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

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)

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

I would love to see notebooks! Keep bringin' em.

Content

Worldometer manually analyzes, validates, and aggregates data from thousands of sources in real time and provides global COVID-19 live statistics for a wide audience of caring people around the world.

Our data is also trusted and used by the UK Government, Johns Hopkins CSSE, the Government of Thailand, the Government of Vietnam, the Government of Pakistan, Financial Times, The New York Times, Business Insider, BBC, and many others.

Acknowledgements

Acknowledge Sujay S

Inspiration

Thanks to blogs out there on medium! That made me do this!

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.

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 ---

Content

This dataset contains Covid-19 data of European ountries as on November 21, 2021

Attribute Information

• Country/Other - Name of European countries and islands
• 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 Countries

Source

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

Other Updated Covid Datasets

https://www.kaggle.com/anandhuh/datasets Please appreciate the effort with an upvote 👍

Thank You

Content

This dataset contains data of weekly trend of Covid-19 in Asia (January 03 - January 10, 2023)

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

une

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 ---

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

Introduction

The dataset contains COVID-19 statistics for the top countries currently affected by the virus. The data was scraped from two popular sites maintaining daily updates on the spread of COVID-19 - https://www.worldometers.info/ and https://en.wikipedia.org/wiki/COVID-19_pandemic

Contents

There are two kinds of csv files. One type of files are country wise daily statistics on COVID-19 spread. The data for the following countries is available:-

• United States
• Russia
• Brazil
• Pakistan
• Germany
• Peru
• Spain
• Belgium
• Italy
• Belarus
• India
• Qatar
• Mexico
• Turkey
• Sweden
• Saudi Arabia
• Iran
• Canada
• Chile
• China
• France
• Ecuador
• Bangladesh

For each of these countries, the dataset contains the following columns:-

• Date
• total cases
• daily cases
• active cases
• total deaths
• daily deaths

The second type of file is the overall statistics which contains statistics for all the countries affected in the world. This dataset contains the following columns:-

• country name
• total cases
• total recoveries
• total deaths

CONTEXT - As of January 30, 2020, a novel coronavirus named 2019-nCoV was identified in Wuhan, China. -It caused pneumonia-like symptoms in individuals with no clear cause, and existing vaccines and treatments were ineffective. -The virus exhibited evidence of human-to-human transmission, and the transmission rate appeared to increase significantly in mid-January 2020. -By that date, approximately 8,243 confirmed cases had been reported.

DATA SOURCE https://github.com/CSSEGISandData/COVID-19 https://www.worldometers.info/

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.

Context

https://github.com/Aditta-das/scrap-worlodometers.com

Columns

Tot Cases/ 1M pop = total confirmed cases per 1 million population Deaths/1M pop = total deaths per 1 million population Tests/1M pop =total tests per 1 million population

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.

This data set combines epidemiological, genetics, and government stringency data of COVID-19 pandemics, all from open data sources. The sources are: Our World in Data, Worldometer, GISAID-Nextstrain, and the Oxford COVID-19 Government Response Tracker (OxCGRT). The cut off date of the first version is at the end of June 2020.

The simple data set is provided as an Excel workbook, where the first, "readme" worksheet describes the details of data of all the worksheets in the data set.

This is a working data set, expected to be refreshed over time.

Raw data are not cleaned - this collection is a tool to check various hypotheses regarding possible relations among the various data types. Simple visualisations of data relations are provided in a separate sheet.

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.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

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

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