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Novel Coronavirus 2019

core

Files Size Format Created Updated License Source
8 547MB csv zip 3 months ago 2 hours ago
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 read more
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Data Files

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time-series-19-covid-combined  

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Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Country/Region 2 string (default)
Province/State 3 string (default)
Lat 4 number (default)
Long 5 number (default)
Confirmed 6 integer (default)
Recovered 7 integer (default)
Deaths 8 integer (default)

key-countries-pivoted  

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Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
China 2 integer Cumulative total confirmed cases to date.
US 3 integer Cumulative total confirmed cases to date.
United_Kingdom 4 integer Cumulative total confirmed cases to date.
Italy 5 integer Cumulative total confirmed cases to date.
France 6 integer Cumulative total confirmed cases to date.
Germany 7 integer Cumulative total confirmed cases to date.
Spain 8 integer Cumulative total confirmed cases to date.
Iran 9 integer Cumulative total confirmed cases to date.

countries-aggregated  

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Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Country 2 string (default)
Confirmed 3 integer (default)
Recovered 4 integer (default)
Deaths 5 integer (default)

worldwide-aggregated  

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Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Confirmed 2 integer (default)
Recovered 3 integer (default)
Deaths 4 integer (default)
Increase rate 5 number Inrease rate from the previous day in percentage.

us_confirmed  

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Field information

Field Name Order Type (Format) Description
UID 1 integer (default)
iso2 2 string (default)
iso3 3 string (default)
code3 4 integer (default)
FIPS 5 number (default)
Admin2 6 string (default)
Lat 7 number (default)
Combined_Key 8 string (default)
Date 9 date (%Y-%m-%d)
Case 10 number (default)
Long 11 number (default)
Country/Region 12 string (default)
Province/State 13 string (default)

us_deaths  

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Field information

Field Name Order Type (Format) Description
UID 1 integer (default)
iso2 2 string (default)
iso3 3 string (default)
code3 4 integer (default)
FIPS 5 number (default)
Admin2 6 string (default)
Lat 7 number (default)
Combined_Key 8 string (default)
Population 9 integer (default)
Date 10 date (%Y-%m-%d)
Case 11 number (default)
Long 12 number (default)
Country/Region 13 string (default)
Province/State 14 string (default)

reference  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
UID 1 integer (default)
iso2 2 string (default)
iso3 3 string (default)
code3 4 integer (default)
FIPS 5 string (default)
Admin2 6 string (default)
Province_State 7 string (default)
Country_Region 8 string (default)
Lat 9 number (default)
Long_ 10 number (default)
Combined_Key 11 string (default)
Population 12 integer (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/covid-19
data info core/covid-19
tree core/covid-19
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/covid-19/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/covid-19/r/0.csv

curl -L https://datahub.io/core/covid-19/r/1.csv

curl -L https://datahub.io/core/covid-19/r/2.csv

curl -L https://datahub.io/core/covid-19/r/3.csv

curl -L https://datahub.io/core/covid-19/r/4.csv

curl -L https://datahub.io/core/covid-19/r/5.csv

curl -L https://datahub.io/core/covid-19/r/6.csv

curl -L https://datahub.io/core/covid-19/r/7.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/covid-19/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/covid-19/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/covid-19/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/covid-19/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

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:

Preparation

This repository uses dataflows to process and normalize the data.

You first need to install the dependencies:

pip install -r scripts/requirements.txt

Then run the script

python scripts/process.py

Python 3.6 .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.

Datapackage.json

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