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.

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.

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.

South Africa faced its first coronavirus (COVID-19) casualty on March 27, 2020. Ever since, the country has registered roughly **** thousand civilians who lost their lives to the pandemic. Moreover, throughout the outbreak, the largest daily death recorded was on January 19, 2021, when *** people were involved. As of October 24, 2021, South Africa was the most affected country on the continent, with over **** million cases of infections.

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.

Context

Late in December 2019, the World Health Organisation (WHO) China Country Office obtained information about severe pneumonia of an unknown cause, detected in the city of Wuhan in Hubei province, China. This later turned out to be the novel coronavirus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) of the coronavirus family. The disease causes respiratory illness characterized by primary symptoms like cough, fever, and in more acute cases, difficulty in breathing. WHO later declared COVID-19 as a Pandemic because of its fast rate of spread across the Globe with over 2.8 Million confirmed cases and over 197,000 deaths as of April 25, 2020. The African continent started confirming its first cases of COVID-19 in late January and early February of 2020 in some of its countries. The disease has since spread across 52 of the 54 African countries with over 29,000 confirmed cases and over 1,300 deaths as of April 25, 2020.

Content

The COVID-19 Africa dataset contains daily level information about the COVID-19 cases in Africa since January 27th, 2020. It is a time-series data and the number of cases on any given day is cumulative. I extracted the data from the World COVID-19 dataset which was uploaded on Kaggle. The R script that I used to prepare this dataset is also available on my Github repository. The original datasets can also be found on the John Hopkins University Github repository. I will be updating the COVID-19 Africa dataset on a daily basis, with every update from John Hopkins University.

Field description

• ObservationDate: Date of observation in YY/MM/DD
• Country: name of an African country
• Confirmed: the number of COVID-19 confirmed cases
• Deaths: the number of deaths from COVID-19
• Recovered: the number of recovered cases
• Active: the number of people still infected with COVID-19 Note: Active = Confirmed - (Deaths + Recovered)

Acknowledgements

1. John Hopkins University for making COVID-19 datasets available to the public: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports
2. John Hopkins University COVID-19 Dashboard: https://coronavirus.jhu.edu/map.html
3. SRk for uploading a global COVID-19 dataset on Kaggle: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset#covid_19_data.csv
4. United Nations Department of General Assembly and Conference Management: https://www.un.org/depts/DGACM/RegionalGroups.shtml
5. African Arguments: https://africanarguments.org/2020/04/07/coronavirus-in-africa-tracker-how-many-cases-and-where-latest/)
6. Wallpapercave.com: https://wallpapercave.com/covid-19-wallpapers

Inspiration

Possible Insights 1. The current number of COVID-19 cases in Africa 2. The current number of COVID-19 cases -19 cases by country 3. The number of COVID-19 cases in Africa / African country(s) by April 30, 2020 (Any future date)

Context

This dataset was collected from data received via this APi.

Content

“[Recovered cases are a] more important metric to track than Confirmed cases.”— Researchers for the University of Virginia’s COVID-19 dashboard

If the number of total cases were accurately known for every country then the number of cases per million people would be a good indicator as to how well various countries are handling the pandemic.

Countries:

Datasets contend data about covid_19 from 232 countries - Afghanistan - Albania - Algeria - Andorra - Angola - Anguilla - Antigua-and-Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia-and-Herzegovina - Botswana - Brazil - British-Virgin-Islands - Brunei - Bulgaria - Burkina-Faso - Burundi - Cabo-Verde - Cambodia - Cameroon - Canada - CAR - Caribbean-Netherlands - Cayman-Islands - Chad - Channel-Islands - Chile - China - Colombia - Comoros - Congo - Cook-Islands - Costa-Rica - Croatia - Cuba - Curaçao - Cyprus - Czechia - Denmark - Diamond-Princess - Diamond-Princess- - Djibouti - Dominica - Dominican-Republic - DRC - Ecuador - Egypt - El-Salvador - Equatorial-Guinea - Eritrea - Estonia - Eswatini - Ethiopia - Faeroe-Islands - Falkland-Islands - Fiji - Finland - France - French-Guiana - French-Polynesia - Gabon - Gambia - Georgia - Germany - Ghana - Gibraltar - Greece - Greenland - Grenada - Guadeloupe - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong-Kong - Hungary - Iceland - India - Indonesia - Iran - Iraq - Ireland - Isle-of-Man - Israel - Italy - Ivory-Coast - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Kuwait - Kyrgyzstan - Laos - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall-Islands - Martinique - Mauritania - Mauritius - Mayotte - Mexico - Micronesia - Moldova - Monaco - Mongolia - Montenegro - Montserrat - Morocco - Mozambique - MS-Zaandam - MS-Zaandam- - Myanmar - Namibia - Nepal - Netherlands - New-Caledonia - New-Zealand - Nicaragua - Niger - Nigeria - Niue - North-Macedonia - Norway - Oman - Pakistan - Palau - Palestine - Panama - Papua-New-Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto-Rico - Qatar - Réunion - Romania - Russia - Rwanda - S-Korea - Saint-Helena - Saint-Kitts-and-Nevis - Saint-Lucia - Saint-Martin - Saint-Pierre-Miquelon - Samoa - San-Marino - Sao-Tome-and-Principe - Saudi-Arabia - Senegal - Serbia - Seychelles - Sierra-Leone - Singapore - Sint-Maarten - Slovakia - Slovenia - Solomon-Islands - Somalia - South-Africa - South-Sudan - Spain - Sri-Lanka - St-Barth - St-Vincent-Grenadines - Sudan - Suriname - Sweden - Switzerland - Syria - Taiwan - Tajikistan - Tanzania - Thailand - Timor-Leste - Togo - Tonga - Trinidad-and-Tobago - Tunisia - Turkey - Turks-and-Caicos - UAE - Uganda - UK - Ukraine - Uruguay - US-Virgin-Islands - USA - Uzbekistan - Vanuatu - Vatican-City - Venezuela - Vietnam - Wallis-and-Futuna - Western-Sahara - Yemen - Zambia - Zimbabw-

MODEL STRATEGIESThis model assesses costs and outcomes for admitted severe and critical Covid-19 patients from time of admission to discharge or deathFour competing strategies are modelled:1. The "status quo" summarizes costs and outcomes for patients assuming no dexamethasone or remdesivir2. The "dexamethasone" comparator summarizes costs and outcomes for patients assuming dexamethasone at 6mg/day3. The "remdesivir" comparator summarizes costs and outcomes for patients assuming remdesivir at 100mg/day4. The "remdesivr plus dexamethasone" comparator summarizes costs and outcomes for patients assuming remdesivir at 100mg/day plus dexamethasone at 6mg/dayMODEL OUTCOMESCost: mean cost (general ward, ICU, dexamethasone and remdesivir as appropriate) per admitted patient from the health care provider's perspectiveHealth outcomes: mean DALYs and deaths per admitted patient Other outcomes: mean ICU days and inpatient days per admitted patientBudget impact: mean costs (or savings) associated with intervention implementation

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.

IntroductionBiomarkers predicting mortality among critical Coronavirus disease 2019 (COVID-19) patients provide insight into the underlying pathophysiology of fatal disease and assist with triaging of cases in overburdened settings. However, data describing these biomarkers in Sub-Saharan African populations are sparse.MethodsWe collected serum samples and corresponding clinical data from 87 patients with critical COVID-19 on day 1 of admission to the intensive care unit (ICU) of a tertiary hospital in Cape Town, South Africa, during the second wave of the COVID-19 pandemic. A second sample from the same patients was collected on day 7 of ICU admission. Patients were followed up until in-hospital death or hospital discharge. A custom-designed 52 biomarker panel was performed on the Luminex® platform. Data were analyzed for any association between biomarkers and mortality based on pre-determined functional groups, and individual analytes.ResultsOf 87 patients, 55 (63.2%) died and 32 (36.8%) survived. We found a dysregulated cytokine response in patients who died, with elevated levels of type-1 and type-2 cytokines, chemokines, and acute phase reactants, as well as reduced levels of regulatory T cell cytokines. Interleukin (IL)-15 and IL-18 were elevated in those who died, and levels reduced over time in those who survived. Procalcitonin (PCT), C-reactive protein, Endothelin-1 and vascular cell adhesion molecule-1 were elevated in those who died.DiscussionThese results show the pattern of dysregulation in critical COVID-19 in a Sub-Saharan African cohort. They suggest that fatal COVID-19 involved excessive activation of cytotoxic cells and the NLRP3 (nucleotide-binding domain, leucine-rich–containing family, pyrin domain–containing-3) inflammasome. Furthermore, superinfection and endothelial dysfunction with thrombosis might have contributed to mortality. HIV infection did not affect the outcome. A clinically relevant biosignature including PCT, pH and lymphocyte percentage on differential count, had an 84.8% sensitivity for mortality, and outperformed the Luminex-derived biosignature.

Abstract

This dataset is a subset of the wider population-based Covid-19 surveillance run at the Africa Health Research Institute from 2020 onwards. The dataset covers one complete year of data collection, such that all residents had the opportunity to participate. The dataset specifically provides all observations and variables needed to replicate the analyses described in the journal article “COVID-19 vaccine uptake, confidence and hesitancy in rural KwaZulu-Natal, South Africa between April 2021 and April 2022: a continuous cross-sectional surveillance study” published in PLOS Global Public Health in 2023. The dataset includes variable on Covid vaccine uptake and willingness to take a hypothetical vaccine offer on the day of interview, as well as variables measuring four groups of potential predictors of these vaccine outcomes: demographics (age, sex), pre-existing conditions (information sources, government trust, education, urbanicity), contextual factors (impact of Covid on household economics and community wellbeing, Covid-related stigma, household age composition) and cues to action (recent case counts in KwaZulu-Natal, concern about impact if infected with Covid, knowledge of others with past Covid infection, household vaccination status, depression/anxiety) and interview date.

Geographic coverage

AHRI demographic surveillance area, uMkhanyakude district in northern KwaZulu-Natal

Analysis unit

Individual

Universe

All individuals aged 18 and over resident within the areas of the Africa Health Research Institute Population Intervention Programme

Kind of data

Survey data

Sampling procedure

All adult residents in the geographic area were eligible via an individual face-to-face interview. Multiple attempts were made to reach each individual if necessary. The final sample reflects all those who consented to and completed an interview.

Cleaning operations

Pentaho Data Integration was used to extract the datasets. NESSTAR Publisher was used to document the datasets.

BACKGROUND Severe acute respiratory syndrome coronavirus (SARS-CoV-2) omicron variant was first detected in South Africa in November 2021. Since then, the number of cases due to this variant increases enormously every day in different parts of the world. Mutations within omicron genome may impair the molecular detection resulting in false negative results during Coronavirus disease 19 (COVID-19) diagnosis. OBJECTIVES To verify if colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) targeting N and E genes would work efficiently to detect omicron SARS-CoV-2 variant and its sub-lineages. METHODS SARS-CoV-2 reverse transcription quantitative polymerase chain reaction (RT-qPCR) positive samples were sequenced by next generation DNA sequencing. The consensus sequences generated were submitted to Pangolin tool for SARS-CoV-2 lineage identification. RT-LAMP reactions were performed at 65ºC/30 min targeting N and E. FINDINGS SARS-CoV-2 omicron can be detected by RT-LAMP targeting N and E genes despite the genomic mutation of this more transmissible lineage. Omicron SARS-CoV-2 sub-lineages were tested and efficiently detected by RT-LAMP. We demonstrated that this test is very sensitive in detecting omicron variant, with LoD as low as 0.4 copies/µL. MAIN CONCLUSIONS Molecular detection of omicron SARS-CoV-2 variant and its sub-lineages can be achieved by RT-LAMP despite the genomic mutations as a very sensitive surveillance tool for COVID-19 molecular diagnosis.

In 2022, the estimated number of deaths in South Africa reached *******. This was lower compared to the previous year when the deaths in the country reached the highest level since 2002, at *******. From 2006 onwards (except in 2015), the number of fatalities dropped annually until 2017. In 2021, however, the count of deaths jumped significantly due to the global coronavirus (COVID-19) pandemic.