Context

COVID-19 has infected many people in France.

Content

The dataset is no longer updated. It contains almost all French metropolitan regions plus overseas regions, updated on March 09 2020. If you want to help updating this dataset, see contributions section below.

This dataset intention is to put all published information about COVID-19 patients in France in a csv file.

Acknowledgements

Source of data: Press releases of the French regional health agencies. Data transcripted in a csv by a GitHub community.

This work is inspired by a similar work made in South Korea: kaggle dataset.

Contributions

We need more contributors to build this dataset and keep it updated. Join us on GitHub.

Contributors: Lior Perez, Samia Drappeau, Manon Fourniol, Zoragna, Raphaël Presberg

France recorded 38989402 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, France reported 163279 Coronavirus Deaths. This dataset includes a chart with historical data for France Coronavirus Cases.

The number of COVID-19 vaccination doses administered in France rose to 154451978 as of Oct 27 2023. This dataset includes a chart with historical data for France Coronavirus Vaccination Total.

Context

This are data useful to track and possibly predict the spread in France of 2019-nCoV, a highly contagious coronavirus that originated from Wuhan (Hubei province), Mainland China. France is a neighboring country of the second mainly infected at the time this database has been created, Italy

Content

Main dataset (France) is sourced from the governamental site:

https://www.data.gouv.fr/en/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/ or the daily updated repository https://github.com/opencovid19-fr

Data for China have been obtained from:

https://data.gov.hk/en-data/dataset/hk-dh-chpsebcddr-novel-infectious-agent

Those for Italy and Piedmont region are from the national website

http://www.protezionecivile.gov.it

Acknowledgements

Thanks to the French government that openly shares these important data to us.

This statistic shows the number of confirmed COVID-19 (coronavirus) cases in France as of March 25, 2020, split down by region. On that day (2:00 pm), there were a total of 25,233 cases registered in all of France. With 7,660 cases, the Paris region (Ile-de-France) was the region most touched by the outbreak. The overseas regions registered a total of 313 cases.

SARS-CoV-2 coronavirus

Coronaviruses are a large family of viruses that cause illnesses ranging from a common cold to more severe conditions. But the virus in question here is a new coronavirus which causes a lung disease which has been named COVID-19 (Corona virus disease 2019). It made its first appearance back in December 2019 in the Chinese city of Wuhan. According to Chinese authorities, people infected with the virus could have contracted it by consuming products of animal origin from a large city market, the Huanan Seafood Wholesale Market. On March 11, the World Health Organization classified COVID-19 as a pandemic.

The situation in France

Although the number of confirmed cases is far less important than it is in China, France nevertheless features among the countries most affected by the outbreak. During a survey conducted in mid-March, 35 percent of French people stated being very worried about the spread of this new virus and 49 percent were rather worried. As of March 26, 1,333 people out of 25,600 infected had died from the virus and 3,907 had been listed as cured.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

View daily updates and historical trends for France Coronavirus Full Vaccination Rate. Source: Our World in Data. Track economic data with YCharts analyti…

France recorded 383768 Coronavirus Recovered since the epidemic began, according to the World Health Organization (WHO). In addition, France reported 127869 Coronavirus Deaths. This dataset includes a chart with historical data for France Coronavirus Recovered.

View daily updates and historical trends for France Coronavirus Deaths Per Day. Source: Johns Hopkins Center for Systems Science and Engineering. Track ec…

WHO: COVID-2019: Number of Patients: Death: New: France data was reported at 0.000 Person in 24 Dec 2023. This stayed constant from the previous number of 0.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: New: France data is updated daily, averaging 0.000 Person from Jan 2020 (Median) to 24 Dec 2023, with 1431 observations. The data reached an all-time high of 5,602.000 Person in 22 Nov 2020 and a record low of 0.000 Person in 24 Dec 2023. WHO: COVID-2019: Number of Patients: Death: New: France data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued). Prior to 03 Feb 2020, data were generated. In 05 March 2020 report, the figures were reduced from prior situation reports due to separation of territories. Negative data reflects the number of retrospective adjustments made by national authorities due to reconciliation exercises, and consequently deducted to the corresponding “To-Date” series.

Work in progress

This dataset comes from https://github.com/scrouzet/covid19-incrementality If you want to get a fresh data update, please go to this repo.

covid19-incrementalite

Study on incrementality of COVID-19 effect. Objective : quantify death increase due to COVID-19 in France at a department level.

References

• https://medium.com/@davidbessis/coronavirus-the-core-metrics-we-should-be-looking-at-2ca09a3dc4b1

Data sources

Death data form INSEE (French Statistic Agency) : https://www.data.gouv.fr/fr/datasets/fichier-des-personnes-decedees/

Geography referential (commune and departement) : https://geo.api.gouv.fr

Population data time serie from INSEE : https://www.insee.fr/fr/statistiques/1893198

Methodology

Data preparation :

• Collection and concatenation of yearly death data (1990=> 2019)
• Deleted " replaced with white space
• retreated dates : removed lines with invalid birth or death dates (0,72% of cases)
• fitler death dates> 1970
• join with geography referential to assiciate commune with department
• correction of department for specific geographies (Lyon, Paris, Marseille with arrondissement code instead of comune code in INSEE File)
• Grouping by death department, death date, sex, age, year of observation

Colonnes du fichier : data/INSEE_deces_2010_2019.zip

• year of observation (annee_comptabilisation) = year of the yearly file where the death has been accounted for. (2019 = deces_2019.txt). Please note that a yearly file contains death dates from previous yeras due to delay in data collection at insee. All files must be concatenated to get a complete view of death for a given year. Duplicate records are already removed.
• sexe : 1 = Male / 2 = Female
• age : age at death time
• departement_deces : department code of the commune where death happend. Note that some department are not valid in the repository : 1% death in "99" departement, non significative death cases in "98" department. The department name and associated region is provided in the geography referential
• date_deces : death_date
• nb_deces : count of deceased person, grouped by (anneee_comptabilisation, sex, age, departement_code, date_deces)

Modélisation

WORK IN PROGRESS - Modélisation par classe d'age et par département - Retraitement de la canicule 2003 - redressement des données hebdo de l'INSEE pour estimer l'effet de décallage dans la remontée des information (délai entre survenance du délai et comptabilisation par l'INSEE)

Preprocessing

The pickle files in 'preprocessed' directory have been generated with data from the 'data' directory. See original repo for preprocessing method.

How to contribute

1. Fork this repo
2. Commit your code in your forked repo
3. Do a pull-request on the main repo and ask for code reviewers
4. Take into account the comments

For contributors 1. Make a branch "feat-short_name_feature" 2. Commit your code in this branch 3. Do a pull-request on the main repo and ask for code reviewers 4. Take into account the comments

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in When more delivers less: Comparing the US and French COVID-19 crisis responses, PIIE Policy Brief 20-9. If you use the data, please cite as: Cohen-Setton, Jérémie, and Jean Pisani-Ferry. (2020). When more delivers less: Comparing the US and French COVID-19 crisis responses. PIIE Policy Brief 20-9. Peterson Institute for International Economics.

Ce tableau de bord fournit une mise à jour quotidienne de la progression du Coronavirus COVID-19 en France (Métropole et DOM).Il se base sur les données publiées quotidiennement par Santé Publique France.Il comptabilise et représente :Le nombre de cas confirmés au niveau nationalLe nombre de décès au niveau nationalLe nombre de cas confirmés par région (sous forme de graphique et de carte par symboles proportionnels)La part des cas confirmés par rapport à la population, par région (sous forme de carte par dégradé de couleurs)L'évolution dans le temps du nombre de cas confirmés (sous forme de graphique) Il est optimisé pour être affiché sur un navigateur web (ordinateur ou tablette).Il fournit également un lien vers le tableau de bord national en version optimisée pour mobile.Visiter le site Esri France

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

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

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

INFORMATION 25/01/2023

Following a seizure error, the number of deaths in week 03 in ESMS has been reduced. The necessary corrections have been made and explain the artificial decline in the number of total deaths that have occurred since the beginning of the epidemic.

⚠** 15/11/2022 Following the suspension of activity by some of the Private Medical Biology Laboratories since 14 November, the number of “New cases confirmed since the previous day” is underestimated as of Tuesday 15/11. Similarly, the incidence rate and the screening rate will be underestimated as of Thursday 17/11. The teams of Public Health France remain mobilised to monitor the epidemic, which is based on multi-source surveillance.

08/06/2022 Given the current favourable trend and the decline of the main indicators, as of 11 June 2022, COVID-19 indicators produced by Santé publique France will be updated on Géodes and data.gouv.fr every day with the exception of weekends and public holidays.

Description of the dataset

This dataset includes most of the summary indicators allowing the monitoring of the COVID-19 outbreak in France. An inventory of COVID-19 data on data.gouv.fr is available here.

These data are shown in particular on the tab overview of the epidemic monitoring dashboard available on government.fr. The latter presents data on the COVID-19 outbreak in France since 28 March 2020.

This tool whose source code is free was developed under the leadership of Etalab and with the collaboration of civil society. It provides a consolidated view of the available official data.

Data description

The data contained in the dataset are published daily.

Contextual data:

— ‘‘‘date’’’ = Date

— ‘‘‘DEP’’= Department

— ‘‘‘Reg’’= Region

— ‘‘‘lib_dep’’’= department wording

— ‘‘‘lib_reg’’’= denominated region

Data on hospital situation

— ‘‘‘Hosp’’= Number of patients currently hospitalised for COVID-19. — ‘‘‘incid_hosp’’= Number of new patients hospitalised in the last 24 hours.

— ‘‘‘REA’’= Number of patients currently undergoing resuscitation or intensive care. — ‘‘‘incid_rea’’= Number of new patients admitted to resuscitation in the last 24 hours.

— ‘‘‘RAD’’= Cumulative number of patients who have been hospitalised for COVID-19 and return home due to improved health status. — ‘‘‘incid_rad’’= New home returns in the last 24 hours.

Data on death from COVID-19

— ‘‘‘dchosp’’= Death in hospital — ‘‘‘incid_dchosp’’= New patients who died in the hospital in the last 24 hours.

— ‘‘‘esms_dc’’’= Death in ESMS

— ‘‘‘dc_tot’’’= Cumulus of deaths (cumulative of deaths recorded in hospital and EMS)

Test data

— ‘‘‘CONF’’= Number of confirmed cases — ‘‘‘conf_j1’’’= Number of new confirmed cases (J-1 results date) — ‘‘‘POS’’= Number of persons declared positive (J-3 withdrawal date) — ‘‘pos_7j’'’ = Number of persons declared positive over one week (D-3 sampling date)

— ‘‘‘esms_cas’’’ = Cases confirmed in ESMS

Data on COVID-19 monitoring indicators

— ‘‘‘tx_pos’’= Positiveness rate of virological tests (The positivity rate corresponds to the number of people tested positive (RT-PCR and antigenic test) for the first time in more than 60 days compared to the total number of people tested positive or negative over a given period; and that have never been tested positive in the previous 60 days.)

— ‘‘‘tx_incid’’= ** Incidence rate** (epidemic activity: The incidence rate is the number of people tested positive (RT-PCR and antigenic test) for the first time in more than 60 days compared to population size. It is expressed per 100000 inhabitants)

— ‘‘‘to’’= Occupancy rate: hospital stress on resuscitation capacity (proportion of COVID-19 patients currently in resuscitation, intensive care, or continuous surveillance unit reported to total beds in initial capacity, i.e. before increasing the capacity of resuscitation beds in a hospital).

— ‘‘‘R’’= ** Virus reproductive factor** (R0 evolution: The number of reproduction of the virus: this is the average number of people an infected person can contaminate. If the actual R is greater than 1, the epidemic develops; if it is less than 1, the epidemic decreases)

— ** Attention points**: — Data collection methods have evolved over time; — During the summer of 2020, the data were not published during weekends and holidays.

resources

— view dashboard — see COVID-19 data inventory on data.gouv.fr — view data from Santé publique France — **[consult data from the Ministry of Solidarity and Health](https://www.data.gouv.fr/fr/organizations/ministere-des-solidarites-et-de-

As of November 24, 2024 there were over 274 million confirmed cases of coronavirus (COVID-19) across the whole of Europe since the first confirmed cases in France in January 2020. France has been the worst affected country in Europe with 39,028,437 confirmed cases, followed by Germany with 38,437,756 cases. Italy and the UK have approximately 26.8 million and 25 million cases respectively. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

Content

This dataset contains # of deaths by date in France 2020-04-11 Now contains nursing home data

Source

Santé Publique France

Abstract France was the first European country to confirm cases of COVID-19, being one of the most affected by the pandemic in the first wave. This case study analyzed the measures adopted by the country in the fight against COVID-19 in 2020 and 2021, correlating it to the characteristics of its health and surveillance system. As a welfare state, it relied on compensatory policies and protection of the economy, as well as increased investments in health. There were weaknesses in the preparation and delay in the implementation of the coping plan. The response was coordinated by the national executive power, adopting strict lockdowns in the first two waves, mitigating restrictive measures in the other waves, after the increase in vaccination coverage and in the face of population resistance. The country faced problems with testing, case and contact surveillance and patient care, especially in the first wave. It was necessary to modify the health insurance rules to expand coverage, access and better articulation of surveillance actions. It indicates lessons learned about the limits of its social security system, but also the potential of a government with a strong response capacity in the financing of public policies and regulation of other sectors to face the crisis.

Context

Try to scrap data from official website of South Korea & France linked to COVID-19 confirmed cases and death in 2020

Content

Script to scrap data (France Publique Santé et South Korean KCDC) Results of scrapy : Data of COVID-19 confirmed cases & deaths Use direct link to differents sources : look at Acknowledgements

I use a very simple R0 model to try to evaluate what would happened without lock-down in Hubei, France, South-Korea, Italy in this https://www.kaggle.com/jeugregg/coronavirus-visualization-modeling

Acknowledgements

The world data is taken from https://github.com/CSSEGISandData/COVID-19 provided by JHU CSSE

South Korea areas data are retrieved with scrapy from online KCDC Press Release articles at https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030.

France areas data are taken with scrapy from online santepubliquefrance.fr Press articles at https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde and https://www.worldometers.info/coronavirus/country/france/ but until 25th March 2020.

For Global France, data are from https://www.data.gouv.fr/fr/datasets/donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/

For Global Italy, Germany, Hubei data are from https://www.worldometers.info/coronavirus/

Inspiration

What is the result of how each countries try to struggle this virus ?

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

Context

This dataset contains around 6k articles related to COVID-19 from 69 french-speaking news websites.

Content

• date_publish: Date of the publication. Some dates are inaccurate.
• title: Headline of the article.
• description: Short description of the article.
• maintext: Main content of the article. Some data are truncated because a subscription is required.
• url: URL of the article.
• labels: Categories of the article.

Acknowledgements

This dataset was collected from lemonde.fr, lefigaro.fr, liberation.fr, leparisien.fr, lesechos.fr, la-croix.com, lequipe.fr, slate.fr, latribune.fr, nouvelobs.com, lexpress.fr, marianne.net, francesoir.fr, leprogres.fr, lejdd.fr, linternaute.com, telerama.fr, bfmtv.com, lci.fr, francetvinfo.fr, boursorama.com, rtl.fr, clubic.com, huffingtonpost.fr, capital.fr, ledauphine.com, parismatch.com, europe1.fr, legorafi.fr, lalibre.be, lesoir.be, closermag.fr, elle.fr, esprit.presse.fr, sciencesetavenir.fr, politis.fr, caminteresse.fr, femmeactuelle.fr, nationalgeographic.fr, voici.fr, regards.fr, larecherche.fr, lhistoire.fr, journalmetro.com, dhnet.be, letemps.ch, levif.be, lesaffaires.com, lactualite.com, rtbf.be, franceinter.fr, lepetitjournal.com, lapresse.ca, futura-sciences.com, science-et-vie.com, pourlascience.fr, demotivateur.fr, buzzbeed.com, nordpresse.be, bopress.ma, secretnews.fr, letelegramme.fr, numerama.com, laprovence.com, ladepeche.fr, midilibre.fr, telestar.fr, courrierinternational.com and melty.fr.

Inspiration

• Study the media impact of COVID-19
• Analyze different writing styles
• Sentiment Analysis
• News generation

Citation

If you're using this dataset for research purposes, please use the following BibTex for citations:

@dataset{covidfrenchnews,
 author = {Gustave Cortal},
 year = {2021},
 month = {03},
 title = {COVID-19: French news dataset},
 url = {https://www.gustavecortal.com}

}