100+ datasets found
  1. Cumulative coronavirus cases in Africa 2022, by country

    • statista.com
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    Statista, Cumulative coronavirus cases in Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1170463/coronavirus-cases-in-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 18, 2022
    Area covered
    Africa
    Description

    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 .

  2. Coronavirus deaths in Africa 2022, by country

    • statista.com
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    Statista, Coronavirus deaths in Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1170530/coronavirus-deaths-in-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 23, 2022
    Area covered
    Africa
    Description

    As of November 18, 2022, the overall deaths due to coronavirus (COVID-19) in Africa reached 257,984. South Africa recorded the highest number of casualties. With over 100,000 deaths, the country accounted for roughly 40 percent of the total. Tunisia was the second most affected on the continent, as the virus made almost 30,000 victims in the nation, around 11 percent of the overall deaths in Africa. Egypt accounted for around 10 percent of the casualties on the continent, with 24,600 victims. By the same date, Africa had recorded more than 12 million cases of COVID-19.

  3. Why has the number of COVID-19 confirmed cases in Africa been insignificant...

    • zenodo.org
    • data.niaid.nih.gov
    bin
    Updated May 13, 2020
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    Azeem Oluwaseyi Zubair; Muritala Olaniyi Zubair; Abdul-Rahim Abdul Samad; Azeem Oluwaseyi Zubair; Muritala Olaniyi Zubair; Abdul-Rahim Abdul Samad (2020). Why has the number of COVID-19 confirmed cases in Africa been insignificant compared to other regions? A descriptive analysis [Dataset]. http://doi.org/10.5281/zenodo.3788733
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    binAvailable download formats
    Dataset updated
    May 13, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Azeem Oluwaseyi Zubair; Muritala Olaniyi Zubair; Abdul-Rahim Abdul Samad; Azeem Oluwaseyi Zubair; Muritala Olaniyi Zubair; Abdul-Rahim Abdul Samad
    License

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

    Description

    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.

  4. T

    CORONAVIRUS DEATHS by Country in AFRICA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Apr 18, 2020
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    TRADING ECONOMICS (2020). CORONAVIRUS DEATHS by Country in AFRICA [Dataset]. https://tradingeconomics.com/country-list/coronavirus-deaths?continent=africa
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    csv, xml, json, excelAvailable download formats
    Dataset updated
    Apr 18, 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
    2025
    Area covered
    Africa
    Description

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

  5. p

    Latest Coronavirus COVID-19 figures for Africa

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

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

    Area covered
    Africa
    Description

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

  6. 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) | | ...

  7. Table_1_The Determinants of the Low COVID-19 Transmission and Mortality...

    • frontiersin.figshare.com
    docx
    Updated May 30, 2023
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    Yagai Bouba; Emmanuel Kagning Tsinda; Maxime Descartes Mbogning Fonkou; Gideon Sadikiel Mmbando; Nicola Luigi Bragazzi; Jude Dzevela Kong (2023). Table_1_The Determinants of the Low COVID-19 Transmission and Mortality Rates in Africa: A Cross-Country Analysis.docx [Dataset]. http://doi.org/10.3389/fpubh.2021.751197.s001
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Yagai Bouba; Emmanuel Kagning Tsinda; Maxime Descartes Mbogning Fonkou; Gideon Sadikiel Mmbando; Nicola Luigi Bragazzi; Jude Dzevela Kong
    License

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

    Description

    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.

  8. COVID-19 vaccination rate in Africa 2023, by country

    • statista.com
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    Statista, COVID-19 vaccination rate in Africa 2023, by country [Dataset]. https://www.statista.com/statistics/1221298/covid-19-vaccination-rate-in-african-countries/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 15, 2023
    Area covered
    Africa
    Description

    As of March 15, 2023, Seychelles was the African country with the highest coronavirus (COVID-19) vaccination rate, with around 205 doses administered per 100 individuals. Mauritius and Rwanda followed with 201 and 190 doses per 100 people, respectively. Ranking fourth, Morocco had a vaccination rate of approximately 148 doses per 100 people, registering the third-highest number of inoculations after Egypt and Nigeria. In South Africa, the most affected country on the continent, the vaccination rate instead reached around 64 per 100 population.

    How did Africa obtain the vaccines?

    Vaccines in Africa were obtained in different ways. African nations both purchased new doses and received them from other countries. At the beginning of the vaccination campaigns, donations came from all over the world, such as China, the United Arab Emirates, India, and Russia. The United Nations-led COVAX initiative provided Oxford/AstraZeneca and Pfizer/BioNTech doses to several African countries. Within this program, the continent received nearly 270 million doses as of January 2022. Moreover, the vaccination campaign has also been an occasion for intra-African solidarity. Senegal has, for instance, donated vaccines to the Gambia, while in January 2021, Algeria announced that it would have shared its supply with Tunisia.

    COVID-19 impact on the African economy

    The spread of COVID-19 negatively affected socio-economic growth in Africa, with the continent’s Gross Domestic Product (GDP) contracting significantly in 2020. Specifically, Southern Africa experienced the sharpest decline, at minus six percent, followed by North Africa at minus 1.7 percent. Most of Africa’s key economic sectors were hit by the pandemic. The drop in global oil prices led to a crisis in the oil and gas sector. Nigeria, the continent’s leading oil-exporting country, witnessed a considerable decrease in crude oil trade in 2020. Moreover, the shrinking number of international tourist arrivals determined a loss of over 12 million jobs in Africa’s travel and tourism sector. Society has also been substantially affected by COVID-19 on the poorest continent in the world, and the number of people living in extreme poverty was estimated to increase by around 30 million in 2020.

  9. T

    CORONAVIRUS CASES by Country in AFRICA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Mar 27, 2020
    + more versions
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    TRADING ECONOMICS (2020). CORONAVIRUS CASES by Country in AFRICA [Dataset]. https://tradingeconomics.com/country-list/coronavirus-cases?continent=africa
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    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Mar 27, 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
    2025
    Area covered
    Africa
    Description

    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.

  10. f

    Data Sheet 1_Spatiotemporal prevalence of COVID-19 and SARS-CoV-2 variants...

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Feb 20, 2025
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    Li-Ping Gao; Can-Jun Zheng; Ting-Ting Tian; Alie Brima Tia; Michael K. Abdulai; Kang Xiao; Cao Chen; Dong-Lin Liang; Qi Shi; Zhi-Guo Liu; Xiao-Ping Dong (2025). Data Sheet 1_Spatiotemporal prevalence of COVID-19 and SARS-CoV-2 variants in Africa.docx [Dataset]. http://doi.org/10.3389/fpubh.2025.1526727.s001
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    docxAvailable download formats
    Dataset updated
    Feb 20, 2025
    Dataset provided by
    Frontiers
    Authors
    Li-Ping Gao; Can-Jun Zheng; Ting-Ting Tian; Alie Brima Tia; Michael K. Abdulai; Kang Xiao; Cao Chen; Dong-Lin Liang; Qi Shi; Zhi-Guo Liu; Xiao-Ping Dong
    License

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

    Area covered
    Africa
    Description

    IntroductionThe coronavirus disease 2019 (COVID-19) pandemic has caused significant public health and socioeconomic crises across Africa; however, the prevalent patterns of COVID-19 and the circulating characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in the continent remain insufficiently documented.MethodsIn this study, national data on case numbers, infection incidences, mortality rates, the circulation of SARS-CoV-2 variants, and key health indexes were collected from various official and professional sources between January 2020 and December 2023 were analyzed with SaTScan and geographically weighted regression (GWR).ResultsThe prevalent profiles and circulating features of SARS-CoV-2 across the African continent, including its five regions and all African countries, were analyzed. Four major waves of the epidemic were observed. The first wave was closely associated with the introduction of the early SARS-CoV-2 strain while the subsequent waves were linked to the emergence of specific variants, including variants of concern (VOCs) Alpha, Beta, variants of interest (VOIs) Eta (second wave), VOC Delta (third wave), and VOC Omicron (fourth wave). SaTScan analysis identified four large spatiotemporal clusters that affected various countries. A significant number of countries (50 out of 56) reported their first cases during February 2020 and March 2020, predominantly involving individuals with confirmed cross-continental travel histories, mainly from Europe. In total, 12 distinct SARS-CoV-2 VOCs and VOIs were identified, with the most prevalent being VOCs Omicron, Delta, Beta, Alpha, and VOI Eta. Unlike the dominance of VOC Delta during the third wave and Omicron during the fourth wave, VOC Alpha was relatively rare in the Southern regions but more common in the other four regions. At the same time, Beta predominated in the Southern region and Eta in the Western region during the second wave. Additionally, relatively higher COVID-19 case incidences and mortalities were reported in the Southern and Northern African regions. Spearman rank correlation and geographically weighted regression (GWR) analyses of COVID-19 incidences against health indexes in 52 African countries indicate that countries with higher national health expenditures and better personnel indexes tended to report higher case incidences.DiscussionThis study offers a detailed overview of the COVID-19 pandemic in Africa. Strengthening the capacity of health institutions across African countries is essential for the timely detection of new SARS-CoV-2 variants and, consequently, for preparedness against future COVID-19 pandemics and other potentially infectious disease outbreaks.

  11. Data from: COVID-19 impacts on healthcare access in sub-Saharan Africa: an...

    • scielo.figshare.com
    jpeg
    Updated Jul 4, 2023
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    Jean-Philippe Chippaux (2023). COVID-19 impacts on healthcare access in sub-Saharan Africa: an overview [Dataset]. http://doi.org/10.6084/m9.figshare.23622706.v1
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    jpegAvailable download formats
    Dataset updated
    Jul 4, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Jean-Philippe Chippaux
    License

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

    Area covered
    Sub-Saharan Africa
    Description

    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.

  12. Z

    Africa COVID-19 Community Vulnerability Index (CCVI)

    • data.niaid.nih.gov
    Updated May 5, 2021
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    Anubhuti Mishra; Peter Smittenaar; Grace K. Charles; Nicholas Stewart; Staci Sutermaster; Valerie C. Valerio; Victor Ohuruogu; Oliver Chinganya; Sofia Braunstein; Rahul Joseph; Mokshada Jain; Olufunke Fasawe; Owens Wiwa; Solomon Zewdu; Ghina R. Mumtaz; Laith J. Abu-Raddad; Sema K. Sgaier (2021). Africa COVID-19 Community Vulnerability Index (CCVI) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4725491
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    Dataset updated
    May 5, 2021
    Dataset provided by
    Clinton Health Access Initiative Inc., Abuja, Nigeria
    Department of Epidemiology and Population Health, American University of Beirut, Beirut, Lebanon
    Global Partnership for Sustainable Development Data, Lagos, Nigeria
    African Centre for Statistics, United Nations Economic and Social Commission for Africa (UNECA), Addis Ababa, Ethiopia
    Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar
    Surgo Ventures
    Africa office, Health and Nutrition, Bill and Melinda Gates Foundation, Johannesburg, South Africa
    Authors
    Anubhuti Mishra; Peter Smittenaar; Grace K. Charles; Nicholas Stewart; Staci Sutermaster; Valerie C. Valerio; Victor Ohuruogu; Oliver Chinganya; Sofia Braunstein; Rahul Joseph; Mokshada Jain; Olufunke Fasawe; Owens Wiwa; Solomon Zewdu; Ghina R. Mumtaz; Laith J. Abu-Raddad; Sema K. Sgaier
    License

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

    Description

    Surgo Ventures' Africa CCVI ranks 756 regions across 48 African countries on their vulnerability—or their ability to mitigate, treat, and delay transmission of the coronavirus. Vulnerability is assessed based on many factors grouped into seven themes: socioeconomic status, population density, access to transportation and housing; epidemiological factors; health system factors; fragility; and age. The index reflects risk factors for COVID-19, both in terms of clinical outcomes and socioeconomic impact.

    The Africa CCVI is the only index to measure vulnerability to COVID-19 within most countries in Africa at this level of detail. The index is modular to reflect the reality that vulnerability is a multi-dimensional construct, and two regions could be vulnerable for very different reasons. This allows stakeholders to customize pandemic responses informed by vulnerability on each dimension. For example, policymakers can identify areas for scaling up COVID-19 testing that are more vulnerable on theme two - population density - or direct community health workers or mobile health units to areas that are vulnerable due to weak health systems infrastructure. The modularity of the Africa CCVI can help governments design lean and precise responses for subnational regions during each phase of the pandemic.

    Data files:

    Africa_CCVI_subnational_zenodo.csv: Africa CCVI and seven themes' scores for 756 administrative level-1 regions across 48 countries

    Africa_CCVI_country_zenodo.csv: Africa CCVI and seven themes scores across 36 countries (12 countries excluded as country-specific data sources were used for them)

    DHS_raw_indicators_Zenodo.csv: this CSV contains indicator data for 36 countries, data was primarily sourced from Demographic and Health Surveys (DHS) in addition to other sources (listed in accvi-data-sources.xlsx)

    non_DHS_raw_indicators_Zenodo.csv: 12 countries that did not have a recent DHS, so we used country-specific surveys, MICS UNICEF, and other sources (listed in accvi-data-sources.xlsx)

    accvi-data-sources.xlsx: data sources used for ACCVI indicators

    zenodo_data_dictionary.csv: names and definitions of variables used in data files

  13. Coronavirus deaths in East Africa 2022, by country

    • statista.com
    Updated Apr 25, 2014
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    Statista (2014). Coronavirus deaths in East Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1175313/coronavirus-deaths-by-country-in-east-africa/
    Explore at:
    Dataset updated
    Apr 25, 2014
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 1, 2022
    Area covered
    Africa
    Description

    As of June 1, 2022, East Africa registered over 26,000 deaths due to the coronavirus (COVID-19). The number of cases in the region surpassed 1.34 million. Ethiopia was the most affected country in East Africa, accounting for some 7,500 casualties. Kenya followed, with over 5,600 deaths caused by the disease.

  14. 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

  15. Additional file 3 of Importations of COVID-19 into African countries and...

    • springernature.figshare.com
    xlsx
    Updated Jun 1, 2023
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    Haoyang Sun; Borame L. Dickens; Alex R. Cook; Hannah E. Clapham (2023). Additional file 3 of Importations of COVID-19 into African countries and risk of onward spread [Dataset]. http://doi.org/10.6084/m9.figshare.12805987.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Haoyang Sun; Borame L. Dickens; Alex R. Cook; Hannah E. Clapham
    License

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

    Area covered
    Africa
    Description

    Additional file 3. Data file for Table 1.

  16. The African region covid-19 dataset

    • kaggle.com
    zip
    Updated Apr 10, 2020
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    Derek Kweku (2020). The African region covid-19 dataset [Dataset]. https://www.kaggle.com/derek560/the-african-region-covid19-dataset
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    zip(56052 bytes)Available download formats
    Dataset updated
    Apr 10, 2020
    Authors
    Derek Kweku
    Description

    Context

    There's a story behind every dataset and here's your opportunity to share yours.

    As the spread of the novel covid-19 continues to run into countries it is important for us to keep records of every Information on it. Therefore, this dataset is built basically to cover the update from Africa.

    Content

    What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. It contains Information on the dates the cases were recorded across Africa. Detailing the death, confirmed and recovery cases in each country.

    Acknowledgements

    We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

    Ethical AI Club John Hopkins University Runmila Institute WHO CDC Ghana Health Service

    Inspiration

    Your data will be in front of the world's largest data science community. What questions do you want to see answered? We should be able to see contributors answering questions about how Africa should prepare and put in the right measures to contain the spread. A better understanding from the Data scientists.

  17. Data_Sheet_1_Spatio-temporal evolution of the COVID-19 across African...

    • frontiersin.figshare.com
    pdf
    Updated Jun 1, 2023
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    Bechir Naffeti; Sebastien Bourdin; Walid Ben Aribi; Amira Kebir; Slimane Ben Miled (2023). Data_Sheet_1_Spatio-temporal evolution of the COVID-19 across African countries.PDF [Dataset]. http://doi.org/10.3389/fpubh.2022.1039925.s001
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    pdfAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Bechir Naffeti; Sebastien Bourdin; Walid Ben Aribi; Amira Kebir; Slimane Ben Miled
    License

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

    Area covered
    Africa
    Description

    The aim of this study is to make a comparative study on the reproduction number R0 computed at the beginning of each wave for African countries and to understand the reasons for the disparities between them. The study covers the two first years of the COVID-19 pandemic and for 30 African countries. It links pandemic variables, reproduction number R0, demographic variable, median age of the population, economic variables, GDP and CHE per capita, and climatic variables, mean temperature at the beginning of each waves. The results show that the diffusion of COVID-19 in Africa was heterogeneous even between geographical proximal countries. The difference of the basic reproduction number R0 values is very large between countries and is significantly correlated with economic and climatic variables GDP and temperature and to a less extent with the mean age of the population.

  18. Z

    Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa

    • data.niaid.nih.gov
    Updated Apr 20, 2020
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    Marivate, Vukosi; Nsoesie, Elaine; Esube Bekele; Africa open COVID-19 data working group (2020). Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3732979
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    Dataset updated
    Apr 20, 2020
    Dataset provided by
    Boston University
    University of Pretoria, CSIR
    US Naval Research Laboratory
    Authors
    Marivate, Vukosi; Nsoesie, Elaine; Esube Bekele; Africa open COVID-19 data working group
    License

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

    Area covered
    Africa
    Description

    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

  19. T

    CORONAVIRUS VACCINATION RATE by Country in AFRICA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Nov 1, 2025
    + more versions
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    TRADING ECONOMICS (2025). CORONAVIRUS VACCINATION RATE by Country in AFRICA [Dataset]. https://tradingeconomics.com/country-list/coronavirus-vaccination-rate?continent=africa
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    excel, json, xml, csvAvailable download formats
    Dataset updated
    Nov 1, 2025
    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
    2025
    Area covered
    Africa
    Description

    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.

  20. Coronavirus deaths in Northern Africa as of November 22, 2021, by country

    • statista.com
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    Statista, Coronavirus deaths in Northern Africa as of November 22, 2021, by country [Dataset]. https://www.statista.com/statistics/1189443/coronavirus-deaths-by-country-in-north-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 22, 2021
    Area covered
    Africa
    Description

    As of November 22, 2021, the cumulative number of coronavirus (COVID-19) related deaths in Northern Africa amounted to 74,622, which represented 32.97 percent of the overall deaths in the African continent. In the northern region, Tunisia reported 25,347 casualties due to the pandemic. Although Morocco was the most affected in the number of COVID-19 cases, the country came third registering 14,764 deaths, while Egypt recorded 19,991 deaths.

Share
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Statista, Cumulative coronavirus cases in Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1170463/coronavirus-cases-in-africa/
Organization logo

Cumulative coronavirus cases in Africa 2022, by country

Explore at:
37 scholarly articles cite this dataset (View in Google Scholar)
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Nov 18, 2022
Area covered
Africa
Description

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 .

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