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owid logocovid-19-data

Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

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Quick Overview

The owid/covid-19-data repository is a comprehensive collection of COVID-19 data maintained by Our World in Data. It provides up-to-date and historical data on COVID-19 cases, deaths, vaccinations, and other related metrics for countries and regions worldwide. The repository serves as a valuable resource for researchers, data analysts, and policymakers studying the pandemic's impact and trends.

Pros

  • Regularly updated with the latest COVID-19 data from reliable sources
  • Covers a wide range of metrics, including cases, deaths, vaccinations, and testing
  • Provides data in easily accessible formats (CSV, JSON) for various countries and regions
  • Includes detailed documentation and methodologies for data collection and processing

Cons

  • Data quality may vary depending on reporting practices of different countries
  • Some metrics may have gaps or inconsistencies due to changes in reporting methods
  • Requires frequent updates to maintain accuracy, which may lead to occasional delays
  • Large dataset size may be challenging to handle for users with limited computational resources

Getting Started

To use the data from this repository:

  1. Clone the repository:

    git clone https://github.com/owid/covid-19-data.git

  2. Navigate to the desired dataset folder (e.g., public/data/)

  3. Use your preferred data analysis tool (e.g., Python, R, Excel) to load and analyze the CSV files

For example, to load the global COVID-19 data using Python and pandas:

import pandas as pd

# Load the global COVID-19 data
df = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv')

# Display the first few rows
print(df.head())

# Get summary statistics
print(df.describe())

This repository is primarily a data source rather than a code library, so extensive code examples are not applicable. Users typically access the data directly and process it using their preferred tools and methods.

Competitor Comparisons

CSSEGISandData logo

COVID-19

29,136

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

Pros of COVID-19

  • More granular data, including county-level information for some countries
  • Longer historical data, dating back to the beginning of the pandemic
  • Includes recovered cases data for many regions

Cons of COVID-19

  • Less consistent data formatting across different regions
  • Fewer additional variables (e.g., testing data, vaccinations) compared to covid-19-data
  • Less frequent updates for some countries

Code Comparison

COVID-19 data format (example):

Province/State,Country/Region,Lat,Long,Date,Confirmed,Deaths,Recovered
New York,US,40.7128,-74.0060,2021-03-01,1000000,50000,900000

covid-19-data format (example):

location,date,total_cases,new_cases,total_deaths,new_deaths,total_vaccinations
United States,2021-03-01,28756489,56079,515151,1431,76899987

The COVID-19 repository provides more detailed geographical information, while covid-19-data offers a more standardized format with additional variables like vaccinations.

nytimes logo

covid-19-data

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A repository of data on coronavirus cases and deaths in the U.S.

Pros of covid-19-data (NYTimes)

  • More granular US data, including county-level information
  • Frequent updates, often multiple times per day
  • Includes additional datasets like excess deaths and mask mandates

Cons of covid-19-data (NYTimes)

  • Limited to US data only
  • Less historical data compared to OWID repository
  • Fewer data categories (e.g., no vaccination or testing data)

Code Comparison

covid-19-data (NYTimes):

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv')
df['date'] = pd.to_datetime(df['date'])

covid-19-data (OWID):

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv')
df['date'] = pd.to_datetime(df['date'])

The code snippets show similar data loading processes, but the NYTimes data is focused on US states, while OWID provides global data. Both repositories offer CSV files that can be easily loaded into pandas DataFrames for analysis.

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Pros of app

  • Official WHO mobile application, providing direct access to authoritative COVID-19 information
  • User-friendly interface for easy access to health guidelines and updates
  • Multilingual support, making information accessible to a global audience

Cons of app

  • Limited to mobile platforms, potentially excluding users without smartphones
  • May not provide as comprehensive or detailed data as covid-19-data
  • Focuses more on general health information rather than extensive statistical data

Code comparison

app (React Native):

import React from 'react';
import { Text, View } from 'react-native';
import { NavigationContainer } from '@react-navigation/native';

const App = () => {
  return (
    <NavigationContainer>
      <View><Text>WHO COVID-19 App</Text></View>
    </NavigationContainer>
  );
};

covid-19-data (Python):

import pandas as pd
import numpy as np

def process_data(data):
    df = pd.DataFrame(data)
    df['new_cases'] = df['total_cases'].diff()
    return df.dropna()

processed_data = process_data(raw_data)

The app repository focuses on mobile app development using React Native, while covid-19-data primarily uses Python for data processing and analysis. The app code snippet shows a basic React Native component structure, whereas the covid-19-data code demonstrates data manipulation using pandas.

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Pros of covid19india-react

  • Focused on India-specific COVID-19 data visualization
  • Interactive web application with real-time updates
  • User-friendly interface for easy data exploration

Cons of covid19india-react

  • Limited to Indian data, less comprehensive global coverage
  • May require more frequent updates to maintain accuracy
  • Potentially higher resource requirements for hosting and maintenance

Code Comparison

covid19india-react (React component):

const MapExplorer = ({ mapData, setHoveredRegion }) => {
  const [currentMap, setCurrentMap] = useState({});
  const [currentHoveredRegion, setCurrentHoveredRegion] = useState({});
  // ... (more component logic)
};

covid-19-data (Python data processing):

def generate_dataset():
    all_covid = []
    all_vaccinations = []
    for country in country_data:
        covid = process_covid(country)
        vaccinations = process_vaccinations(country)
        all_covid.append(covid)
        all_vaccinations.append(vaccinations)
    return all_covid, all_vaccinations

The covid19india-react repository focuses on creating a React-based web application for visualizing COVID-19 data specific to India, while the covid-19-data repository emphasizes data collection and processing for global COVID-19 statistics. The code snippets reflect these differences, with covid19india-react using React components for UI rendering and covid-19-data utilizing Python for data manipulation and analysis.

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README

Warning Our GitHub datasets are no longer updated since 19 August 2024

You can access the CSV version of our COVID-19 dataset via Our World in Data's data catalog: https://catalog.ourworldindata.org/garden/covid/latest/compact/compact.csv

See our complete documentation for more information, other files, and metadata: https://docs.owid.io/projects/etl/api/covid/

COVID-19 Dataset by Our World in Data

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Project structure

The project contains two independent directories:

  • public/data: Contains the final datasets. This is for people interested in consuming the data and understanding all the caveats about it and its metrics.
  • scripts: Contains all the code and intermediate files to produce the final dataset. This is for people interested in contributing to the project or better understanding our internal technical processes.

Documentation

If you are interested in the final dataset file, refer to this document. If you want to learn more about our processes, refer to our technical documentation.

Contribute

Thanks for considering contributing to this project! A good place to start is our contribution guideline.