COVID-19 statistics from Worldometers. Covers 213 countries/ territories. Recorded as of 22nd May 2020, 14:56 PM IST. The purpose of this data is to understand and analyse the trends of COVID-19, and the extent of its spread.

Note: The new_cases column is full of strings that look like numbers. To convert them to numbers, see the following kernel: https://www.kaggle.com/danoozy44/coronavirus-predicting-new-cases

The new_cases and new_deaths columns pertain to 22/05/2020 only.

All credit goes to Worldometers, and its constituent data gatherers. The official link is here: 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.

This dataset, titled "Global COVID-19 Statistics - Jan 2025," contains the latest COVID-19 statistics collected from the Worldometer website on Jan 09, 2025. The data includes crucial metrics such as the total number of cases, deaths, recoveries, and active cases for countries around the world. The information is extracted from the comprehensive table provided by Worldometer, which is widely regarded as a reliable source for real-time coronavirus statistics. Source and Collection Date Source: Worldometer Coronavirus Page Date of Collection: Jan 09, 2025

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe.

Content

The COVID-19 datasets organized by continent contain daily level information about the COVID-19 cases in the different continents of the world. It is a time-series data and the number of cases on any given day is cumulative. The original datasets can be found on this John Hopkins University Github repository. I will be updating the COVID-19 datasets on a daily basis, with every update from John Hopkins University. I have also included the World COVID-19 tests data scraped from Worldometer and 2020 world population also from [worldometer]((https://www.worldometers.info/world-population/population-by-country/).

The datasets

COVID-19 cases covid19_world.csv. It contains the cumulative number of COVID-19 cases from around the world since January 22, 2020, as compiled by John Hopkins University. covid19_asia.csv, covid19_africa.csv, covid19_europe.csv, covid19_northamerica.csv, covid19.southamerica.csv, covid19_oceania.csv, and covid19_others.csv. These contain the cumulative number of COVID-19 cases organized by the continent.

Field description - ObservationDate: Date of observation in YY/MM/DD - Country_Region: name of Country or Region - Province_State: name of Province or State - Confirmed: the number of COVID-19 confirmed cases - Deaths: the number of deaths from COVID-19 - Recovered: the number of recovered cases - Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

COVID-19 tests `covid19_tests.csv. It contains the cumulative number of COVID tests data from worldometer conducted since the onset of the pandemic. Data available from June 01, 2020.

Field description Date: date in YY/MM/DD Country, Other: Country, Region, or dependency TotalTests: cumulative number of tests up till that date Population: population of Country, Region, or dependency Tests/1M pop: tests per 1 million of the population 1 Testevery X ppl: 1 test for every X number of people

2020 world population world_population(2020).csv. It contains the 2020 world population as reported by woldometer.

Field description Country (or dependency): Country or dependency Population (2020): population in 2020 Yearly Change: yearly change in population as a percentage Net Change: the net change in population Density(P/km2): population density Land Area(km2): land area Migrants(net): net number of migrants Fert. Rate: Fertility Rate Med. Age: median age Urban pop: urban population World Share: share of the world population as a percentage

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. COVID-19 Africa dashboard: http://covid-19-africa.sen.ovh/
4. Worldometer: https://www.worldometers.info/
5. SRk for uploading a global COVID-19 dataset on Kaggle: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset#covid_19_data.csv
6. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
7. wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

Inspiration

Possible Insights 1. The current number of COVID-19 cases in Africa 2. The current number of COVID-19 cases by country 3. The number of COVID-19 cases in Africa / African country(s) by May 30, 2020 (Any future date)

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 latest trend plot. It covers China, Canada, Australia (at province/state level), and the rest of the world (at country level, represented by either the country centroids or their capitals)and the US at county-level. Data sources: WHO, CDC, ECDC, NHC, DXY, 1point3acres, Worldometers.info, BNO, state and national government health departments, and local media reports. . The China data is automatically updating at least once per hour, and non-China data is updating hourly. 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 and JHU Data Services. This layer is opened to the public and free to share. Contact us.

In past 24 hours, N. Korea, Asia had N/A new cases, N/A deaths and N/A recoveries.

Analysis of ‘Covid19 in World Countries-Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid19-in-world-countrieslatest-data on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Content

This dataset contains Covid-19 data of world countries as on November 10, 2021

Attribute Information

• Country - Name of world countries
• Total Cases - Total number of Covid-19 cases
• Total Deaths - Total number of Deaths
• Total Recovered - Total number of recovered cases
• Active Cases - Total number of Active cases
• Total Cases/1 mil population- Total Cases per 1 million of the population
• Death/1 mil population - Total Deaths per 1 million of the population
• Total Tests - Total number of Covid tests done
• Tests/1 mil population - Covid tests done per 1 million of the population
• Population - Population of the country

Source

Link : https://www.worldometers.info/coronavirus/#countries

Other Updated Covid 19 Datasets

Link : https://www.kaggle.com/anandhuh/datasets

Upvote if you find it useful 🙏

--- Original source retains full ownership of the source dataset ---

Covid-19 cases per country snapshot

13-Apr-2020 at 14:19 CET

Data source: https://www.worldometers.info/coronavirus/

Obtained by web-scraping

Contains header on 1st row.

Columns:

• Index: Number, not used
• Country: PK, text, unique identifier for Country
• Total Cases: Int, total cases per country.
• New Cases (Nuevos casos): Int, difference between cases from one day to the other.
• Total Deaths: Int, total deaths per country.
• New Deaths: Int, difference between deaths from one day to the other.
• Total Recovered: Int, total recovered cases per country.
• Active Cases: Int, total active cases per country.
• Serious, Critical: Int, total seriuos or critical cases per country.
• Tot Cases/1M pop: Float, number of deaths per 1M population
• Tot Deaths/1M pop: Float, number of deaths per 1M population

Context

The 2019–20 coronavirus pandemic is an ongoing global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus first emerged in Wuhan, Hubei, China, in December 2019. On 11 March 2020, the World Health Organization declared the outbreak a pandemic. As of 11 March 2020, over 126,000 cases have been confirmed in more than 110 countries and territories, with major outbreaks in mainland China, Italy, South Korea, and Iran. More than 4,600 have died from the disease and 67,000 have recovered.

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 information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this data was scrapped from https://www.worldometers.info/coronavirus/.This data is solely for education purposes only.

Acknowledgements

This data is solely belongs to https://www.worldometers.info/coronavirus/. for licensing visit https://www.worldometers.info/licensing/

In past 24 hours, Sweden, Europe had N/A new cases, N/A deaths and 18 recoveries.

In past 24 hours, Iran, Asia had N/A new cases, N/A deaths and N/A recoveries.

This dataset is a three dimensional dataset in wich we analyze the evolution of some data related with COVID-19 along the time.

We analyse how a type of data behave along the time in the different countries.

In each csv, we have kind of varibale (Cases, recovered, deaths) by country and date (from 03/30 to 05/04). Howevwe, the data its up to date in https://github.com/AdrianArnaiz/scrap_uoc (updated automatically every day).

So we have 5 time series by country: one for each kind of data.

The csv contais the information related with a kind of data, and are described by the other two dimensions: country and date.

We obtained this dataset scrapping Worldometers.

Analysis of ‘Covid-19 Weekly Trends In Europe - Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid19-weekly-trends-in-europe-latest-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

This dataset contains data of weekly trend of Covid-19 in Europe (January 01 - January 07, 2022)

Attribute Information

1. Country/Other
2. Cases in the last 7 days
3. Cases in the preceding 7 days
4. Weekly Case % Change
5. Cases in the last 7 days/1M pop
6. Deaths in the last 7 days
7. Deaths in the preceding 7 days
8. Weekly Death % Change
9. Deaths in the last 7 days/1M pop
10. Population

Source

Link : https://www.worldometers.info/coronavirus/weekly-trends/#weekly_table

Other Updated Covid Datasets

Link : https://www.kaggle.com/anandhuh/datasets

Please appreciate the effort with an upvote 👍

Thank You

--- Original source retains full ownership of the source dataset ---

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Analysis of ‘Covid in African Countries - Latest Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/anandhuh/covid-in-african-countries-latest-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Content

This dataset contains Covid-19 data of African countries as on January 26, 2022

Attribute Information

• Country - Name of African countries
• Total Cases - Total number of Covid-19 cases
• Total Deaths - Total number of Deaths
• Total Recovered - Total number of recovered cases
• Active Cases - Total number of Active cases
• Total Cases/1 mil population- Total Cases per 1 million of the population
• Deaths/1 mil population - Total Deaths per 1 million of the population
• Total Tests - Total number of Covid tests done
• Tests/1 mil population - Covid tests done per 1 million of the population
• Population - Population of the country

Source

Link : https://www.worldometers.info/coronavirus/#countries

Other Updated Covid19 Datasets

Link : https://www.kaggle.com/anandhuh/datasets

If you find it useful, please support by upvoting 👍

Thank You

--- Original source retains full ownership of the source dataset ---

In past 24 hours, North America had 1,151 new cases, 7 deaths and 10,459 recoveries.

Data for COVID-19 Coronavirus Pandemic from Worldometer (March 27, 2020)

Research Hypothesis - That cannabis may predispose to COVID-19 viral infection due to its immunomodulatory, envorinmental contaminants, vaping and smoking inhalation actions. Data was analyzed by geospatial and causal inference techniques in R. Data was gathered from publicly available on line sources including: Data were downloaded from Publicly available datasets including: • US Census bureau 2019 • Five Year American Community Survey 2013-2018 • National Survey of Drug Use and Health (NSDUH) • NSDUH Resticted Use Data Analysis System (RDAS) • US Department of Transport International Flight Data • Worldometer Covid -19 Dataset

Data were collected in the six domains of: • COVID numbers • Fights – numbers of flights and numbers of overseas destionations • Median household income • State ethnic composition • Population and population density • Drug use

Inverse probability weights were constructed by inverse probability weighting conducted in package ipw in R.

Geospatial weights were constructed in package spdep in R.