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- 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)][sars2] 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.
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- [covid]: https://en.wikipedia.org/wiki/Coronavirus_disease_2019
- [sars2]: https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome_coronavirus_2
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- This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:
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- * 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
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- ## Data
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- Data is in CSV format and updated daily. It is sourced from [this upstream repository](https://github.com/CSSEGISandData/COVID-19) maintained by the amazing team at [Johns Hopkins University Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) who have been doing a great public service from an early point by collating data from around the world.
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- 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][dp].
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- You can view the data, its structure as well as download it in alternative formats (e.g. JSON) from the DataHub:
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- https://datahub.io/core/covid-19
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- [dp]: https://frictionlessdata.io/data-package/
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- ### Sources
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- The upstream dataset currently lists the following upstream datasources:
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- - 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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
- - US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
- - Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
- - Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
- - European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
- - Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
- - Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
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- 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](https://systems.jhu.edu/research/public-health/ncov/), and in the [Lancet paper](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext) ([DOI](https://doi.org/10.1016/S1473-3099(20)30120-1)), which includes this figure:
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- 
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- ## Preparation
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- This repository uses [dataflows](https://github.com/datahq/dataflows) to process and normalize the data.
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- You first need to install the dependencies:
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- ```
- pip install -r requirements.txt
- ```
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- Then run the script
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- ```
- python process.py
- ```
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- [](https://www.python.org/downloads/release/python-360/)
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- ## License
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- This dataset is licensed under the Open Data Commons [Public Domain and Dedication License][pddl].
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- [pddl]: https://www.opendatacommons.org/licenses/pddl/1-0/
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- The data comes from a variety public sources and was collated in the first instance via John 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.
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