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 .

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) | | ...

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.

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.

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.

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.

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

Context

This dataset contains a list of all african countries affected by covid 19, this statistics was last updated on 09 April 2020, it contains number of confirmed cases, recovered, deaths and name of countries and their regions

Content

with the help of the worldometer and the google searche engine i was able to collect a list of all african countries affected

Acknowledgements

Worldometer

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Coronavirus (COVID-19) pandemic impacted negatively the mental health of some ** percent of respondents in six selected African countries. On the other hand, ** percent mentioned a bit or much better mental health since the start of the COVID-19 crisis. Concerning physical health, up to three-quarters of the respondents reported a neutral to better condition, while only ** percent cited a deterioration.

According to the source, levels of emotional distress differed regionally, as Kenya was reported to be the worst emotionally of the six countries. This was due to the regulations and restrictions that were being carried out, as Kenyan's had to experience curfew's and a rise of cases, compared to Ivory Coast which had a relatively lower case count and fewer restrictions.

Data Set

This dataset contains the COVID19 data for some specific African countries, as sourced from one of the world's top repositories on COVID 19 (https://www.worldometers.info/coronavirus/#countries).

The raw data contains COVID19 cases, deaths, recoveries, population etc, grouped into continents and countries.

Motivation

Over the last 3 years, the whole world has been ravaged by the pandemic COVID19. Over this period, some nations have come to a halt, economic activities reduced drastically in many cities. This was accompanied by hundreds of thousands of deaths across the world.

Considering a continent as populous as Africa, we have had our own fair share of the effects of the COVID19 pandemic.

This analysis project was motivated by my desire to seek out and compare COVID 19 prevalence in some African countries between June 15th - June 27th; and also draw out insights from this analysis.

Data Cleaning

Upon collection of this data from the data source, the data was cleaned using MS Excel to search for missing values, outliers, spellings, duplicate data etc.

This cleaned data was further transformed using Power Query.

Analysis

I carried out this analysis in a bid to answer some pressing questions: 1. Which were the 10 best-performing countries (based on the least number of COVID cases) 2. Which were the 10 worst performing countries (based on the most number of COVID cases) 3. Carry out descriptive analysis for each of 1 and 2 above. 4. Compare the expository analysis between 1 and 2 stated above. 5. Create visualization for 3 and 4 above. 6. Perform a forecast of cases for each of the 10 best and worst-performing countries.

Visualization

The analysis was done by visualization and creating insights using Microsoft PowerBI Desktop.

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.

This file contains the aggregated dataset that was the basis of this paper ' Reasons for Low Burden of COVID-19 in Africa: An Explorative Cross-Sectional Analysis of Twenty-One African Countries from January to June 2020.'

Lack of financial stability was the main challenge due to the coronavirus (COVID-19) pandemic reported by respondents in *** African countries as of 2020. Some ** percent mentioned finances as the biggest distress. Roughly *********** cited staying at home due to enforced curfews and restrictions, while another ***** percent reported emotional wellbeing as the main struggle. Moreover, *** percent reported as challenge the illness of loved ones and **** percent, physical health.

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

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.

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.

Abstract The COVID-19 pandemic has caused turmoil around the world. In Africa, some similarities and differences could be observed in the nature of the outbreak and the policy responses across the continent. This article discusses the policy responses and reflects on their effectiveness as a containment strategy. We speculate on why these strategies seem to work or not, and the lessons therein. The analysis also examines the setting up of crisis teams and whether they indicate lack of trust in the existing public administration system. The article argues that though South African cases and testing diverged significantly from the rest of the continent, a wider similarity can be observed in pandemic management across the continent. The article identifies some factors including quick and early measures, recent experience managing epidemic/health crises, and a display of some form of community resilience acquired over years of living in a state of poor governance as some of the important factors in the management of the pandemic. We find there is a dearth of scholarship on crisis management in the context of public administration and suggest this should be an object of future study in the field. The use of ad-hoc crisis teams that assume emergency powers is a common practice, but there is insufficient rigorous analysis to show their effectiveness and impact on existing bureaucratic institutions.

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.

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.

The data is freely available and COVID-19 cases and deaths are changing by the day.