Context

The files provided are daily datasets that I scraped from the COVID-19 tracking website Worldometer over the course of 3 days—08/04/21–08/06/21. The dates don't necessarily have to contain the most recent data because that is not the intent of this dataset.

Inspiration

For me, I find making data visualizations very satisfying. Seeing a neat and tidy graph come out of an enormous CSV file is very inspirational to me. The goal is simply to use this data to make visualizations of how COVID-19 is continuing to affect each country throughout the world.

The Data

I made a pandas DataFrame out of the table on the website, and I included all 21 of their columns. Descriptions for each column are provided below.

• Country: String. Name of each country.
• TotalCases: Integer. Total number of cases
• NewCases: Integer. Number of new additional cases
• TotalDeaths: Integer. Total number of deaths due to COVID-19
• NewDeaths: Integer. Number of new additional deaths
• TotalRecovered: Integer. Total number of patients recovered from COVID-19
• NewRecovered: Integer. Number of new additional recovered patients
• ActiveCases: Integer. Number of current active cases
• Critical: Integer. Number of critically ill patients
• Tot Cases/1M pop: Integer. Total cases per 1M (one million) population
• Deaths/1M pop: Float. Deaths per 1M population
• TotalTests: Integer Total number of COVID-19 tests administered
• Tests/1M pop: String. Tests per 1M population
• Population: Integer. Population of country
• Continent: String. Continent on which the country is located
• 1 Case Every X ppl: Integer. Gives us an idea of the rate of cases per country
• 1 Death Every X ppl: Integer. Gives us an idea of the rate of death due to COVID-19
• 1 Test Every X ppl: Integer. Gives us an idea of the rate of testing per country
• New Cases/1M pop: Float. New cases per 1M population
• New Deaths/1M pop: Integer. New deaths per 1M population
• Active Cases/1M pop: Integer. Active cases per 1M population

Sources

This data was collected from https://www.worldometers.info/coronavirus/

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources:Global: World Health Organization (WHO)U.S.: U.S. Centers for Disease Control and Prevention (CDC)For more information, visit the Johns Hopkins Coronavirus Resource Center.This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.

This feature layer contains the most up-to-date COVID-19 cases and the latest trend plot. It covers the US (county or state level), China, Canada, Australia (province/state level), and the rest of the world (country/region level, represented by either the country centroids or their capitals). Data sources are WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, the COVID Tracking Project (testing and hospitalizations), state and national government health departments, and local media reports. This layer is created and maintained by the Center for Systems Science and Engineering (CSSE) at the Johns Hopkins University. This feature layer is supported by Esri Living Atlas team, JHU APL and JHU Data Services. This layer is opened to the public and free to share. Contact us.