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....

As of December 12, 2022, Hong Kong had the highest rate of coronavirus (COVID-19) cases reported in the previous seven days in the Asia-Pacific region, around 1.19 thousand cases per 100 thousand people. South Korea followed with 825 cases per 100,000 people in the past seven days.

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.

Column Description

Main file in this dataset is covid_19_data.csv and the detailed descriptions are below.

covid_19_data.csv

• Sno - Serial number
• ObservationDate - Date of the observation in MM/DD/YYYY
• Province/State - Province or state of the observation (Could be empty when missing)
• Country/Region - Country of observation
• Last Update - Time in UTC at which the row is updated for the given province or country. (Not standardised and so please clean before using it)
• Confirmed - Cumulative number of confirmed cases till that date
• Deaths - Cumulative number of of deaths till that date
• Recovered - Cumulative number of recovered cases till that date

Apart from that these two files have individual level information

COVID_open_line_list_data.csv This file is originally obtained from this link

COVID19_line_list_data.csv This files is originally obtained from this link

Country level datasets If you are interested in knowing country level data, please refer to the following Kaggle datasets: South Korea - https://www.kaggle.com/kimjihoo/coronavirusdataset Italy -
https://www.kaggle.com/sudalairajkumar/covid19-in-italy

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.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus

Inspiration

Some useful insi...

As of June 6, 2022, the novel coronavirus SARS-CoV-2 that originated in Wuhan, the capital of Hubei province in China, had infected over 2.1 million people and killed 14,612 in the country. Hong Kong is currently the region with the highest active cases in China.

From Wuhan to the rest of China

In late December 2019, health authorities in Wuhan detected several pneumonia cases of unknown cause. Most of these patients had links to the Huanan Seafood Market. With Chinese New Year approaching, millions of Chinese migrant workers travelled back to their hometowns for the celebration. Before the start of the travel ban on January 23, around five million people had left Wuhan. By the end of January, the number of infections had surged to over ten thousand. The death toll from the virus exceeded that of the SARS outbreak a few days later. On February 12, thousands more cases were confirmed in Wuhan after an improvement to the diagnosis method, resulting in another sudden surge of confirmed cases. On March 31, 2020, the National Health Commission (NHC) in China announced that it would begin reporting the infection number of symptom-free individuals who tested positive for coronavirus. On April 17, 2020, health authorities in Wuhan revised its death toll, adding 50 percent more fatalities. After quarantine measures were implemented, the country reported no new local coronavirus COVID-19 transmissions for the first time on March 18, 2020.

The overloaded healthcare system

In Wuhan, 28 hospitals were designated to treat coronavirus patients, but the outbreak continued to test China’s disease control system and most of the hospitals were soon fully occupied. To combat the virus, the government announced plans to build a new hospital swiftly. On February 3, 2020, Huoshenshan Hospital was opened to provide an additional 1,300 beds. Due to an extreme shortage of health-care professionals in Wuhan, thousands of medical staff from all over China came voluntarily to the epicenter to offer their support. After no new deaths reported for first time, China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country.

I continue to work on improving this Dataset and will upload as soon as I have an improved version of it. I don't own this dataset, I have merely tried to enrich the data that is gathered from multiple sources by John Hopkins CSSE.

Context

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

Content

This file consists of incidents reported from across the world Jan 22 onwards. Incidents are categorized into Confirmed, Deaths and Recovered. Country/Region and/or Province/State information is available. Geo-coordinates are available but these are missing for countries like China

Acknowledgements

This data belongs to John Hopkins CSSE which they gathered from multiple sources. Below is from JHU Github account, please read before using the dataset.

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

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

Visual Dashboard (mobile): http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61

Lancet Article: An interactive web-based dashboard to track COVID-19 in real time

Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Data Sources:

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

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

Contact Us:

Email: jhusystems@gmail.com

Terms of Use:

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

Inspiration

COVID-19 is perhaps the biggest historical event of our lifetime with the kind of destruction and disruption it has already caused to the people around the world. I wanted to build a dashboard summarizing the events from beginning to date and that's the reason I worked on combining all the daily reports into one file.

Context

This dataset is a curated version of 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE.

Content

This is the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).

Data processing

From the original source of the data, we perform the following operations: * Concatenate the daily reports files (https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports)
* Add daily update date (as Date field)

• Fix country names duplicates. Multiple countries have duplicate names, ex: South Korea, Republic of Korea, Korea, South. data_df.loc[data_df['Country/Region']==' Azerbaijan', 'Country/Region'] = 'Azerbaijan' data_df.loc[data_df['Country/Region']=='Czechia', 'Country/Region'] = 'Czech Republic' data_df.loc[data_df['Country/Region']=="Cote d'Ivoire", 'Country/Region'] = 'Ivory Coast' data_df.loc[data_df['Country/Region']=='Iran (Islamic Republic of)', 'Country/Region'] = 'Iran' data_df.loc[data_df['Country/Region']=='Hong Kong SAR', 'Country/Region'] = 'Hong Kong' data_df.loc[data_df['Country/Region']=='Holy See', 'Country/Region'] = 'Vatican City' data_df.loc[data_df['Country/Region']=='Macao SAR', 'Country/Region'] = 'Macau' data_df.loc[data_df['Country/Region']=='Mainland China', 'Country/Region'] = 'China' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Korea, South', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Ireland', 'Country/Region'] = 'Ireland' data_df.loc[data_df['Country/Region']=='Republic of Korea', 'Country/Region'] = 'South Korea' data_df.loc[data_df['Country/Region']=='Republic of Moldova', 'Country/Region'] = 'Moldova' data_df.loc[data_df['Country/Region']=='Republic of the Congo', 'Country/Region'] = 'Congo (Brazzaville)' data_df.loc[data_df['Country/Region']=='Taiwan*', 'Country/Region'] = 'Taiwan' data_df.loc[data_df['Country/Region']=='The Gambia', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='Gambia, The', 'Country/Region'] = 'Gambia' data_df.loc[data_df['Country/Region']=='UK', 'Country/Region'] = 'United Kingdom' data_df.loc[data_df['Country/Region']=='Viet Nam', 'Country/Region'] = 'Vietnam'
• Replace missing data in Lat/Long for Province/State and/or Country/Region

data_df = pd.DataFrame()
for file in tqdm(os.listdir(db_source)):
  try:
    crt_date, crt_ext = crt_file = file.split(".")
    if(crt_ext == "csv"):
      crt_date_df = pd.read_csv(os.path.join(db_source, file))
      crt_date_df['date_str'] = crt_date
      crt_date_df['date'] = crt_date_df['date_str'].apply(lambda x: datetime.strptime(x, "%m-%d-%Y"))
      data_df = data_df.append(crt_date_df)
  except:
    pass

province_state = data_df['Province/State'].unique()

for ps in province_state:

  data_df.loc[(data_df['Province/State']==ps) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Province/State']==ps), 'Latitude'].median()
  
  data_df.loc[(data_df['Province/State']==ps) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Province/State']==ps), 'Longitude'].median() 

country_region = data_df['Country/Region'].unique()

for cr in country_region:

  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Latitude'].isna()), 'Latitude'] =\
        data_df.loc[(~data_df['Latitude'].isna()) & \
              (data_df['Country/Region']==cr), 'Latitude'].median()
  
  data_df.loc[(data_df['Country/Region']==cr) & (data_df['Longitude'].isna()), 'Longitude'] =\
      data_df.loc[(~data_df['Longitude'].isna()) & \
            (data_df['Country/Region']==cr), 'Longitude'].median() 

Acknowledgements

Data source: https://github.com/CSSEGISandData/COVID-19

Inspiration

Represent the geographical data distribution of 2019-nCoV spread. Represent time series with Confirmed, Recovered, Deaths cases. Analyse the mortality. Try to forecast the evolution of cases. Compare the spread of Coronavirus for different countries, with different policies for social isolation, closing schools, stopping international travels.

The dataset contains COVID-19 cases, recovered and deaths, daily reported by prefecture level from the website DXY.cn which collect public data from National Health Commission, provincial health commission, provincial governments, Hong Kong official channel, Macao official channel and Taiwan official channel. The data are extracted in a CSV format everyday at 16:00 EST. The name of the prefecture, province and country are translated by using Google Translate.

The new SARS-like coronavirus has spread around China since its outbreak in Wuhan - the capital of central China’s Hubei province. As of June 7, 2022, there were 2,785,848 active cases with symptoms in Greater China. The pandemic has caused a significant impact in the country's economy.

Fast-moving epidemic

In Wuhan, over 3.8 thousand deaths were registered in the heart of the outbreak. The total infection number surged on February 12, 2020 in Hubei province. After a change in official methodology for diagnosing and counting cases, thousands of new cases were added to the total figure. There is little knowledge about how the virus that originated from animals transferred to humans. While human-to-human transmission has been confirmed, other transmission routes through aerosol and fecal-oral are also possible. The deaths from the current virus COVID-19 (formally known as 2019-nCoV) has surpassed the toll from the SARS epidemic of 2002 and 2003.

Key moments in the Chinese coronavirus timeline

The doctor in Wuhan, Dr. Li Wenliang, who first warned about the new strain of coronavirus was silenced by the police. It was announced on February 7, 2020 that he died from the effects of the coronavirus infection. His death triggered a national backlash over freedom of speech on Chinese social media. On March 18, 2020, the Chinese government reported no new domestically transmissions for the first time after a series of quarantine and social distancing measures had been implemented. On March 31, 2020, the National Health Commission (NHC) in China started reporting the infection number of symptom-free individuals who tested positive for coronavirus. Before that, asymptomatic cases had not been included in the Chinese official count. China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country. On April 17, 2020, health authorities in Wuhan revised its death toll, adding some 1,290 fatalities in its total count.

Mexico SALUD: COVID-19: Confirmed Cases: To Date: Jalisco data was reported at 277,335.000 Person in 17 Aug 2022. This records an increase from the previous number of 276,948.000 Person for 16 Aug 2022. Mexico SALUD: COVID-19: Confirmed Cases: To Date: Jalisco data is updated daily, averaging 86,289.000 Person from Feb 2020 (Median) to 17 Aug 2022, with 902 observations. The data reached an all-time high of 277,335.000 Person in 17 Aug 2022 and a record low of 0.000 Person in 13 Mar 2020. Mexico SALUD: COVID-19: Confirmed Cases: To Date: Jalisco data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table MX.D001: Ministry of Health: Coronavirus Disease 2019 (COVID-2019) (Discontinued). Current day data is released daily between 7PM and 11PM Mexico City Time. Weekend data are updated following Monday morning, Hong Kong Time. Number of Confirmed Cases are based on the state where it is reported.

MDS: COVID-19: Confirmed Cases: To Date: South data was reported at 8,307,028.000 Person in 03 May 2025. This records an increase from the previous number of 8,306,862.000 Person for 02 May 2025. MDS: COVID-19: Confirmed Cases: To Date: South data is updated daily, averaging 7,357,982.000 Person from Feb 2020 (Median) to 03 May 2025, with 1895 observations. The data reached an all-time high of 8,307,028.000 Person in 03 May 2025 and a record low of 0.000 Person in 09 Mar 2020. MDS: COVID-19: Confirmed Cases: To Date: South data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA001: Disease Outbreaks: COVID-19: Confirmed Cases. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

Hong Kong SAR (China) CHP: COVID-2019: NoP: Suspect data was reported at 12.000 Person in 10 May 2020. This records an increase from the previous number of 7.000 Person for 09 May 2020. Hong Kong SAR (China) CHP: COVID-2019: NoP: Suspect data is updated daily, averaging 39.000 Person from Dec 2019 (Median) to 10 May 2020, with 132 observations. The data reached an all-time high of 326.000 Person in 27 Mar 2020 and a record low of 0.000 Person in 01 Jan 2020. Hong Kong SAR (China) CHP: COVID-2019: NoP: Suspect data remains active status in CEIC and is reported by Centre for Health Protection. The data is categorized under High Frequency Database’s Disease Outbreaks – Table HK.D001: Centre for Health Protection: Coronavirus Disease 2019 (COVID-2019). Criteria: a. been to Wuhan in the past 14 days b. presented with fever, respiratory infection or pneumonia symptoms c. Inpatient pneumonia cases with travel history to Mainland China within 14 days before onset of symptoms 2. Data prior to Jan. 26, 2020 was sourced from Centre for Health Protection and it has been ceased. Latest data is source from Hong Kong Information Statistics Department.

Distribution of sample weights in the donor pool for synthetic Hong Kong.

Updated @ March 13, 2020

ver 0.0.12

• added additional data since last update

ver 0.0.11

• added Lat / Lon / Country Code / Region / Country Flag (image URL)
• cleaned timestamp format

Context

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. (source: CDC)

In this dataset, you will have minutes-level timesereis 2019-nCoV reporting data which can help capture the outbreak trend more accurately than the daily data.

Content

• Available File Format

  • CSV

• Time Window

  • ~0.5 Hour (may have some gaps in early mornings)

• Date Range

  • 2020-01-22 ~ 2020-03-11 (actively updating)

• Geographic Region

  • The Greater China Area (China Mainland, Hong Kong, Macau, and Taiwan)
  • The worldwide impacted areas

• Columns

  • province: String, the reported provinces / areas (not listed if no cases reported).
  • country: the country name.
  • latitude: the latitude data of the country.
  • longitude : the longitude data of the country.
  • confirmed_cases: Int, the number of confirmed cases of the place at the reporting time.
  • deaths: Int, the number of deaths of the place at the reporting time.
  • recovered, Int, the number of recovered patients at the reporting time.
  • update_time: Timestamp (CST timezone), the reporting timestamp.
  • data_source: String, the raw data sources (currently bno and dxy).
  • country_code: String, this is the country code.
  • region: String, this is the region (Europe, Asia etc.).
  • country_flag: String, this is the URL for country flag image.

Acknowledgements

Special thanks to @globalcitizen who has scrapped the raw data files from multiple public sources.

Repo here ==> https://github.com/globalcitizen/2019-wuhan-coronavirus-data

• Original Data Sources:
  • DXY.cn(Chinese medical information site)
  • bnownews.com
  • JHU

Please contact me if you consider this dataset violate your copyright and I'm happy to remove it.

Inspiration

• To the whole Kaggle community:
  • From this provided dataset, how do you see the outbreak trend of 2019-nCoV different from the historical coronavirus outbreaks (e.g. SARS, MERS)?
  • What additional dataset do you require so you can get better insights about 2019-nCov?

UPVOTES ==> Let more people know this dataset and use it to gather insights.

Appreciate it Thanks

Pre-intervention balance of predictor variables in Panel A (June 20 as the intervention point).

MDS: COVID-19: Confirmed Cases: To Date: Central West data was reported at 4,610,386.000 Person in 03 May 2025. This records an increase from the previous number of 4,610,224.000 Person for 02 May 2025. MDS: COVID-19: Confirmed Cases: To Date: Central West data is updated daily, averaging 3,953,610.000 Person from Feb 2020 (Median) to 03 May 2025, with 1895 observations. The data reached an all-time high of 4,610,386.000 Person in 03 May 2025 and a record low of 0.000 Person in 06 Mar 2020. MDS: COVID-19: Confirmed Cases: To Date: Central West data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA001: Disease Outbreaks: COVID-19: Confirmed Cases. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

In 2024, about ****** people passed away in Hong Kong. The figure of deaths was higher than normal in 2022 due to the COVID-19 pandemic.

MDS: COVID-19: Confirmed Cases: To Date: Central West: MG do Sul data was reported at 639,309.000 Person in 03 May 2025. This stayed constant from the previous number of 639,309.000 Person for 02 May 2025. MDS: COVID-19: Confirmed Cases: To Date: Central West: MG do Sul data is updated daily, averaging 580,707.000 Person from Feb 2020 (Median) to 03 May 2025, with 1895 observations. The data reached an all-time high of 639,759.000 Person in 11 Apr 2025 and a record low of 0.000 Person in 15 Mar 2020. MDS: COVID-19: Confirmed Cases: To Date: Central West: MG do Sul data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA001: Disease Outbreaks: COVID-19: Confirmed Cases. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.

Data-set of the paper Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks for replication purpose of the Data Editor of AEJMacro. Detailed information on the data-set is in the readme file in the public repository openicpsr-175241 (under review). We study the dynamic interaction between Covid-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for 1-2 months. These shocks are the main drivers of the pandemic, explaining between 20-60% of the average and historical variability in mobility, cases, and deaths worldwide. The policy tradeoff associated to non-pharmaceutical interventions is 1pp less economic mobility per day for 8% fewer deaths after three months. The panel data-set contains the main data to perform the analysis in the paper. It contains dailiy data for (in sheets) Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Saudi Arabia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom and United States. Included variables are: Confirmed Cases, Total Deaths, Days Last Reported Case, Total Tests, School Closing, Workplace Closing, Cancel Public Events, Restrictions Gatherings, Close Public Transport, Stay at Home Requirements, Restrictions Internal Movement, International Travel Controls, Income Support, Debt/Contract Relief, Fiscal Measures, International Support, Public Information Campaigns, Testing Policy, Contact Tracing, Healthcare Emergency Investment, Investment Vaccines, Stringency Index, Small Cap, Large Cap, Government Benchmarks 3 Month, Government Benchmarks 1 Year, Government Benchmarks 2 Year, Government Benchmarks 5 Year, Government Benchmarks 10 Year, FX Indices Broad, FX Indices Narrow, Mobility Retail Mobility Grocery, Mobility Parks, Mobility Transit Stations Mobility Workplaces, Mobility Residential. Period: 30.12.2016 to 31.08.2020. All data are downloaded from Macrobond. We do not have the permission to redistribute part of the data (daily financial series).

Argentina MSCOVID-19: Confirmed Cases: New: Chaco data was reported at 0.000 Person in 23 Apr 2022. This stayed constant from the previous number of 0.000 Person for 22 Apr 2022. Argentina MSCOVID-19: Confirmed Cases: New: Chaco data is updated daily, averaging 99.000 Person from Mar 2020 (Median) to 23 Apr 2022, with 776 observations. The data reached an all-time high of 3,203.000 Person in 12 Jan 2022 and a record low of 0.000 Person in 23 Apr 2022. Argentina MSCOVID-19: Confirmed Cases: New: Chaco data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table AR.D001: Ministry of Health: Coronavirus Disease 2019 (COVID-2019). Current day data is released daily between 9PM and 11PM Argentina Time. Weekend data are updated following Monday morning, Hong Kong Time. Previous day data revision is released daily between 7AM and 9AM Argentina Time.

MDS: COVID-19: Confirmed Cases: To Date: Northeast: Sergipe data was reported at 368,806.000 Person in 26 Apr 2025. This records an increase from the previous number of 368,801.000 Person for 25 Apr 2025. MDS: COVID-19: Confirmed Cases: To Date: Northeast: Sergipe data is updated daily, averaging 342,881.000 Person from Feb 2020 (Median) to 26 Apr 2025, with 1888 observations. The data reached an all-time high of 368,806.000 Person in 26 Apr 2025 and a record low of 0.000 Person in 14 Mar 2020. MDS: COVID-19: Confirmed Cases: To Date: Northeast: Sergipe data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table BR.HLA001: Disease Outbreaks: COVID-19: Confirmed Cases. Current day data is released daily between 6PM and 7PM Brazil Time. Weekend data are updated following Monday morning, Hong Kong Time.