100+ datasets found
  1. South African COVID-19 Provincial Data

    • kaggle.com
    zip
    Updated Feb 1, 2023
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    The Devastator (2023). South African COVID-19 Provincial Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/south-african-covid-19-provincial-data
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    zip(48839 bytes)Available download formats
    Dataset updated
    Feb 1, 2023
    Authors
    The Devastator
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    South Africa
    Description

    South African COVID-19 Provincial Data

    Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

    By [source]

    About this dataset

    This dataset provides a detailed look into the ongoing COVID-19 pandemic in South Africa. It contains data on the number of confirmed cases, deaths, recoveries, and testing rates at both a provincial and national level. With this data set, users are able to gain insight into the current state and trends of the pandemic in South Africa. This provides essential information necessary to help fight the epidemic and make informed decisions surrounding its prevention. Using this set as a resource will allow users to monitor how this devastating virus has impacted communities, plans for containment and treatment strategies all while taking into account cultural, socioeconomic factors that can influence these metrics. This dataset is an invaluable tool for understanding not only South Africa’s specific current challenge with COVID-19 but is relevant on a global scale whenit comes to fighting back against this virus that continues to wreak havoc aroundthe worldl

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    How to use the dataset

    How to use This Dataset

    This Kaggle dataset provides an overview of the South African COVID-19 pandemic situation. It contains data regarding the number of confirmed cases, deaths, recoveries, and testing rates for each province at both the provincial and national level. In order to understand this dataset effectively, it is important to know what each column represents in this dataset. The following is a description of all column names that are included:

    Column Names

    • EC: Number of confirmed cases in Eastern Cape province
    • FS: Number of confirmed cases in Free State province
    • GP: Number of confirmed cases in Gauteng province
    • KZN: Number of confirmed cases in KwaZulu Natal province
    • LP: Number of confirmed cases in Limpopo province
    • MP: Number of confirmed cases in Mpumalanga Province
    • NC: Number total number orconfirmed casews in Northern Cape Province

      • NW :Number total numberurceof confirmes ed cacasesin North WestProvince

      • WC :Number totaconsfirme dcasescinWestern CapProvincee

      • UNKNOWN :Number totalnumberorconfirmesdacsesinsUnknown locations

      • Total :Totalnumberofconfrmecase sacrosseSouthAfrica

      • Source :Sourecodataset fedzile_Dbi ejweleputswaMangaungXharie thabo_MofutsanyanaRecoveriesDeathsYYMMDD

    Research Ideas

    • Creating an interactive map to show the spread of COVID-19 over time, with up date information about confirmed cases, deaths, recoveries and testing rates for each province or district.
    • Constructing a machine learning model to predict the likely number of future cases in each province based on previous data activities.
    • Comparing different districts and provinces within South Africa and drawing out trends among them with comparative graphical representations or independent analyses

    Acknowledgements

    If you use this dataset in your research, please credit the original authors. Data Source

    License

    License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

    Columns

    File: covid19za_provincial_cumulative_timeline_recoveries.csv | Column name | Description | |:--------------|:---------------------------------------------------------------| | date | Date of the data entry. (Date) | | YYYYMMDD | Date in YYYYMMDD format. (String) | | EC | Number of confirmed cases in Eastern Cape Province. (Integer) | | FS | Number of confirmed cases in Free State Province. (Integer) | | GP | Number of confirmed cases in Gauteng Province. (Integer) | | KZN | Number of confirmed cases in Kwazulu Natal Province. (Integer) | | LP | Number of confirmed cases in Limpopo Province. (Integer) | | MP | Number of confirmed cases in Mpumalanga Province. (Integer) | | NC | Number of confirmed cases in Northern Cape Province. (Integer) | | ...

  2. Coronavirus (COVID-19) cases in South Africa as of March 06, 2022, by region...

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Coronavirus (COVID-19) cases in South Africa as of March 06, 2022, by region [Dataset]. https://www.statista.com/statistics/1108127/coronavirus-cases-in-south-africa-by-region/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 7, 2022
    Area covered
    South Africa
    Description

    As of March 06, 2022, overall coronavirus (COVID-19) cases in South Africa reached its highest at 3,684,319 infections. It was also the largest volume of confirmed cases compared to other African countries. Regionally, Gauteng (Johannesburg) was hit hardest and registered 1,196,591 cases, whereas KwaZulu-Natal (Durban) and Western Cape (Cape Town) counted 653,945 and 642,153 coronavirus cases, respectively. In total 23,245,373 tests were conducted in the country. Total recoveries amounted to 3,560,217. On December 12, 2021, the highest daily increase in cases was recorded in South Africa.

    Economic impact on businesses in South Africa

    The coronavirus pandemic is not only causing a health crisis but influences the economy heavily as well. According to a survey on the financial impact of COVID-19 on various industries in South Africa, 89.6 percent of businesses indicated to see a turnover below the normal range. Mining and quarrying industry was hit hardest with nearly 95 percent of all companies seeing a decrease in turnover, whereas the largest share of businesses experiencing no economic impact are working within the real estate sector and other business services. As a response to the coronavirus, laying off workers in the short term was the most common workforce measure that businesses in South Africa implemented. 36.4 percent of businesses indicated to have laid of staff temporarily, and roughly 25 percent decreased the working hours. Approximately 20 percent of the surveyed companies, on the other hand, said no measures have been taken.

    Business survivability without any revenue

    Due to the measures taken by the government to prevent the coronavirus from spreading too fast, many businesses had to close its doors temporarily. However, if the coronavirus would leave them without any form of revenue for up to three months, eight out of ten businesses in South Africa predicted (in April 2020) they will go bankrupt. Just 6.7 percent said to survive for longer than three months without any turnover.

  3. Coronavirus (COVID-19) death rate in South Africa as of November 16, by age

    • statista.com
    Updated Jun 3, 2025
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    Statista (2025). Coronavirus (COVID-19) death rate in South Africa as of November 16, by age [Dataset]. https://www.statista.com/statistics/1127280/coronavirus-covid-19-deaths-by-age-distribution-south-africa/
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    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 16, 2020
    Area covered
    South Africa
    Description

    As of November 16, 2020, a total of 17.577 COVID-19 related casualties were registered in South Africa. Some 14.1 percent of the deaths fell within the age group of 60 to 64 years with, whereas 12.6 percent of whom were aged 55 to 59 passed away due to the diseases caused by the coronavirus. Confirmed coronavirus cases per region in South Africa illustrated Gauteng was hit hardest. As of January 15, 2021, the region with Johannesburg as its capital registered 350,976 individuals with COVID-19 , whereas KwaZulu-Natal and Western Cape had dealt with less cases.

  4. T

    South Africa Coronavirus COVID-19 Vaccination Rate

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Apr 21, 2021
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    TRADING ECONOMICS (2021). South Africa Coronavirus COVID-19 Vaccination Rate [Dataset]. https://tradingeconomics.com/south-africa/coronavirus-vaccination-rate
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    json, xml, excel, csvAvailable download formats
    Dataset updated
    Apr 21, 2021
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2021 - May 1, 2023
    Area covered
    South Africa
    Description

    The number of COVID-19 vaccination doses administered per 100 people in South Africa rose to 65 as of Oct 27 2023. This dataset includes a chart with historical data for South Africa Coronavirus Vaccination Rate.

  5. T

    South Africa Coronavirus COVID-19 Deaths

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Mar 5, 2020
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    TRADING ECONOMICS (2020). South Africa Coronavirus COVID-19 Deaths [Dataset]. https://tradingeconomics.com/south-africa/coronavirus-deaths
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    xml, json, excel, csvAvailable download formats
    Dataset updated
    Mar 5, 2020
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 4, 2020 - May 17, 2023
    Area covered
    South Africa
    Description

    South Africa recorded 102595 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, South Africa reported 4072533 Coronavirus Cases. This dataset includes a chart with historical data for South Africa Coronavirus Deaths.

  6. f

    South Africa’s COVID-19 lockdown levels, period and summary of restrictions....

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Oct 28, 2021
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    Dekel, Bianca; Abrahams, Naeemah (2021). South Africa’s COVID-19 lockdown levels, period and summary of restrictions. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000825908
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    Dataset updated
    Oct 28, 2021
    Authors
    Dekel, Bianca; Abrahams, Naeemah
    Area covered
    South Africa
    Description

    South Africa’s COVID-19 lockdown levels, period and summary of restrictions.

  7. South Africa COVID-19 Twitter Posts Dataset

    • kaggle.com
    zip
    Updated Jul 4, 2022
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    Blessing Ogbuokiri (2022). South Africa COVID-19 Twitter Posts Dataset [Dataset]. https://www.kaggle.com/datasets/ogbuokiriblessing/tweetdatasa
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    zip(1713167 bytes)Available download formats
    Dataset updated
    Jul 4, 2022
    Authors
    Blessing Ogbuokiri
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    This dataset contains Twitter posts containing daily updates of location-based COVID–19 vaccine-related tweets from January 2021 to August 2021.

    With an existing Twitter account, we applied for Developer Access and were granted access to Twitter Academic Researcher API which allows for over 10 million tweets per month. Then, we created an application to generate the API credentials (access tokens) from Twitter. The access token was used in Python (v3.6) script to authenticate and establish a connection to the Twitter database. To get goe-tagged vaccine-related tweets, we used the python script we developed to perform a historical search (archive search) of vaccine-related keywords with place country South Africa (ZA). By goe-tagged tweets, we refer to Twitter posts with a know location. These vaccine-related keywords include but are not limited to the vaccine, anti-vaxxer, vaccination, AstraZeneca, Oxford-AstraZeneca, IChooseVaccination, VaccineToSaveSouthAfrica, JohnsonJohnson, and Pfizer. The keywords were selected from the trending topic during the period of discussion. A complete list of the keywords is shown below:

    Oxford-AstraZeneca, AstraZeneca, JohnsonJohnson, Vaccine, BioNTech, anti-vaccine, jab, Vaccination, Covax, Vaccine Rollout, Sputnik, VaccineToSaveSouthAfrica, IChooseVaccination, TeachersVaccine, AstraZeneca vaccine, Pfizer, J & J, Johonson & Johnson, Moderna, VaccinesWork, VacciNation, Vaccine, Steriod, COVIDvaccine, covax, VaccineEquity, VaccineReady, Jab OR PfizerGang, Scamdemic, Plandemic, Scaredemic, COVID-19, coronavirus, SARS-CoV-2, anti-vaxxers, jab, Pfizer, BioNTech, JJ, Vaccine, JohnsonJohnson Vaccine, Vaccine Rollout, J & J, Sputnik, COVAX, CoronaVac

    The preferred language of the tweet is English.

  8. Coronavirus (COVID-19) daily cases in South Africa as of March 6, 2022

    • statista.com
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    Statista, Coronavirus (COVID-19) daily cases in South Africa as of March 6, 2022 [Dataset]. https://www.statista.com/statistics/1107993/coronavirus-cases-in-south-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    South Africa
    Description

    On March 6, 2021, confirmed cases of coronavirus COVID-19 on a single day in South Africa amounted to 8,078. Total cases reached 3,684,319, which is the highest number of confirmed cases compared to other African countries. As of the same date, there were 99,543 casualties and 3,560,217 recoveries in the country.

    The most affected country in the continent

    Since the outbreak of the COVID-19 pandemic in the continent, starting in Egypt on February 14, 2020, South Africa has been harshly affected, quickly becoming the worst-hit country in Africa. Gauteng, the province with Johannesburg as its capital, was the most affected regionally with over 1.2 million cases as of early March, 2022. As well as its health effects, the pandemic had a strong impact on businesses with nine out of ten businesses operating in different industries claiming that the turnover was below the normal range they used to receive as of April 2020.

    Vaccination efforts

    Countries around the world are racing to get their populations vaccinated to be able to go back to normal. As the fourth wave hits South Africa in December 2021, and as the different stronger variants emerge, the country is also trying to vaccinate its population faster to minimize the severe health effects. After facing a harsh start to its vaccination program due to the ineffectiveness of the AstraZeneca vaccine to the Beta variant also known as B.1.351, on May 17, 2021, South Africa began the second phase of its vaccination program, opening it for people who are 60 and over. Previously, the so-called Sisonke Program was rolled out as the first phase to ensure the vaccination of the health workers protecting them from the pandemic. As of March 6, 2022, Gauteng was the region with the highest number of vaccinated individuals followed by Western Cape with around 9.02 million and five million inoculations, respectively.

  9. Latest Coronavirus COVID-19 figures for South Africa

    • covid19-today.pages.dev
    json
    Updated Jul 30, 2025
    + more versions
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    Worldometers (2025). Latest Coronavirus COVID-19 figures for South Africa [Dataset]. https://covid19-today.pages.dev/countries/south-africa/
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    jsonAvailable download formats
    Dataset updated
    Jul 30, 2025
    Dataset provided by
    Worldometershttps://dadax.com/
    CSSE at JHU
    License

    https://github.com/disease-sh/API/blob/master/LICENSEhttps://github.com/disease-sh/API/blob/master/LICENSE

    Area covered
    South Africa
    Description

    In past 24 hours, South Africa, Africa had N/A new cases, N/A deaths and N/A recoveries.

  10. Coronavirus disease (COVID-19) case data - South Africa

    • zenodo.org
    • data.niaid.nih.gov
    bin, csv
    Updated Feb 21, 2023
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    Vukosi Marivate; Vukosi Marivate; Alta de Waal; Herkulaas Combrink; Ofentswe Lebogo; Shivan Moodley; Nompumelelo Mtsweni; Vuthlari Rikhotso; Jay Welsh; S'busiso Mkhondwane; Alta de Waal; Herkulaas Combrink; Ofentswe Lebogo; Shivan Moodley; Nompumelelo Mtsweni; Vuthlari Rikhotso; Jay Welsh; S'busiso Mkhondwane (2023). Coronavirus disease (COVID-19) case data - South Africa [Dataset]. http://doi.org/10.5281/zenodo.3732419
    Explore at:
    csv, binAvailable download formats
    Dataset updated
    Feb 21, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Vukosi Marivate; Vukosi Marivate; Alta de Waal; Herkulaas Combrink; Ofentswe Lebogo; Shivan Moodley; Nompumelelo Mtsweni; Vuthlari Rikhotso; Jay Welsh; S'busiso Mkhondwane; Alta de Waal; Herkulaas Combrink; Ofentswe Lebogo; Shivan Moodley; Nompumelelo Mtsweni; Vuthlari Rikhotso; Jay Welsh; S'busiso Mkhondwane
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    COVID 19 Data for South Africa created, maintained and hosted by DSFSI research group at the University of Pretoria

    Disclaimer: We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know.

    See original GitHub repo for detailed information https://github.com/dsfsi/covid19za

  11. u

    COVID-19 Vaccine Survey, Survey 2 2022 - South Africa

    • datafirst.uct.ac.za
    Updated May 17, 2022
    + more versions
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    Southern Africa Labour and Development Research Unit (2022). COVID-19 Vaccine Survey, Survey 2 2022 - South Africa [Dataset]. https://www.datafirst.uct.ac.za/dataportal/index.php/catalog/899
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    Dataset updated
    May 17, 2022
    Dataset authored and provided by
    Southern Africa Labour and Development Research Unit
    Time period covered
    2022
    Area covered
    South Africa
    Description

    Abstract

    The COVID-19 Vaccine Survey (CVACS) is a South African national panel study of individuals initially unvaccinated against COVID-19. CVACS is implemented by the Southern Africa Labour and Development Research Unit (SALDRU) based at the University of Cape Town. The same respondents are interviewed twice, a few months apart, in 2021 and then 2022, to gather information about their attitudes, beliefs and intentions regarding COVID-19 vaccination. The purpose of CVACS is to collect high quality, timely, and relevant information on facilitators and barriers to COVID-19 vaccine uptake - including vaccine hesitancy and access constraints - to contribute to the development of data-driven campaigns and programmes to increase COVID-19 vaccination uptake in South Africa. In comparison to Survey 1, Survey 2 collected data on unvaccinated and vaccinated respondents. Final data files are: Unvaccinated (as was in S1) Vaccinated (New to S2) derived (As in S1) Link_File (New in S2 - this links the panel)

    Geographic coverage

    CVACS was not designed to be, and should not be used as a prevalence study. The data cannot be considered to be nationally representative of all unvaccinated individuals in South Africa.

    Analysis unit

    Households and individuals

    Kind of data

    Sample survey data

    Sampling procedure

    CVACS Survey 1 was obtained from a stratified sample drawn from the GeoTerraImage (GTI) 2021 sampling frame (https://geoterraimage.com/), using individuals aged eighteen and older. The sample was primarily stratified across the following categories: province, population group, geographic area type (metro, non-metro urban, non-metro rural) and the neighbourhood lifestyle index (NLI), in groups of NLI 1-2, NLI 3-4, and NLI 5-10. Age categories defined according to the COVID-19 vaccination age groups (18-34, 35-49, 50-59, 60+), and gender were used as further explicit stratification variables. A credit bureau database was linked to this database at the enumeration area level, including individuals who had applied for credit, regardless of the outcome, and individuals who have had a credit check.

    The CVACS Sample in Survey 2 included individuals from Survey 1 who were re-interviewed, who fell into two categories: vaccinated between Survey 1 and 2, or those remaining unvaccinated. In order to realise an unvaccinated sample of similar size to Survey 1, a top-up sample of unvaccinated individuals was interviewed. These individuals were drawn from the same sampling frame as Survey 1. Younger and female respondents were less likely to be re-interviewed in Survey 2. The full Survey 2 unvaccinated sample is more skewed to the younger age categories, due to higher vaccination rates among the elderly precluding many from inclusion into the study.

    Mode of data collection

    Computer Assisted Telephone Interview

    Research instrument

    Data was collected for Survey 2 with two questionnaires, one for vaccinated and one for unvaccinated respondents. CVACS used computer-assisted telephone interviews (CATI). The CVACS questionnaires were translated into all South African languages and interviews were conducted in the preferred language of the respondent. Most of the survey questions collected individual-level data, with some household level data also collected through the individual questionnaire.

  12. S

    South Africa New Covid deaths per million people, March, 2023 - data, chart...

    • theglobaleconomy.com
    csv, excel, xml
    + more versions
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    Globalen LLC, South Africa New Covid deaths per million people, March, 2023 - data, chart | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/South-Africa/covid_new_deaths_per_million/
    Explore at:
    excel, csv, xmlAvailable download formats
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 29, 2020 - Mar 31, 2023
    Area covered
    South Africa
    Description

    New Covid deaths per million people in South Africa, March, 2023 The most recent value is 0 new Covid deaths per million people as of March 2023, compared to the previous value of 0 new Covid deaths per million people. Historically, the average for South Africa from February 2020 to March 2023 is 45 new Covid deaths per million people. The minimum of 0 new Covid deaths per million people was recorded in February 2020, while the maximum of 266 new Covid deaths per million people was reached in January 2021. | TheGlobalEconomy.com

  13. f

    South Africa Social Listening Reports for COVID-19 vaccine hesitancy

    • ufs.figshare.com
    Updated Feb 22, 2023
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    Herkulaas Combrink (2023). South Africa Social Listening Reports for COVID-19 vaccine hesitancy [Dataset]. http://doi.org/10.38140/ufs.22128401.v1
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    Dataset updated
    Feb 22, 2023
    Dataset provided by
    University of the Free State
    Authors
    Herkulaas Combrink
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    Collection of data reports and weekly reports that assisted the COVID-19 outbreak in terms of data, meta data and trends that aided in the fight against public healthcare interventions such as public vaccine programmes.

  14. S

    South Africa New Covid cases per month, March, 2023 - data, chart |...

    • theglobaleconomy.com
    csv, excel, xml
    Updated Mar 15, 2023
    + more versions
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    Globalen LLC (2023). South Africa New Covid cases per month, March, 2023 - data, chart | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/South-Africa/covid_new_cases/
    Explore at:
    xml, csv, excelAvailable download formats
    Dataset updated
    Mar 15, 2023
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 29, 2020 - Mar 31, 2023
    Area covered
    South Africa
    Description

    New Covid cases per month in South Africa, March, 2023 The most recent value is 10955 new Covid cases as of March 2023, an increase compared to the previous value of 5922 new Covid cases. Historically, the average for South Africa from February 2020 to March 2023 is 107172 new Covid cases. The minimum of 0 new Covid cases was recorded in February 2020, while the maximum of 482853 new Covid cases was reached in December 2021. | TheGlobalEconomy.com

  15. M

    Project Tycho Dataset; Counts of COVID-19 Reported In SOUTH AFRICA:...

    • catalog.midasnetwork.us
    • tycho.pitt.edu
    • +1more
    + more versions
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    MIDAS Coordination Center, Project Tycho Dataset; Counts of COVID-19 Reported In SOUTH AFRICA: 2020-2021 [Dataset]. http://doi.org/10.25337/T7/ptycho.v2.0/ZA.840539006
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    Dataset provided by
    MIDAS COORDINATION CENTER
    Authors
    MIDAS Coordination Center
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Time period covered
    Jan 3, 2020 - Jul 31, 2021
    Area covered
    Country
    Variables measured
    Viruses, disease, COVID-19, pathogen, mortality data, Population count, infectious disease, viral Infectious disease, vaccine-preventable Disease, viral respiratory tract infection, and 1 more
    Dataset funded by
    National Institute of General Medical Sciences
    Description

    This Project Tycho dataset includes a CSV file with COVID-19 data reported in SOUTH AFRICA: 2020-01-03 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.

  16. covid19-za-cumulativetotals

    • kaggle.com
    zip
    Updated Apr 3, 2020
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    Zakia Salod (2020). covid19-za-cumulativetotals [Dataset]. https://www.kaggle.com/datasets/zakias/covid19zacumulativetotals
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    zip(635 bytes)Available download formats
    Dataset updated
    Apr 3, 2020
    Authors
    Zakia Salod
    Description

    Context

    In view of the recent Coronavirus disease (COVID-19) that is widespread across the globe, this dataset presents South Africa's total cumulative positive COVID-19 cases, per day, from 5th of March 2020 upto and including 2nd of April 2020.

    Content

    The covid19-za-cumulativetotals.csv dataset was prepared by referring to the statistics of the daily press releases posted on South Africa's COVID-19 online resource and news portal. The dataset shows the cumalative total positive COVID-19 cases per day in South Africa, since the first reported case in the country, which was on the 5th of March 2020, upto and including the 2nd of April 2020 (which are the latest reported results, as at the writing of this notebook). There is therefore, 27 rows of data. Of these 27 rows, the provincial totals for Date = 27 March 2020 was not published by the South Africa's COVID-19 online resource and news portal. Consequently, there is missing data for the provincial columns for this date in the dataset. There are 12 columns: Date, EC, FS, GP, KZN, LP, MP, NC, NW, WC, Unknown and Total.

    Inspiration

    Possible suggestion(s) for future work: - Try to improve the current Prophet forecast model in the notebook provided. - Try to create a forecast model using the ARIMA forecasting method. - Possibly, try to keep the dataset updated by monitoring the data published on the source and updating the dataset here, accordingly. This will assist by providing more historic data, and, consequently, also has the probability of gaining more accurate forecasts.

  17. South Africa Covid-19 dataset

    • kaggle.com
    zip
    Updated Aug 15, 2020
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    Simanga Mchunu (2020). South Africa Covid-19 dataset [Dataset]. https://www.kaggle.com/datasets/simacoder/south-africa-covid19-dataset
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    zip(6898 bytes)Available download formats
    Dataset updated
    Aug 15, 2020
    Authors
    Simanga Mchunu
    Area covered
    South Africa
    Description

    Dataset

    This dataset was created by Simanga Mchunu

    Contents

  18. H

    National and Subnational Estimates of the Covid 19 Reproduction Number (R)...

    • dataverse.harvard.edu
    Updated Mar 23, 2022
    + more versions
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    Sam Abbott; Christopher Bennett; Joe Hickson; Jamie Allen; Katharine Sherratt; Sebastian Funk (2022). National and Subnational Estimates of the Covid 19 Reproduction Number (R) for South Africa Based on Deaths [Dataset]. http://doi.org/10.7910/DVN/4Q5CG9
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 23, 2022
    Dataset provided by
    Harvard Dataverse
    Authors
    Sam Abbott; Christopher Bennett; Joe Hickson; Jamie Allen; Katharine Sherratt; Sebastian Funk
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    South Africa, South Africa, South Africa, South Africa, South Africa, South Africa, South Africa, South Africa, South Africa
    Description

    Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally in South Africa.

  19. Data_Sheet_1_Modeling the positive testing rate of COVID-19 in South Africa...

    • frontiersin.figshare.com
    pdf
    Updated May 31, 2023
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    Olajumoke Evangelina Owokotomo; Samuel Manda; Jürgen Cleasen; Adetayo Kasim; Rudradev Sengupta; Rahul Shome; Soumya Subhra Paria; Tarylee Reddy; Ziv Shkedy (2023). Data_Sheet_1_Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.pdf [Dataset]. http://doi.org/10.3389/fpubh.2023.979230.s001
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    pdfAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Olajumoke Evangelina Owokotomo; Samuel Manda; Jürgen Cleasen; Adetayo Kasim; Rudradev Sengupta; Rahul Shome; Soumya Subhra Paria; Tarylee Reddy; Ziv Shkedy
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Africa
    Description

    Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.

  20. S

    South Africa Total fully vaccinated people against Covid, March, 2023 -...

    • theglobaleconomy.com
    csv, excel, xml
    Updated Mar 15, 2023
    + more versions
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    Globalen LLC (2023). South Africa Total fully vaccinated people against Covid, March, 2023 - data, chart | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/South-Africa/people_fully_vaccinated_covid/
    Explore at:
    xml, excel, csvAvailable download formats
    Dataset updated
    Mar 15, 2023
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Feb 28, 2021 - Mar 31, 2023
    Area covered
    South Africa
    Description

    Total fully vaccinated people against Covid in South Africa, March, 2023 The most recent value is 21000000 total fully-vaccinated people as of March 2023, no change compared to the previous value of 21000000 total fully-vaccinated people. Historically, the average for South Africa from February 2021 to March 2023 is 13715508 total fully-vaccinated people. The minimum of 70527 total fully-vaccinated people was recorded in February 2021, while the maximum of 21000000 total fully-vaccinated people was reached in December 2022. | TheGlobalEconomy.com

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The Devastator (2023). South African COVID-19 Provincial Data [Dataset]. https://www.kaggle.com/datasets/thedevastator/south-african-covid-19-provincial-data
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South African COVID-19 Provincial Data

Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

Explore at:
zip(48839 bytes)Available download formats
Dataset updated
Feb 1, 2023
Authors
The Devastator
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Area covered
South Africa
Description

South African COVID-19 Provincial Data

Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

By [source]

About this dataset

This dataset provides a detailed look into the ongoing COVID-19 pandemic in South Africa. It contains data on the number of confirmed cases, deaths, recoveries, and testing rates at both a provincial and national level. With this data set, users are able to gain insight into the current state and trends of the pandemic in South Africa. This provides essential information necessary to help fight the epidemic and make informed decisions surrounding its prevention. Using this set as a resource will allow users to monitor how this devastating virus has impacted communities, plans for containment and treatment strategies all while taking into account cultural, socioeconomic factors that can influence these metrics. This dataset is an invaluable tool for understanding not only South Africa’s specific current challenge with COVID-19 but is relevant on a global scale whenit comes to fighting back against this virus that continues to wreak havoc aroundthe worldl

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For more datasets, click here.

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How to use the dataset

How to use This Dataset

This Kaggle dataset provides an overview of the South African COVID-19 pandemic situation. It contains data regarding the number of confirmed cases, deaths, recoveries, and testing rates for each province at both the provincial and national level. In order to understand this dataset effectively, it is important to know what each column represents in this dataset. The following is a description of all column names that are included:

Column Names

  • EC: Number of confirmed cases in Eastern Cape province
  • FS: Number of confirmed cases in Free State province
  • GP: Number of confirmed cases in Gauteng province
  • KZN: Number of confirmed cases in KwaZulu Natal province
  • LP: Number of confirmed cases in Limpopo province
  • MP: Number of confirmed cases in Mpumalanga Province
  • NC: Number total number orconfirmed casews in Northern Cape Province

    • NW :Number total numberurceof confirmes ed cacasesin North WestProvince

    • WC :Number totaconsfirme dcasescinWestern CapProvincee

    • UNKNOWN :Number totalnumberorconfirmesdacsesinsUnknown locations

    • Total :Totalnumberofconfrmecase sacrosseSouthAfrica

    • Source :Sourecodataset fedzile_Dbi ejweleputswaMangaungXharie thabo_MofutsanyanaRecoveriesDeathsYYMMDD

Research Ideas

  • Creating an interactive map to show the spread of COVID-19 over time, with up date information about confirmed cases, deaths, recoveries and testing rates for each province or district.
  • Constructing a machine learning model to predict the likely number of future cases in each province based on previous data activities.
  • Comparing different districts and provinces within South Africa and drawing out trends among them with comparative graphical representations or independent analyses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: covid19za_provincial_cumulative_timeline_recoveries.csv | Column name | Description | |:--------------|:---------------------------------------------------------------| | date | Date of the data entry. (Date) | | YYYYMMDD | Date in YYYYMMDD format. (String) | | EC | Number of confirmed cases in Eastern Cape Province. (Integer) | | FS | Number of confirmed cases in Free State Province. (Integer) | | GP | Number of confirmed cases in Gauteng Province. (Integer) | | KZN | Number of confirmed cases in Kwazulu Natal Province. (Integer) | | LP | Number of confirmed cases in Limpopo Province. (Integer) | | MP | Number of confirmed cases in Mpumalanga Province. (Integer) | | NC | Number of confirmed cases in Northern Cape Province. (Integer) | | ...

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