This dataset provides comprehensive statistics on COVID-19 for countries around the world. It includes data on the number of active cases, critical cases, total deaths, and total tests conducted. The dataset is updated frequently to ensure the most current information is available.

Key Features:

Global Coverage: Data for countries across all continents, including Asia, Africa, Europe, North America, South America, and Oceania. Detailed Statistics: Includes metrics such as active cases, critical cases, total deaths, and total tests. Population Data: Provides population figures for each country to contextualize the COVID-19 statistics. Frequent Updates: The dataset is updated regularly to reflect the latest information.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

Context:

From World Health Organization - On 31 December 2019, WHO was alerted to several cases of pneumonia in Wuhan City, Hubei Province of China. The virus did not match any other known virus. This raised concern because when a virus is new, we do not know how it affects people.

So daily level information on the affected people can give some interesting insights when it is made available to the broader data science community.

Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

Edited:

Now data is available as csv files in the Johns Hopkins Github repository. Please refer to the github repository for the Terms of Use details. Uploading it here for using it in Kaggle kernels and getting insights from the broader DS community.

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 daily level information on the number of affected cases, deaths and recovery from 2019 novel coronavirus. Please note that this is a time series data and so the number of cases on any given day is the cumulative number.

The data is available from 22 Jan, 2020.

Here’s a polished version suitable for a professional Kaggle dataset description:

Dataset Description

This dataset contains time-series and case-level records of the COVID-19 pandemic. The primary file is covid_19_data.csv, with supporting files for earlier records and individual-level line list data.

Files and Columns

1. covid_19_data.csv (Main File)

This is the primary dataset and contains aggregated COVID-19 statistics by location and date.

• Sno – Serial number of the record
• ObservationDate – Date of the observation (MM/DD/YYYY)
• Province/State – Province or state of the observation (may be missing for some entries)
• Country/Region – Country of the observation
• Last Update – Timestamp (UTC) when the record was last updated (not standardized, requires cleaning before use)
• Confirmed – Cumulative number of confirmed cases on that date
• Deaths – Cumulative number of deaths on that date
• Recovered – Cumulative number of recoveries on that date

2. 2019_ncov_data.csv (Legacy File)

This file contains earlier COVID-19 records. It is no longer updated and is provided only for historical reference. For current analysis, please use covid_19_data.csv.

3. COVID_open_line_list_data.csv

This file provides individual-level case information, obtained from an open data source. It includes patient demographics, travel history, and case outcomes.

4. COVID19_line_list_data.csv

Another individual-level case dataset, also obtained from public sources, with detailed patient-level information useful for micro-level epidemiological analysis.

✅ Use covid_19_data.csv for up-to-date aggregated global trends.

✅ Use the line list datasets for detailed, individual-level case analysis.

Country level datasets:

If you are interested in knowing country level data, please refer to the following Kaggle datasets:

India - https://www.kaggle.com/sudalairajkumar/covid19-in-india

South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset

Italy - https://www.kaggle.com/sudalairajkumar/covid19-in-italy

Brazil - https://www.kaggle.com/unanimad/corona-virus-brazil

USA - https://www.kaggle.com/sudalairajkumar/covid19-in-usa

Switzerland - https://www.kaggle.com/daenuprobst/covid19-cases-switzerland

Indonesia - https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

Acknowledgements :

Johns Hopkins University for making the data available for educational and academic research purposes

MoBS lab - https://www.mobs-lab.org/2019ncov.html

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....

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Contain informative data related to COVID-19 pandemic. Specially, figure out about the First Case and First Death information for every single country. The datasets mainly focus on two major fields first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent. The datasets also contain the Binary Matrix of spread chain among different country and region.

*This is not a country. This is a ship. The name of the Cruise Ship was not given from the government.
"N+": the age is not specified but greater than N
“No Trace”: some data was not found
“Unspecified”: not available from the authority
“N/A”: for “Last Visited Country(s) of Confirmed Case(s)” column, “N/A” indicates that the confirmed case(s) of those countries do not have any travel history in recent past; in “Age of First Death(s)” column “N/A” indicates that those countries do not have may death case till May 16, 2020.

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.

The dataset is a cross-sectional dataset covering social and public health data pertaining to the Covid-19 outbreak in 199 countries. The dataset was compiled from public register and other openly available sources. Data on Covid-19 cases and related fatalities is current as of medio July 2020. Data on other variables is mainly from the last three years, depending on data availability. Standardized unique unit identifiers (ISO-3166-1 Alpha-3) are included, enabling merging with other data. The dataset was assembled concurrently with a similar one on the Norwegian municipal level, as part of the project «Ressurs for studentaktiv læring i undervisning i statistisk og romlig analyse for samfunnsfag», at the Department of Social Science and The Norwegian College of Fishery Science, UiT. Dette er et tverrsnittsdatasett med samfunns- og folkehelsedata relatert til den pågående Covid-19-pandemien. Datasettet dekker 199 land. Det er satt sammen med data fra offentlige registre og andre åpent tilgjengelige kilder. Data om Covid-19-tilfeller og -dødsfall er à jour per medio juli 2020. Data på andre variabler er hovedsaklig fra de tre siste årene, avhengig av hva som var tilgjengelig på innsamlingstidspunktet. Standardiserte unike ID-variabler (ISO-3166-1 Alpha-3) er inkludert for å muliggjøre fusjonering med annen data. Datasettet ble satt sammen parallellt med et tilsvarende på kommunenivå (Norge), som en del av prosjektet «Ressurs for studentaktiv læring i undervisning i statistisk og romlig analyse for samfunnsfag» ved Institutt for samfunnsvitenskap og Norges fiskerihøgskole, UiT.

This dataset is maintained by the European Centre for Disease Prevention and Control (ECDC) and reports on the geographic distribution of COVID-19 cases worldwide. This data includes COVID-19 reported cases and deaths broken out by country. This data can be visualized via ECDC’s Situation Dashboard . More information on ECDC’s response to COVID-19 is available here . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This dataset is hosted in both the EU and US regions of BigQuery. See the links below for the appropriate dataset copy: US region EU region This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes.

The dataset contains a weekly situation update on COVID-19, the epidemiological curve and the global geographical distribution (EU/EEA and the UK, worldwide).

Since the beginning of the coronavirus pandemic, ECDC’s Epidemic Intelligence team has collected the number of COVID-19 cases and deaths, based on reports from health authorities worldwide. This comprehensive and systematic process was carried out on a daily basis until 14/12/2020. See the discontinued daily dataset: COVID-19 Coronavirus data - daily. ECDC’s decision to discontinue daily data collection is based on the fact that the daily number of cases reported or published by countries is frequently subject to retrospective corrections, delays in reporting and/or clustered reporting of data for several days. Therefore, the daily number of cases may not reflect the true number of cases at EU/EEA level at a given day of reporting. Consequently, day to day variations in the number of cases does not constitute a valid basis for policy decisions.

ECDC continues to monitor the situation. Every week between Monday and Wednesday, a team of epidemiologists screen up to 500 relevant sources to collect the latest figures for publication on Thursday. The data screening is followed by ECDC’s standard epidemic intelligence process for which every single data entry is validated and documented in an ECDC database. An extract of this database, complete with up-to-date figures and data visualisations, is then shared on the ECDC website, ensuring a maximum level of transparency.

ECDC receives regular updates from EU/EEA countries through the Early Warning and Response System (EWRS), The European Surveillance System (TESSy), the World Health Organization (WHO) and email exchanges with other international stakeholders. This information is complemented by screening up to 500 sources every day to collect COVID-19 figures from 196 countries. This includes websites of ministries of health (43% of the total number of sources), websites of public health institutes (9%), websites from other national authorities (ministries of social services and welfare, governments, prime minister cabinets, cabinets of ministries, websites on health statistics and official response teams) (6%), WHO websites and WHO situation reports (2%), and official dashboards and interactive maps from national and international institutions (10%). In addition, ECDC screens social media accounts maintained by national authorities on for example Twitter, Facebook, YouTube or Telegram accounts run by ministries of health (28%) and other official sources (e.g. official media outlets) (2%). Several media and social media sources are screened to gather additional information which can be validated with the official sources previously mentioned. Only cases and deaths reported by the national and regional competent authorities from the countries and territories listed are aggregated in our database.

Disclaimer: National updates are published at different times and in different time zones. This, and the time ECDC needs to process these data, might lead to discrepancies between the national numbers and the numbers published by ECDC. Users are advised to use all data with caution and awareness of their limitations. Data are subject to retrospective corrections; corrected datasets are released as soon as processing of updated national data has been completed.

If you reuse or enrich this dataset, please share it with us.

The data set provides numbers of COVID-19 infections in Vietnam and some neighboring countries in Asia. In the data set in Vietnam, there is a classification of the total number of new cases, new cases and new infections in the community. The number of infections in the community is the number of unknown infections. The case data is published on a daily basis and is aggregated to the time of current statistics.

Disclaimer: As of January 2025, SPC will no longer provide updated information on COVID-19 cases and deaths. The information presented on this page is for reference only. For current epidemic and emerging disease alerts in the Pacific region, please visit: https://www.spc.int/epidemics/

Statistics from SPC's Public Health Division (PHD) on the number of cases of COVID-19 and the number of deaths attributed to COVID-19 in Pacific Island Countries and Territories.

Find more Pacific data on PDH.stat.

Earlier this year, Dr. Hoffman and Dr. Fafard published a book chapter on the efficacy and legality of border closures enacted by governments in response to changing COVID-19 conditions. The authors concluded border closures are at best, regarded as powerful symbolic acts taken by governments to show they are acting forcefully, even if the actions lack an epidemiological impact and breach international law. This COVID-19 travel restriction project was developed out of a necessity and desire to further examine the empirical implications of border closures. The current dataset contains bilateral travel restriction information on the status of 179 countries between 1 January 2020 and 8 June 2020. The data was extracted from the ‘international controls’ column from the Oxford COVID-19 Government Response Tracker (OxCGRT). The data in the ‘international controls’ column outlined a country’s change in border control status, as a response to COVID-19 conditions. Accompanying source links were further verified through random selection and comparison with external news sources. Greater weight is given to official national government sources, then to provincial and municipal news-affiliated agencies. The database is presented in matrix form for each country-pair and date. Subsequently, each cell is represented by datum Xdmn and indicates the border closure status on date d by country m on country n. The coding is as follows: no border closure (code = 0), targeted border closure (= 1), and a total border closure (= 99). The dataset provides further details in the ‘notes’ column if the type of closure is a modified form of a targeted closure, either as a land or port closure, flight or visa suspension, or a re-opening of borders to select countries. Visa suspensions and closure of land borders were coded separately as de facto border closures and analyzed as targeted border closures in quantitative analyses. The file titled ‘BTR Supplementary Information’ covers a multitude of supplemental details to the database. The various tabs cover the following: 1) Codebook: variable name, format, source links, and description; 2) Sources, Access dates: dates of access for the individual source links with additional notes; 3) Country groups: breakdown of EEA, EU, SADC, Schengen groups with source links; 4) Newly added sources: for missing countries with a population greater than 1 million (meeting the inclusion criteria), relevant news sources were added for analysis; 5) Corrections: external news sources correcting for errors in the coding of international controls retrieved from the OxCGRT dataset. At the time of our study inception, there was no existing dataset which recorded the bilateral decisions of travel restrictions between countries. We hope this dataset will be useful in the study of the impact of border closures in the COVID-19 pandemic and widen the capabilities of studying border closures on a global scale, due to its interconnected nature and impact, rather than being limited in analysis to a single country or region only. Statement of contributions: Data entry and verification was performed mainly by GL, with assistance from MJP and RN. MP and IW provided further data verification on the nine countries purposively selected for the exploratory analysis of political decision-making.

The World Health Organization reported 766440796 Coronavirus Cases since the epidemic began. In addition, countries reported 6932591 Coronavirus Deaths. This dataset provides - World Coronavirus Cases- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for CORONAVIRUS VACCINATION TOTAL reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The data is in CSV format and includes all historical data on the pandemic up to 03/01/2023, following a 1-line format per country and date.

In the pre-processing of these data, missing data were checked. It was observed, for example, that the missing data referring to new_cases was where the total number of cases had not been changed and that most of the missing data related to vaccination, which actually at the beginning of the pandemic there was no data. Therefore, to solve these cases of missing data it was decided to replace the data containing “NaN” by zero. Some of these features were combined to generate new features. This process that creates new features (data) from existing data, aiming to improve the data before applying machine learning algorithms, is called feature engineering. The new features created were: - Vaccination rate (vaccination_ratio'): total number of people who received at least one dose of vaccine divided by the population at risk. This dose number was chosen because it has a higher correlation with new deaths. - Prevalence: existing cases of the disease at a given time divided by the population at risk of having the disease. Formula: COVID-19 cases ÷ Population at risk * 100. Example: 168,331 ÷ 210,000,000 * 100 = 0.08. - Incidence: new cases of the disease in a defined population during a specific period (one day, for example) divided by the population at risk. Formula: New COVID-19 cases in one day ÷ Population - Total cases * 100. Example: 5,632 ÷ 209,837,301 * 100 = 0.0026.

This dataset provides values for CORONAVIRUS CASES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Cases of COVID-19 Figure1: per country (per day after the first case and per date), Figure2: clustering of Figure1 data, Figure 3: per country/population (per day after the first case and per date), Figure 4: clustering of Figure3 data, Figure5: per country/population/land area (per day after the first case and per date), Figure 6: clustering of Figure 5 data

It has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.

I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.

The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder QUERY 1 SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Deaths>0 Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.

Query 2 SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll" FROM "public"."coronavirus_updated" WHERE Recovered>(Deaths/2) AND Confirmed>0 GROUP BY country

Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries

Query 3 SELECT country as "Countries where Coronavirus has reached" FROM "public"."coronavirus_updated" WHERE confirmed>0 GROUP BY country Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.

Query 4 SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 AND confirmed=0 GROUP BY country ORDER BY sum(suspected) DESC

Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.

Query 5 SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss", 100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage" FROM "public"."coronavirus_updated" WHERE suspected>0 AND deaths=0 GROUP BY country ORDER BY sum(suspected) DESC Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.

Data Provided by: SRK, Data Scientist at H2O.ai, Chennai, India