the gathered information.

Context

This data was gathered as part of the data mining project for the General Assembly Data Science course. using the API from https://rapidapi.com/astsiatsko/api/coronavirus-monitor .

Covid-19

The Covid-19 is a contagious coronavirus that hailed from Wuhan, China. This new strain of the virus has strike fear in many countries as cities are quarantined and hospitals are overcrowded. This dataset will help us understand how Covid-19 in Italy.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak.

### High Light: - Spread to various overtime in Italy - Try to predict the spread of COVID-19 ahead of time to take preventive measures

Content

• id: id number
• total_cases: the total number of cases have the coronavirus
• new_cases: the number of new cases with coronavirus in this day and time
• active_cases: Number of active cases with coronavirus
• total_deaths: the total deaths numbers by a coronavirus
• new_deaths: the number of new deaths in this day and time
• total_recovered: the number of recovered from the coronavirus
• serious_critical: numbe of the people have the coronavirus in serious critical
• total_cases_per1m: the number of confirmed cases per 1 million people than China
• record_date: Date of notification - YYYY-MM-DD HH:MM:SS

Inspiration

https://www.livescience.com/why-italy-coronavirus-deaths-so-high.html

The number of COVID-19 vaccination doses administered in Italy rose to 143854436 as of Oct 27 2023. This dataset includes a chart with historical data for Italy Coronavirus Vaccination Total.

The dataset used in this paper is the COVID-19 data from Italy, downloaded from the Italian Ministry for Health free repository.

A live version of the data record, which will be kept up-to-date with new estimates, can be downloaded from the Humanitarian Data Exchange: https://data.humdata.org/dataset/covid-19-mobility-italy.

If you find the data helpful or you use the data for your research, please cite our work:

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

The data record is structured into 4 comma-separated value (CSV) files, as follows:

id_provinces_IT.csv. Table of the administrative codes of the 107 Italian provinces. The fields of the table are:

COD_PROV is an integer field that is used to identify a province in all other data records;

SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard (https://en.wikipedia.org/wiki/ISO_3166-2:IT);

DEN_PCM is the full name of the province.

OD_Matrix_daily_flows_norm_full_2020_01_18_2020_04_17.csv. The file contains the daily fraction of users’ moving between Italian provinces. Each line corresponds to an entry of matrix (i, j). The fields of the table are:

p1: COD_PROV of origin,

p2: COD_PROV of destination,

day: in the format yyyy-mm-dd.

median_q1_q3_rog_2020_01_18_2020_04_17.csv. The file contains median and interquartile range (IQR) of users’ radius of gyration in a province by week. Each entry of the table fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

week: median value of the radius of gyration on week week, with week in the format dd/mm-DD/MM where dd/mm and DD/MM are the first and the last day of the week, respectively.

week Q1 first quartile (Q1) of the distribution of the radius of gyration on week week,

week Q3 third quartile (Q3) of the distribution of the radius of gyration on week week,

average_network_degree_2020_01_18_2020_04_17.csv. The file contains daily time-series of the average degree 〈k〉 of the proximity network. Each entry of the table is a value of 〈k〉 on a given day. The fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

day in the format yyyy-mm-dd.

ESRI shapefiles of the Italian provinces updated to the most recent definition are available from the website of the Italian National Office of Statistics (ISTAT): https://www.istat.it/it/archivio/222527.

Italy recorded 190080 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, Italy reported 25828252 Coronavirus Cases. This dataset includes a chart with historical data for Italy Coronavirus Deaths.

The first two cases of the new coronavirus (COVID-19) in Italy were recorded between the end of January and the beginning of February 2020. Since then, the total number of cases in Italy increased steadily and the country faced five harsh waves of contagion. The total number of cases reached 26,964,654 as of January 8, 2025. The region mostly hit by the virus in the country was Lombardy, counting almost than 4.4 million cases. The virus originated in Wuhan, a Chinese city populated by millions and located in the province of Hubei. More statistics and facts about the virus in Italy are available here.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Abstract

As part of the efforts of the World Bank Group to understand the impact of COVID-19 on the private sector, the Enterprise Analysis unit is conducting follow-up surveys on recently completed Enterprise Surveys (ES) in several countries. These short surveys follow the baseline ES and are designed to provide quick information on the impact and adjustments that COVID-19 has brought about in the private sector.

Geographic coverage

Italy

Analysis unit

Firms

Kind of data

Sample survey data [ssd]

Sampling procedure

The follow-up surveys re-contact all establishments sampled in the standard ES using stratified random sampling. The total sample target was 760. Sample Frame Source : Completed interviews in the Italy 2019 ES. For more information on sampling methodology, see https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note.pdf

Mode of data collection

Computer Assisted Telephone Interviews (CATI) and Computer Assisted Web Interviews (CAWI), with phone follow-up

Research instrument

The questionnaire contains the following modules: - Control information and introduction - Sales - Production - Labor - Finance - Policies - Expectations - Information on permanently closed establishments - Interview protocol

Response rate

65.9%

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.

Records of reported Counts of COVID-19 case counts in Italy from 2019-2021. Download is a zipped CSV file with readme.

Introduction

This dataset contains #COVID19 data about Italy: • National trend • JSON data • Provinces data • Regions data • Summary cards • Areas

Structure

COVID-19/
│
├── andamento-nazionale/
│  ├── dpc-covid19-ita-andamento-nazionale-yyyymmdd.csv
├── aree/
│  ├── geojson
│  │  ├── dpc-covid19-ita-aree.geojson
│  ├── shp
│  │  ├── dpc-covid19-ita-aree.shp
├── dati-province/
│  ├── dpc-covid19-ita-province-yyyymmdd.csv
├── dati-json/
│  ├── dpc-covid19-ita-*.json
├── dati-regioni/
│  ├── dpc-covid19-ita-regioni-yyyymmdd.csv
├── schede-riepilogative/
│  ├── province
│  │  ├── dpc-covid19-ita-scheda-province-yyyymmdd.pdf
│  ├── regioni
│  │  ├── dpc-covid19-ita-scheda-regioni-yyyymmdd.pdf

Reference

Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile repo

Context

Coronaviruses are a large family of viruses which may cause illness in animals or humans. In humans, several coronaviruses are known to cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). The most recently discovered coronavirus causes coronavirus disease COVID-19 - WHO

People can catch COVID-19 from others who have the virus. This has been spreading rapidly around the world and Italy is one of the most affected country.

On March 8, 2020 - Italy’s prime minister announced a sweeping coronavirus quarantine early Sunday, restricting the movements of about a quarter of the country’s population in a bid to limit contagions at the epicenter of Europe’s outbreak. - TIME

Content

This dataset is from https://github.com/pcm-dpc/COVID-19 collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale

This dataset has two files

• covid19_italy_province.csv - Province level data of COVID-19 cases
• covid_italy_region.csv - Region level data of COVID-19 cases

Acknowledgements

Data is collected by Sito del Dipartimento della Protezione Civile - Emergenza Coronavirus: la risposta nazionale and is uploaded into this github repo.

Dashboard on the data can be seen here. Picture courtesy is from the dashboard.

Inspiration

Insights on * Spread to various regions over time * Try to predict the spread of COVID-19 ahead of time to take preventive measures

COVID-19 data for Italy from 2020-02-24 to 2023-11-01, including tot_cases, tot_deaths

Files:

• ".pkl" Cache file backup
• Dataframe exported "PyCoa-DF.csv"
• Original database backup

This repository includes a Linked Data representation of the covid19-ita dataset provided by the Department of Civil Protection in Italy, following the RDF Data Cube Vocabulary and the KPIOnto ontology. The dataset includes measurements of various indicators related to COVID19 spread at the province, regional, and country levels, on a daily basis from February 24th to November 20th, 2020. The RDF format allows describing statistical multidimensional data as Linked Data on the Web. As such, each data point is represented as an observation of the relevant measures along two dimensions, namely time and geographical area.

The project is also available on Github.

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

2019 Novel Coronavirus (2019-nCoV) is a virus (more specifically, a coronavirus) identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China. Early on, many of the patients in the outbreak in Wuhan, China reportedly had some link to a large seafood and animal market, suggesting animal-to-person spread. However, a growing number of patients reportedly have not had exposure to animal markets, indicating person-to-person spread is occurring. At this time, it’s unclear how easily or sustainably this virus is spreading between people - CDC

This dataset has daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

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

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

World Health Organization (WHO): https://www.who.int/

DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.

BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml

China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm

Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html

Macau Government: https://www.ssm.gov.mo/portal/

Taiwan CDC: https://sites.google....