South African COVID-19 Provincial Data

Timeline of Confirmed Cases, Deaths, Recoveries and Testing Rates

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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) | | ...

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.

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.

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

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.

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.

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

As of June 7, 2021, a total of 57,063 COVID-19 related casualties and 1,581,540 recoveries were registered in South Africa. Western Cape registered 11,881 casualties and 279,984 recoveries in total, closely followed by Eastern Cape with only 208 casualties less and 185,995 recoveries.

Analyzing the confirmed coronavirus cases per region in South Africa, Gauteng was hit hardest. As of June 7, 2021, the region with Johannesburg as its capital registered 476,514 cases of COVID-19.

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

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.

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.

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.

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.

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.

Offering information resources to remote clients during the COVID-19 pandemic was crucial in supporting higher education activities such as teaching, learning, and research activities. The study investigated the provision of information resources to remote clients in higher education (HE) environments in South Africa during the COVID-19 pandemic. The focus of the research was on determining the information resources and services provided to remote clients by HE academic libraries; and the strategies academic librarians could employ to improve the ability of remote clients to access information resources during that period. This study aimed to propose a framework that can be used by academic libraries to provide information resources to their clients in the midst of any situational crisis (like the COVID-19 pandemic) that might intend to hinder service delivery. The objectives of this study were: to determine the information resources and services rendered to remote clients by the higher- education academic libraries; to examine the academic librarians’ support of information technology application; to determine the new library and information services introduced as part of managing COVID-19 restrictions; to identify the training strategies that higher-education academic libraries employed to improve the ability of remote clients to access information resources during the COVID-19 pandemic; to determine ways in which improved access to services and resources can be offered to remote clients in the higher- education environment; to identify the challenges encountered by academic librarians regarding the provision of information services to remote clients during the COVID-19 pandemic; and to develop a framework that will assist academic libraries operating in an online education environment to ensure that remote clients' information needs are met. The study employed a pragmatism research paradigm, using a mixed-methods approach through an explanatory sequential design to collect data using a questionnaire (primary data collection tool); and interviews to confirm, supplement and validate the questionnaire findings. The research targeted academic librarians working in HE environments in Gauteng province, South Africa. As a result, online questionnaire was emailed to the three participating libraries (the University of South Africa, the University of the Witwatersrand and the University of Johannesburg) in Gauteng Province of South Africa, whereby 82 responses were received. Based on the responses of the 82 respondents, three (3) line managers from each participating academic libraries were interviewed to confirm, validate and supplement information received from the academic librarians through the questionnaire. Gauteng province was selected as it includes the highest number of HEIs per province in South Africa. The conceptual framework anchoring the study related to the Situational Crisis Communication Theory (SCCT) and the Standards for Distance Learning Library Services (SDLLS), which were aligned to assist in understanding the provision of information resources to remote clients. Findings revealed that the COVID-19 pandemic had caused academic librarians in Gauteng province to change their traditional methods of providing information resources to remote clients. The critical responsibilities of academic librarians changed to offering online services relevant to catering to the needs of remote clients through email and other forms of social media engagement. The mode of training also changed, with undergraduate and postgraduate students having to be trained in the use of library services and resources through Zoom, Microsoft Teams, Skype, Webinar, Webcast, library websites, designated students’ email and social media. The study also revealed that limited access to data, requests from clients after library hours and the emotional pressure of supporting a vast array of clients with different needs all impacted the ability of academic librarians to offer services to remote clients during the pandemic.

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.

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.

This dataset aims to determine the views, perceptions and behaviours of Generation Z in South Africa towards health crisis communication during the COVID-19 pandemic. The data was collected through semi-structured face-to-face interviews, and the 12 participants were selected from across the gender among Gen Z at the University of Pretoria. These interviews dwelled on areas like trust in government messaging, adherence to health measures, using social and digital platforms to share information and attitudes towards contradictory or inconsistent health information.The semi-structured interview methodology combining some extent of formal questions and answers with detailed participants’ descriptions gave profound qualitative data. It was, therefore, essential to come up with questions that would bring out episodes, feelings and thought processes that people went through during the pandemic. This way of working was helpful in thoroughly investigating the topics of interest while respecting the participants’ priorities. The dataset is central to this part of the analysis because cognitive dissonance, communication strategies, and behavioural outcomes are presumably linked in this population.

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.

Household survey concerning rural South African smallholder households' experiences with the Covid-19 pandemic and its impact on farming. The data consists of responses to six open questions from 104 households, which comprise all households in one village in the Eastern Cape province. The dataset includes information about gender and age of respondents. Some responses mention religious activities and health status. The questions concern experiences of the Covid-19 pandemic and experiences of how the pandemic impacted farming. The data was collected between March and May 2022.