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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F128750%2F66baee67b3e35bf9656ff816e692527e%2Fsnapshot_worldometer_july4.png?generation=1593988535797227&alt=media" alt="">

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

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/

Description

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, 2024

Categories

Coronavirus

Acknowledgements & Source:

Shuvo Kumar Basak Shuvo

Data Source: Mendeley Dataset

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

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

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/

The high sensitivity of COVID-19 and the need for high accuracy calculations necessitate collecting the required data sets from reliable sources. Thus, all information was collected and categorized from reputable sources such as WHO (World Health Organization) and worldometers site (www.worldometers.info). The worldometers site contains information such as daily mortality statistics, recovery, and newly confirmed cases. Research data including observation data is obtained from a collection of Iranian samples’ reports in three parts (i.e. death, confirmed and recovered). This countrywide daily information is confirmed by the WHO. It should be noted that the relevant data was collected between February 19 and May 16, 2020.

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1843297%2F662051bd1966c60846f34eea7ed837a3%2FScreenshot%20from%202020-04-20%2023-08-44.png?generation=1587655709318285&alt=media" alt="">

I created a few models for predicting the COVID-19 Total Cases, Total Deaths, and Total Active Cases. The model can be download here. I also created a website to display predicted charts for major countries that has great number of infection.

Content

The rows are showing the Date and the Total. The columns are showing how much total (e.g. cases) over time.

Acknowledgements

The data was extracted and scrapped from [worldometers.info)[https://worldometers.info] website into CSV file format.

Inspiration

I hope these extracted data can help others in their model faster.

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

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

COVID-19 Dataset for Epidemic Model Development

I combined several data sources to gain an integrated dataset involving country-level COVID-19 confirmed, recovered and fatalities cases which can be used to build some epidemic models such as SIR, SIR with mortality. Adding information regarding population which can be used for calculating incidence rate and prevalence rate. One of my applications based on this dataset is published at https://dylansp.shinyapps.io/COVID19_Visualization_Analysis_Tool/.

Content

My approach is to retrieve cumulative confirmed cases, fatalities and recovered cases since 2020-01-22 onwards from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) COVID-19 dataset, merged with country code as well as population of each country. For the purpose of building epidemic models, I calculated information regarding daily new confirmed cases, recovered cases, and fatalities, together with remaining confirmed cases which equal to cumulative confirmed cases - cumulative recovered cases - cumulative fatalities. I haven't yet to find creditable data sources regarding probable cases of various countries yet. I'll add them once I found them.

• Date: The date of the record.
• Country_Region: The name of the country/region. -alpha-3_code: country code for that can be used for map visualization.
• Population: The population of the given country/region.
• Total_Confirmed_Cases: Cumulative confirmed cases.
• Total_Fatalities: Cumulative fatalities.
• Total_Recovered_Cases: Cumulative recovered cases.
• New_Confirmed_Cases: Daily new confirmed cases.
• New_Fatalities: Daily new fatalities.
• New_Recovered_Cases: Daily new recovered cases.
• Remaining_Confirmed_Cases: Remaining infected cases which equal to (cumulative confirmed cases - cumulative recovered cases - cumulative fatalities).

Acknowledgements

1. The data source of confirmed cases, recovered cases and deaths is JHU CSSE https://github.com/CSSEGISandData/COVID-19;
2. The data source of the country-level population mainly comes from https://storage.guidotti.dev/covid19/data/ and Worldometer (https://www.worldometers.info/population/).

Inspiration

1. Building up the country-level COVID-19 case track dashboard.
2. Insights regarding the incidence rate, prevalence rate, mortality and recovery rate of various countries.
3. Building up epidemic models for forecasting.

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...

Based on data extracted from Worldometer: 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, Iran, Asia had N/A new cases, N/A deaths and N/A recoveries.

In past 24 hours, Réunion, Africa 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.

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.

In past 24 hours, Europe had 165 new cases, 16 deaths and 104 recoveries.

This dataset merges three distinct data sources to explore the relationship between COVID-19 death rates, vaccination efforts, and public sentiment on Twitter from December 25, 2020 to March 29, 2022. It includes 2,000 cleaned rows with 16 variables, created by combining global health statistics and social media sentiment data.

Sources & Variables:

1. COVID-19 Deaths Data (scraped from Worldometer - COVID-19 Deaths via BeautifulSoup):

   • Date: Date of record
   • daily_increase_percent: % change in deaths from previous day
   • Season: Derived from date (Winter, Spring, Summer, Fall)

2. Tweet Sentiment Data : COVID Vaccine Tweets Dataset

   • Date: Tweet timestamp
   • text_sentiment: Sentiment label (positive, neutral, negative) from NLTK’s SentimentIntensityAnalyzer
   • user_verified: Whether the user is verified
   • user_since_days: Age of the Twitter account (in days)
   • country: Cleaned user location

3. Vaccination Data : Vaccination Dataset

   • Date: Date of record
   • total_vaccinations_per_hundred: Doses per 100 people
   • daily_vaccinations: Daily dose count
   • vaccine_group: Grouped vaccine type (e.g., mRNA, Viral Vector)
   • country: Country name

Preprocessing Summary:

• Merged by Date and country
• Cleaned invalid country names (e.g., “moon”, “nowhere”)
• Standardized all datetime formats
• Removed entries with missing or unreliable values
• Created derived variables: Season, user_since_days, vaccine_group

This dataset was used in a final data science project to:

• Classify public sentiment toward vaccines using health indicators
• Predict daily COVID-19 death counts using sentiment and vaccination data