Novel Coronavirus 2019

FilesSizeFormatCreatedUpdatedLicenseSource
8310 MBcsvover 2 years agoOpen Data Commons Public Domain Dedication and License

Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COV...

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Data Views

Data Files

FileDescriptionSizeLast modifiedDownload
countries-aggregated
5.51 MBover 2 years ago
countries-aggregated
reference
416 kBover 2 years ago
reference
key-countries-pivoted
57.6 kBover 2 years ago
key-countries-pivoted
time-series-19-covid-combined
8.58 MBover 2 years ago
time-series-19-covid-combined
us_confirmed
97.1 MBover 2 years ago
us_confirmed
us_deaths
93.2 MBover 2 years ago
us_deaths
us_simplified
105 MBover 2 years ago
us_simplified
worldwide-aggregate
43.4 kBover 2 years ago
worldwide-aggregate

Data Previews

countries-aggregated

Schema

nametypeformat
Datedate%Y-%m-%d
Countrystringdefault
Confirmedintegerdefault
Recoveredintegerdefault
Deathsintegerdefault

reference

Schema

nametypeformat
UIDintegerdefault
iso2stringdefault
iso3stringdefault
code3integerdefault
FIPSstringdefault
Admin2stringdefault
Province_Statestringdefault
Country_Regionstringdefault
Latnumberdefault
Long_numberdefault
Combined_Keystringdefault
Populationintegerdefault

key-countries-pivoted

Schema

nametypeformat
Datestring%Y-%m-%d
Chinaintegerdefault
USintegerdefault
United_Kingdomintegerdefault
Italyintegerdefault
Franceintegerdefault
Germanyintegerdefault
Spainintegerdefault
Iranintegerdefault

time-series-19-covid-combined

Schema

nametypeformat
Datedate%Y-%m-%d
Country/Regionstringdefault
Province/Statestringdefault
Confirmedintegerdefault
Recoveredintegerdefault
Deathsintegerdefault

us_confirmed

Schema

nametypeformat
Admin2stringdefault
Datedate%Y-%m-%d
Caseintegerdefault
Country/Regionstringdefault
Province/Statestringdefault

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COVID-19 dataset

Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COVID-19) is caused by the Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and has had a worldwide effect. On March 11 2020, the World Health Organization (WHO) declared it a pandemic, pointing to the over 118,000 cases of the Coronavirus illness in over 110 countries and territories around the world at the time.

This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:

  • confirmed tested cases of Coronavirus infection
  • the number of people who have reportedly died while sick with Coronavirus
  • the number of people who have reportedly recovered from it

Data

Data is in CSV format and updated daily. It is sourced from this upstream repository maintained by the amazing team at Johns Hopkins University Center for Systems Science and Engineering (CSSE) who have been doing a great public service from an early point by collating data from around the world.

We have cleaned and normalized that data, for example tidying dates and consolidating several files into normalized time series. We have also added some metadata such as column descriptions and data packaged it.

You can view the data, its structure as well as download it in alternative formats (e.g. JSON) from the DataHub:

https://datahub.io/core/covid-19

Sources

The upstream dataset currently lists the following upstream data sources:

We will endeavour to provide more detail on how regularly and by which technical means the data is updated. Additional background is available in the CSSE blog, and in the Lancet paper (DOI), which includes this figure:

countries timeline

Preparation

This repository uses Pandas to process and normalize the data.

You first need to install the dependencies:

pip install -r scripts/requirements.txt

Then run the following scripts:

python scripts/process_worldwide.py
python scripts/process_us.py

Python 3.8 .github/workflows/actions.yml

License

This dataset is licensed under the Open Data Commons Public Domain and Dedication License.

The data comes from a variety public sources and was collated in the first instance via Johns Hopkins University on GitHub. We have used that data and processed it further. Given the public sources and factual nature we believe that there the data is public domain and are therefore releasing the results under the Public Domain Dedication and License. We are also, of course, explicitly licensing any contribution of ours under that license.