Manually collected daily snapshots of worldometer COVID-19 data (since April 18)

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Content

The dataset contains data about the numbers of tests, cases, deaths, serious/critical cases, active cases and recovered cases in each country for every day since April 18, and also contains the population of each country to calculate per-capita penetration of the virus

I've removed data from the "Diamond Princess" and "MS Zaandam" since they are not countries

Additionally, an auxiliray table with information about the fraction of the general population at different age groups for every country is added (taken from Wikipedia). This is specifically relevant since COVID-19 death rate is very much age dependent.

Acknowledgements

The people at "www.worldometers.info" collecting and maintaining this site really are doing very important work "https://www.worldometers.info/coronavirus/#countries">https://www.worldometers.info/coronavirus/#countries

Data about age structure for every country comes from wikipedia

Inspiration

It's possible to use this dataset for various purposes and analyses My goal will be to use the additional data about the number of tests performed in each country to estimate the true death and infection rates of COVID-19

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.

Context

This data was collected as part of a university research paper where COVID-19 cases were analysed using a cross-sectional regression model as at 17th May 2020. In order to better understand COVID-19 cases growth at a country level I decided to create a dataset containing key dates in the progression of the virus globally.

Content

210 rows, 6 columns.

This dataset contains data relating to COVID-19 cases for 210 countries globally. Data was collected using the most recent and reliable information as at 17th May 2020. The majority of data was collected from Worldometer. https://www.worldometers.info/coronavirus/#countries

This dataset contains dates for the 1st coronavirus case, 100th coronavirus case, and (50th coronavirus case per 1 million people) for 210 countries. Data is also provided for the number of days between the 1st case and the 100th as well as the 1st case and the 50th per 1 million people.

Data prior to 15th February 2020, was not easily accessible at the country level from Worldometer. Therefore any dates prior to 15th February 2020 were not sourced from Worldometer but reputable government and local media sources.

Blanks (null values) indicate that the country in question has not reached either 50 coronavirus cases per 1 million people or 100 coronavirus cases. These were left blank.

Acknowledgements

I would like to acknowledge Worldometer for providing the vast majority of the data in this file. Worldometer is a website that provides real time statistics on topics such as coronavirus cases. Its sources include government official reports as well as trusted local media sources all of which are referenced on their website.

Inspiration

Hopefully this data can be used to better understand the growth of COVID-19 cases globally.

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/

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

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

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

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.

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/

In past 24 hours, India, Asia had 68 new cases, N/A deaths and N/A recoveries.

In past 24 hours, USA, North America had 1,151 new cases, 7 deaths and 10,109 recoveries.

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

In past 24 hours, Finland, Europe had N/A new cases, N/A deaths and 17 recoveries.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

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/

According to Cognitive Market Research, the global alcohol-based hand sanitizer market size is USD 2351.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 3.60% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 940.5 million in 2023 and will grow at a compound annual growth rate (CAGR) of 1.8% from 2023 to 2030
Europe accounted for a share of over 30% of the global market size of USD 705.4 million
Asia Pacific held the market of more than 23% of the global revenue with a market size of USD 540.8 million in 2023 and will grow at a compound annual growth rate (CAGR) of 5.6% from 2023 to 2030
Latin America market of more than 5% of the global revenue with a market size of USD 117.6 million in 2023 and will grow at a compound annual growth rate (CAGR) of 3.0% from 2023 to 2030
Middle East and Africa held the major market of more than 2% of the global revenue with a market size of USD 47.02 million in 2023 and will grow at a compound annual growth rate (CAGR) of 3.3% from 2023 to 2030

Enhanced Focus on Hand Sanitization to Provide Viable Market Output

Consumer behavior has been significantly impacted by the global coronavirus outbreak, which has also encouraged consumers to improve their personal hygiene, especially their hand hygiene. 

As of February 23, 2022, approximately 43 million individuals worldwide have been infected by the coronavirus, with 6.5 million cases still active and 0.59 million deaths recorded, according to Worldometer.

Source-www.worldometers.info/coronavirus/coronavirus-death-toll/

France, Russia, the United States, and the United Kingdom are the nations most badly impacted. As a result, customers became alarmed by the rising number of virus-related deaths and began paying more attention to hand hygiene as a defense against getting sick. The World Health Organization, the Centers for Disease Control and Prevention, and medical professionals everywhere advise using hand sanitizers as well. They assert that applying an alcohol-based hand rub is one of the best defenses against the virus. The alcohol-based hand sanitizer market is currently growing because of this factor.

Increasing Consciousness and Governmental Efforts to Propel Market Growth

The public's increasing awareness of the importance of hand hygiene, sparked by government and health organization campaigns, is driving a notable increase in the alcohol-based hand sanitizer industry. Consumer demand for alcohol-based hand sanitizer has surged as a result of awareness of the product's critical role in stopping the transmission of infectious diseases. The market has had significant effects from the COVID-19 pandemic. The virus is extremely contagious, thus there is an immediate need for strong disinfection procedures. The alcohol-based hand sanitizer have become a popular and practical answer to this problem. Continuous market expansion is the outcome of the pandemic's indelible habit of alcohol-based hand sanitizer use in daily routines.

Key Dynamics of

Alcohol based Hand Sanitizer Market

Key Drivers of

Alcohol based Hand Sanitizer Market

Heightened Hygiene Awareness Following the Pandemic: The COVID-19 pandemic has profoundly altered consumer habits, establishing hand hygiene as a lasting priority in homes, workplaces, and public areas. Even after the pandemic, the consistent use of hand sanitizers has become ingrained in both personal and institutional practices. Alcohol-based hand sanitizers are especially favored due to their demonstrated efficacy in eliminating 99.9% of bacteria and viruses. Health organizations such as the WHO and CDC advocate for a minimum of 60% alcohol content in sanitizers, further supporting their utilization.

Increasing Utilization in Healthcare and Commercial Settings: Hospitals, clinics, laboratories, food service sectors, and corporate offices are adopting alcohol-based sanitizers as vital tools for infection control. Hand sanitizing stations have become a common feature in commercial buildings, transportation hubs, educational institutions, and retail centers. Institutional purchasers generally buy in bulk and favor alcohol-based formulations for their rapid action and comprehensive germ protection.

Robust Product Availability Across Distribution Channels: The extensive availability of alco...

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

Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

Context

WHO declared COVID-19 as the global pandemic. Data science and research communities all over the world came together to fight against it in this tough time. This dataset contains the datewise updates of the number of confirmed, deaths, recovered, quarantine and released from quarantine cases for Bangladesh. Hopefully it will help the local community to find meaningful insight and find the pattern of the pandemic which may save millions of life.

Content

All of data are taken from the Govt.site, WHO, DGHS and Worldometer open source data. The dataset contains all data from the date of March 1, 2020 to April 3, 2020.

Column Description

Date- Specific Date
Confirmed - The number of confirmed cases
Recovered - The number of recovered cases
Deaths- The number of death cases
Quarantine - The number of quarantined cases
Released From Quarantine - The number of released quarantine cases

Acknowledgements

• corona.gov.bd: http://www.corona.gov.bd/
• DGHS: https://corona.gov.bd/press-release
• WHO: https://www.who.int/countries/bgd/en/
• Worldometer :https://www.worldometers.info/coronavirus/

Inspiration

As the dataset contains datewise updates of the coronavirus cases in Bangladesh, feel free to prepare meaningful insights from the data. Share and collaborate to find the factors of pandemic for Bangladesh, make time series calculation and so on. Don't forget to suggest useful dataset to merge along with this dataset. Thanks.

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.