As of November 18, 2022, the number of confirmed COVID-19 cases in Africa amounted to around 12.7 million, which represented around two percent of the infections around the world. By the same date, coronavirus cases globally were over 640 million, deaths were over six million, while approximately 620 million people recovered from the disease. On the African continent, South Africa was the most drastically affected country, with more than 3.6 million infections.

The African continent fighting the pandemic  

The African continent first came in contact with the coronavirus pandemic on February 14, 2020, in the northernmost part, particularly Egypt. Since then, the different governments took severe restrictive measures to try to curb the spread of the disease. Moreover, the official numbers of the African continent are significantly lower than those of Europe, North America, South America, and Asia. Nevertheless, the infectious disease still managed to have its effects on several countries. South Africa had the highest number of deaths. Morocco and Tunisia, the second and third most affected in Africa, recorded 16,002 and 27,824 deaths, respectively, while Egypt registered at 24,132 as of March 02, 2022.

The light at the end of the tunnel  

Although the African countries still have a long way to fully combat the virus, vaccination programs have been rolled out in the majority of Africa. Also, according to a survey, public opinion in several African countries shows a high willingness to be vaccinated, with Ethiopia having numbers as high as 94 percent. As of March 2022, Egypt was the country administering the highest number of vaccine doses, however, Seychelles had the highest per rate per 100 people .

Dataset

This dataset was created by Malcolm Durosaye

Contents

It contains the following files:

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

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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) | | ...

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

Nearly ** percent of respondents in *** selected African countries reported their income has decreased since June 2020, following the coronavirus (COVID-19) pandemic. This was to be contrasted with only **** percent who cited an income increase in the same period, while ** percent mentioned the outbreak did not impact their income at all. In general, financial problems were the biggest challenge faced in Africa due to the COVID-19 crisis.

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa.Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package.Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette–Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden.Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.

Method

The dataset contains several confirmed COVID-19 cases, number of deaths, and death rate in six regions. The objective of the study is to compare the number of confirmed cases in Africa to other regions.

Death rate = Total number of deaths from COVID-19 divided by the Total Number of infected patients.

The study provides evidence for the country-level in six regions by the World Health Organisation's classification.

Findings

Based on the descriptive data provided above, we conclude that the lack of tourism is one of the key reasons why COVID-19 reported cases are low in Africa compared to other regions. We also justified this claim by providing evidence from the economic freedom index, which indicates that the vast majority of African countries recorded a low index for a business environment. On the other hand, we conclude that the death rate is higher in the African region compared to other regions. This points to issues concerning health-care expenditure, low capacity for testing for COVID-19, and poor infrastructure in the region.

Apart from COVID-19, there are significant pre-existing diseases, namely; Malaria, Flu, HIV/AIDS, and Ebola in the continent. This study, therefore, invites the leaders to invest massively in the health-care system, infrastructure, and human capital in order to provide a sustainable environment for today and future generations. Lastly, policy uncertainty has been a major issue in determining a sustainable development goal on the continent. This uncertainty has differentiated Africa to other regions in terms of stepping up in the time of global crisis.

Central African Republic Coronavirus COVID-19 Vaccination Total - values, historical data, forecasts and news - updated on December of 2025.

Central African Republic recorded 15367 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, Central African Republic reported 113 Coronavirus Deaths. This dataset includes a chart with historical data for Central African Republic Coronavirus Cases.

Content

This dataset contains data of weekly trend of Covid-19 in Africa (September 13- September 20, 2023)

Attribute Information

1. Country/Other
2. Cases in the last 7 days
3. Cases in the preceding 7 days
4. Weekly Case % Change
5. Cases in the last 7 days/1M pop
6. Deaths in the last 7 days
7. Deaths in the preceding 7 days
8. Weekly Death % Change
9. Deaths in the last 7 days/1M pop
10. Population

Source

Link : https://www.worldometers.info/coronavirus/weekly-trends/#weekly_table

Other Updated Covid Datasets

Link : https://www.kaggle.com/anandhuh/datasets

Thank You

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

This feature contain two layers, 1 depicts the up-to-date COVID-19 cases for Nigeria by states and the 2 shows population density of Nigeria by LGAs. These were superimposed on each other for easy comparison. Data sources include NCDC, WHO, and Africa Geoportal. The COVID-19 data is updated at least once per day, following NCDC update timeline. This layer is specifically designed for a COVID-19 monitoring dashboard found here. This layer is created and maintained by DR. NKEKI F. N. and his team (Eugene .A. Atakpiri and Akinde .N. Kolawole) to Support NCDC to fight against the spread of COVID-19 in Nigeria. This layer is opened to the public and free to share. Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com Phone: +2348117643525

Email: nkekifndidi@gmail.com Phone: +2348117643525

Email: nkekifndidi@gmail.com

Phone: +2348117643525

Email: nkekifndidi@gmail.com

Dataset contains COVID-19 information about confirmed cases, deaths, and persons under treatment at country and first administrative levels in Central and West Africa.

View daily updates and historical trends for Central African Republic Coronavirus Cases. Source: Johns Hopkins Center for Systems Science and Engineering.…

This feature contain several data layers: 1 depicts the up-to-date COVID-19 cases for Nigeria by states and the 2 shows population density of Nigeria by Local Government Areas; and these were superimposed on each other for easy comparison; 3 is a map of the statistically significant population hot spot and cold spot in Nigeria. All these datasets constitute this well presented COVID-19 dashboard for monitoring Nigeria cases. Data sources include NCDC, WHO, and Africa Geoportal. The COVID-19 data is updated at least once per day, following NCDC update timeline. This layer is created and maintained by DR. NKEKI F. N. and his team (Eugene .A. Atakpiri and Akinde .N. Kolawole) to Support NCDC to fight against the spread of COVID-19 in Nigeria. This layer is opened to the public and free to share. Contact Info: Phone: +23408063131159Email: nkekifndidi@gmail.com

Abstract This overview aimed to describe the situation of healthcare access in sub-Saharan Africa, excluding South Africa, during the COVID-19 pandemic. A PubMed® search from March 31, 2020, to August 15, 2022, selected 116 articles. Healthcare access and consequences of COVID-19 were assessed based on comparisons with months before its onset or an identical season in previous years. A general reduction of healthcare delivery, associated with the decline of care quality, and closure of many specialty services were reported. The impact was heterogeneous in space and time, with an increase in urban areas at the beginning of the pandemic (March-June 2020). The return to normalcy was gradual from the 3rd quarter of 2020 until the end of 2021. The impact of COVID-19 on the health system and its use was attributed to (a) conjunctural factors resulting from government actions to mitigate the spread of the epidemic (containment, transportation restrictions, closures of businesses, and places of entertainment or worship); (b) structural factors related to the disruption of public and private facilities and institutions, in particular, the health system; and (c) individual factors linked to the increase in costs, impoverishment of the population, and fear of contamination or stigmatization, which discouraged patients from going to health centers. They have caused considerable socio-economic damage. Several studies emphasized some adaptability of the healthcare offer and resilience of the healthcare system, despite its unpreparedness, which explained a return to normal activities as early as 2022 while the COVID-19 epidemic persisted. There appears to be a strong disproportion between the moderate incidence and severity of COVID-19 in sub-Saharan Africa, and the dramatic impact on healthcare access. Several articles make recommendations for lowering the socioeconomic consequences of future epidemics to ensure better management of health issues.

Covid-19 data at the city level in Burkina Faso - Infections (new cases, gender), Deaths, Recoveries + Urban / Rural locations.

The purpose of this repository is to collate data on the ongoing coronavirus pandemic in Africa. Our goal is to record detailed information on each reported case in every African country. We want to build a line list – a table summarizing information about people who are infected, dead, or recovered. The table for each African country would include demographic, location, and symptom (where available) information for each reported case. The data will be obtained from official sources (e.g., WHO, departments of health, CDC etc.) and unofficial sources (e.g., news). Such a dataset has many uses, including studying the spread of COVID-19 across Africa and assessing similarities and differences to what’s being observed in other regions of the world.

See the repo here https://github.com/dsfsi/covid19africa

Additional file 3. Data file for Table 1.