luoluogogogo / COVID19-1

Unified dataset for a better understanding of COVID-19

Home Page:https://covid19datahub.io

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COVID-19 Data Hub

DOI eRum2020::CovidR Build Status

The repository aims at developing a unified dataset by collecting worldwide fine-grained case data, merged with exogenous variables helpful for a better understanding of COVID-19. Available in:

R | Python | MATLAB | Scala | Julia | Node.js | Excel

The data are updated on an hourly basis. Read more

Breaking Changes

  • Due to the incresing size of the data files, we stopped providing the pre-processed data on 01 April 2021, so to improve the update and storage of the raw data. Please switch to the raw data if you are still using the pre-processed files.

See the changelog

Historical Data

The dataset includes the time series of vaccines, tests, cases, deaths, recovered, hospitalizations, intensive therapy, policy measures and more. See the full dataset documentation.

Administrative Areas

The data are available at different levels of granularity:

  • admin area level 1: administrative area of top level, usually countries.
  • admin area level 2: usually states, regions, cantons.
  • admin area level 3: usually cities, municipalities.

Direct Download

The latest and vintage CSV data files are available here.

Data Sources

Add a new data source

You are welcome to join and extend the number of supporting data sources as a joint effort against COVID-19. Join us on Slack to get help, add a new data source and earn a badge.

Use Cases

See the projects and publications that use COVID-19 Data Hub.

Cite as

We have invested a lot of time and effort in creating COVID-19 Data Hub, please agree to the Terms of Use and cite the following reference when using it:

Guidotti, E., Ardia, D., (2020), "COVID-19 Data Hub", Journal of Open Source Software 5(51):2376, doi: 10.21105/joss.02376.

A BibTeX entry for LaTeX users is:

@Article{,
    title = {COVID-19 Data Hub},
    year = {2020},
    doi = {10.21105/joss.02376},
    author = {Emanuele Guidotti and David Ardia},
    journal = {Journal of Open Source Software},
    volume = {5},
    number = {51},
    pages = {2376}
}

Supported by

R Consortium IVADO HEC Montréal Hack Zurich Università degli Studi di Milano

About

Unified dataset for a better understanding of COVID-19

https://covid19datahub.io

License:GNU General Public License v3.0


Languages

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