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.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

South Africa COVID-2019: Number of Cases: To Date: CC: Unallocated data was reported at 1.000 Person in 01 Feb 2023. This stayed constant from the previous number of 1.000 Person for 25 Jan 2023. South Africa COVID-2019: Number of Cases: To Date: CC: Unallocated data is updated daily, averaging 0.000 Person from Mar 2020 (Median) to 01 Feb 2023, with 876 observations. The data reached an all-time high of 403.000 Person in 31 Mar 2022 and a record low of 0.000 Person in 14 Dec 2022. South Africa COVID-2019: Number of Cases: To Date: CC: Unallocated data remains active status in CEIC and is reported by Department of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table ZA.D001: South African Department of Health: Coronavirus Disease 2019 (COVID-2019).

South Africa COVID-2019: Number of Cases: To Date: CC: Eastern Cape data was reported at 366,907.000 Person in 01 Feb 2023. This records an increase from the previous number of 366,769.000 Person for 25 Jan 2023. South Africa COVID-2019: Number of Cases: To Date: CC: Eastern Cape data is updated daily, averaging 198,717.000 Person from Mar 2020 (Median) to 01 Feb 2023, with 876 observations. The data reached an all-time high of 366,907.000 Person in 01 Feb 2023 and a record low of 0.000 Person in 20 Mar 2020. South Africa COVID-2019: Number of Cases: To Date: CC: Eastern Cape data remains active status in CEIC and is reported by Department of Health. The data is categorized under High Frequency Database’s Disease Outbreaks – Table ZA.D001: South African Department of Health: Coronavirus Disease 2019 (COVID-2019).

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)

The data consists of daily reported COVID-19 cases in South Africa for the period 7 March 2020 to 27 September 2021. The data is used in the manuscript "Estimation of extreme quantiles of confirmed COVID-19 cases using South African data"

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.

South Africa recorded 102595 Coronavirus Deaths since the epidemic began, according to the World Health Organization (WHO). In addition, South Africa reported 4072533 Coronavirus Cases. This dataset includes a chart with historical data for South Africa Coronavirus Deaths.

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

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

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.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

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-

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the “0–20 first” strategy was the most effective way to reduce confirmed cases. In addition, “21–30 first” and “31–40 first” strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to “0–20” groups always had a favorable effect, and the prioritizing vaccine allocation among the “60+” age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.

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.

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.

IntroductionMultisystem inflammatory syndrome in children (MIS-C) is a severe acute inflammatory reaction to SARS-CoV-2 infection in children. There is a lack of data describing differential expression of immune genes in MIS-C compared to healthy children or those with other inflammatory conditions and how expression changes over time. In this study, we investigated expression of immune-related genes in South African MIS-C patients and controls.MethodsThe cohort included 30 pre-treatment MIS-C cases and 54 healthy non-inflammatory paediatric controls. Other controls included 34 patients with juvenile systemic lupus erythematosus, Kawasaki disease or other inflammatory conditions. Longitudinal post-treatment MIS-C specimens were available at various timepoints. Expression of 80 immune-related genes was determined by real-time quantitative PCR.ResultsA total of 29 differentially expressed genes were identified in pre-treatment MIS-C compared to healthy controls. Up-regulated genes were found to be overrepresented in innate immune pathways including interleukin-1 processing and pyroptosis. Post-treatment follow-up data were available for up to 1,200 hours after first treatment. All down-regulated genes and 17/18 up-regulated genes resolved to normal levels in the timeframe, and all patients clinically recovered. When comparing MIS-C to other febrile conditions, only IL27 expression could differentiate these two groups with high sensitivity and specificity.ConclusionsThese data indicate a unique 29-gene signature of MIS-C in South African children. The up-regulation of interleukin-1 and pyroptosis pathway genes highlights the role of the innate immune system in MIS-C. IL-27 is a potent anti-inflammatory and antiviral cytokine that may distinguish MIS-C from other conditions in our setting.