This dataset encompasses information on 579 confirmed COVID-19 cases in Taiwan, spanning from January 21 to November 9, 2020. The dataset includes various features such as travel history, age, gender, onset of symptoms, confirmed date, symptoms, critically ill date, recovered date, death date, and details on contact types between cases.In addition to individual case data, supplementary daily summary information is provided, sourced from the Taiwan CDC and covering the period from January 21, 2020, to May 23, 2022. This supplementary dataset furnishes population-level insights into the progression of the COVID-19 pandemic in Taiwan.Data Fields:Travel HistoryAgeGenderOnset of SymptomsConfirmed DateSymptomsCritically Ill DateRecovered DateDeath DateContact Types Between CasesTemporal Coverage:Individual Case Data: January 21, 2020, to November 9, 2020Daily Summary Data: January 21, 2020, to May 23, 2022Source:Taiwan Centers for Disease Control press release (CDC press release)United Daily News (COVID-19 Visualization)Taiwan CDC Open Data Portal, Regents of the National Center for High-performance Computing (COVID-19 Dashboard)Taiwan Centers for Disease Control open data portal (CDC open data portal)Taiwan Centers for Disease Control press conference (CDC press conference)

Taiwan MOHW: COVID-2019: Number of Cases: Confirmed: To-Date data was reported at 10,239,998.000 Person in 16 Apr 2023. This stayed constant from the previous number of 10,239,998.000 Person for 15 Apr 2023. Taiwan MOHW: COVID-2019: Number of Cases: Confirmed: To-Date data is updated daily, averaging 16,348.000 Person from Jan 2020 (Median) to 16 Apr 2023, with 1086 observations. The data reached an all-time high of 10,239,998.000 Person in 16 Apr 2023 and a record low of 1.000 Person in 23 Jan 2020. Taiwan MOHW: COVID-2019: Number of Cases: Confirmed: To-Date data remains active status in CEIC and is reported by Ministry of Health and Welfare. The data is categorized under High Frequency Database’s Disease Outbreaks – Table TW.D001: Ministry of Health and Welfare: Coronavirus Disease 2019 (COVID-2019).

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...

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.

Taiwan MOHW: COVID-2019: Number of Cases: Suspected: New Increase data was reported at 134.000 Person in 06 Jun 2020. This records a decrease from the previous number of 185.000 Person for 05 Jun 2020. Taiwan MOHW: COVID-2019: Number of Cases: Suspected: New Increase data is updated daily, averaging 272.000 Person from Jan 2020 (Median) to 06 Jun 2020, with 139 observations. The data reached an all-time high of 1,853.000 Person in 20 Apr 2020 and a record low of 0.000 Person in 17 Jan 2020. Taiwan MOHW: COVID-2019: Number of Cases: Suspected: New Increase data remains active status in CEIC and is reported by Ministry of Health and Welfare. The data is categorized under High Frequency Database’s Disease Outbreaks – Table TW.D001: Ministry of Health and Welfare: Coronavirus Disease 2019 (COVID-2019).

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.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

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.

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

WHO: COVID-2019: Number of Patients: Confirmed: New: China data was reported at 0.000 Person in 24 Dec 2023. This stayed constant from the previous number of 0.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Confirmed: New: China data is updated daily, averaging 110.000 Person from Jan 2020 (Median) to 24 Dec 2023, with 1451 observations. The data reached an all-time high of 6,966,046.000 Person in 23 Dec 2022 and a record low of 0.000 Person in 24 Dec 2023. WHO: COVID-2019: Number of Patients: Confirmed: New: China data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued). Prior to 26 Jan 2020, data were generated. Data includes new confirmed cases in Mainland China, Macau (SAR), Hong Kong (SAR) and Taiwan