Context

Johns Hopkins University has made an excellent dashboard using the affected case data. Data is extracted from the google sheets associated and made available here.

This data is available as CSV files in the Johns Hopkins Github repository. Please refer to the Github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

Content

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 to 28 May 2020.

Column Description

The main file in this dataset is covid_19_data_cleaned.csv and the detailed descriptions are below.
covid_19_data_cleaned.csv

• 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 standardized and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of deaths till that date
• Active - Cumulative number of Active cases till that date