Data Source Recommendation: Novel Coronavirus 2019 Dataset
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.
Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the licensing details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.
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.
Main file in this dataset is
covid_19_data.csv and the detailed descriptions are below.
- Sno – Serial number
- ObservationDate – Date of the observation in MM/DD/YYYY
- Province/State – Province or state of the observation (Could be empty when missing)
- Country/Region – Country of observation
- Last Update – Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
- Confirmed – Cumulative number of confirmed cases till that date
- Deaths – Cumulative number of of deaths till that date
- Recovered – Cumulative number of recovered cases till that date
This is older file and is not being updated now. Please use the
Added two new files with individual level information
This file is obtained from this link
This files is obtained from this link
If you are interested in knowing country level data, please refer to the following Kaggle datasets:
South Korea – https://www.kaggle.com/kimjihoo/coronavirusdataset
Italy – https://www.kaggle.com/sudalairajkumar/covid19-in-italy
Johns Hopkins university has made the data available in google sheets format here. Sincere thanks to them.
Thanks to WHO, CDC, NHC and DXY for making the data available in first place.
Picture courtesy : Johns Hopkins University dashboard
Some insights could be
- Changes in number of affected cases over time
- Change in cases over time at country level
- Latest number of affected cases
Download the dataset here.