The impact of SARS-CoV-2 variants on the COVID-19 epidemic in South Africa

Emerging variants of severe acute coronavirus 2 respiratory syndrome (SARS-CoV-2) have contributed to successive waves of coronavirus disease 2019 (COVID-19). As several of these variants have the ability to evade immunity, they also pose a huge threat to the success of COVID-19 vaccines in achieving herd immunity.

New study published on medRxiv * the preprint server explores the role of new variants of SARS-CoV-2 in maintaining the pandemic in South Africa. In addition, the researchers in this study found that these options increased the severity of the disease and viral transmission in the country, thus increasing morbidity and mortality from COVID-19.

Study: The impact of SARS-CoV-2 lines (variants) on the COVID-19 epidemic in South Africa. Image credit: Quatrox Production /


As of October 31, 2021, the COVID-19 pandemic had caused more than 5 million deaths worldwide, among more than 247 million infections. While national and state governments have imposed local restrictions on the movement of people, combined with the closure of businesses, schools, leisure activities and tourist centers, these measures have had a significant adverse impact on the global economy.

In response, vaccine development efforts have gained momentum, culminating in the release of several vaccines in late 2020. However, low- and middle-income countries are still far behind in vaccine coverage, leaving large populations vulnerable. to the virus.

New dangerous variants of SARS-CoV-2 (VOC) have further exacerbated this situation, leading to further spread of the virus even among immune populations. The SARS-CoV-2 genome is a single strand of ribonucleic acid (RNA) with multiple spikes of glycoproteins on the envelope. The genome encodes many structural, nonstructural, and helper proteins.

The two main transcription units are the open reading frames (ORFs) 1a and 1b, which lead to polyproteins PP1a and PP1b, respectively. PP1a transcribes four major structural proteins and 16 non-structural proteins (Nsp1-Nsp16), which are involved in viral replication, correction, translation, suppression of host translation, immune avoidance, and stabilization.

Another third of the genome encodes structural and helper proteins. The latter is necessary for proper genome-host interaction and inhibition of cytokine production, among other functions.

The spike protein SARS-CoV-2 is involved in viral binding and entry into the host cell. The spike undergoes cleavage at a unique furin-like cleavage site (FCS) to form S1 and S2 subunits.

The S1 subunit has a receptor-binding domain (RBD) and an N-terminal domain (NTD). RBD mediates the attachment of thorn protein to the receptor of angiotensin-converting enzyme 2 (ACE2) on the host cell.

The S2 subunit is responsible for the fusion of the viral cell membrane. After binding to the S1 receptor, the S2 subunit is primed by host transmembrane serine protease 2 (TMPRSS2), allowing the conformational switch that mediates viral cell entry and replication endocytosis.

Numerous mutations in the viral genome have been reported. The wild type is considered to be very similar to the Wuhan-Hu-1 strain. With over 200,000 genomes uploaded to the Global Avian Influenza Data Sharing Initiative (GISAID) database, the algorithm was initially designed to map genomic relationships. This is called phylogenetic assignment of so-called global epidemic lines (PANGOLIN).

Two sub-lines, A and B, have been identified, with the main lines C and D being redirected to line B. More than 266 lines have so far been identified, with line B and sub-line B.1 dominating worldwide.

Until February 2020, mutations were rare, but then the virus spread rapidly, showing many mutations, despite its ability to correct. The finding that groups of mutations define different strains led to the differentiation of newer variants into variants of concern (VOCs) and variants of interest (VOIs).

VOCs may either show increased transmissibility, cause more severe disease, as evidenced by increased hospitalizations and deaths, or show resistance to antibodies caused by previous infection or vaccination, thereby affecting the efficacy of the vaccine (VE). ). SARS-CoV-2 VOCs include alpha (B.1.1.7), beta (B.1.351, B1.351.2, B.1.351.3), gamma (P.1, P.1.1, P.1.2, P. 1.4 , P1.6, P.1.7) and Delta (B.1.617.2, AY.1, AY.2, AY.3, AY.3.1) lines.

VOIs show genetic markers that alter viral characteristics such as the above and can therefore pose a threat to global health. Known SARs-CoV-2 VOIs include the Eta (B.1.525), Iota (B.1.526), ​​Kappa (B.1.617.1) and Lambda (C.37) lines.

The present study focuses on the effect of SARS-CoV-2 lines in South Africa. The first case of COVID-19 in the country was on March 5, 2020, followed by three waves. They occurred between March 5 and September 30, 2020; December 1, 2020, peak on January 15, 2021; and respectively from April 27 to the end of September 2021.

South Africa set up the South African Genome Monitoring Network (NGS-SA) to monitor the spread of the virus in June 2020. The first wave detected more than 16 lines of the virus circulating in the region alone. Of these, there were three clusters responsible for over 40% of infections in the country.

C.1 was the most common of the genera at the time. The beta variant, which appeared in October 2020, consisted of three RBD mutations, including K417N, E484K and N501Y, and caused a second wave in that country, displacing earlier clusters. This strain of SARS-CoV-2 is resistant to existing neutralizing antibodies, with half of the maximum inhibitory concentration (IC50) being 6 to 200 times higher than that required for first wave lines.

The third wave is related to the Alpha, Beta, Eta and Delta SARS-CoV variants, with about two thirds being caused by the Beta variant and the other three representing about a quarter of the cases in May 2021. By June 2021, the picture had changed of one in which the Delta variant is dominant, in 66% of sequenced genomes, while Beta causes 16% of cases.

The Delta variant was sequenced in 96% of the genome samples taken by September 2021, while C1.2 was 1%. The latter emerged in May 2021 from line C.1, showing multiple mutations and deletions, with a collection of new mutations that are likely to impart neutralization resistance. This option has spread to most of the country.

How pedigree affects portability

The average number of cases in each wave is constantly increasing to double the initial level, both the average active cases and the daily new cases. Genealogical cluster 1 (mainly B.1.1.54 and B.1.1.56 C.1) in the first wave gave way smoothly to genealogical cluster 2 (mostly beta) (B.1.351) in the second, with almost no difference in daily new or active cases .

In the third wave, the average daily cases increased by more than half compared to the first two waves, driven mainly by the Delta variant, which therefore turned out to be far more portable than the others.

Effect of hospitalization

The hospitalized cases are mostly those with severe or critical COVID-19. The largest increase in the ratio of hospitalized to active cases is observed in the transition to the second wave, by over 110%, compared to about 55% and 35% for the first / third and second / third wave, respectively.

This includes the beta variant in the increased severity of the disease during the second wave, while the Delta variant causes a more severe infection than the first cluster.

The severity of the disease

During all three waves, 72-78% of the hospitalized patients were in the general ward, with a similar percentage of patients in the intensive care unit all the time. However, those of oxygen increase from about 17% in the first wave to approximately 28% and 22% in the second and third waves, respectively, although cases requiring ventilation remain stable. Thus, beta infection is associated with the highest percentage of patients requiring oxygen.

Age distribution

About 56% of all cases of COVID-19 in all waves occurred in people between the ages of 50 and 69, followed by about 15% in those between the ages of 40-49 in the first and third waves. The number of cases in the 60-69 age group increased from about 16% to 18% of the second wave. Ultimately, older people were targeted by the Delta option compared to earlier lines.

Mortality distribution

Most hospital deaths occurred in people between the ages of 60 and 69, with one in four people dying from COVID-19. In comparison, there was a 40% chance that those aged 50-59 would die from COVID-19.

Those between the ages of 70-79 make up the next largest segment of deaths, approximately one in five, during the first and third waves. In the second wave, about 30% of deaths occurred in people aged 60-69 years.

The highest risk of death is in the age group 50-59 years, driven largely by the increased percentage of such deaths in the second wave. Thus, the beta variant led to deaths in the age group 50-59 years.

Both hospitalizations and mortality among young people in the 0-19 and 0-29 age groups are very low, accounting for up to 4.4% and 2% of total deaths, respectively. However, there is no significant difference in the in-hospital mortality rates in the three waves.


The results of the present study show that beta VOCs cause an increased severity of the disease, while the delta wave causes about 55% higher transmission. The percentage of hospitalized patients on oxygen is the highest in the beta variant.

The Delta variant has caused more cases among those over the age of 70 than the other two generic groups. In general, most hospitalized patients are in the 50-69 age group. Mortality does not differ between the three waves, mostly aged 50-69 years. Children and young people (0-29 years) are relatively mildly affected.

The evolution of SARS-CoV-2 has led to an increase in the portability and severity of COVID-19 in South Africa. Delta SARS-CoV-2 VOC resulted in increased portability of COVID-19 in the South African population, while both Beta SARS-CoV-2 VOC and Delta SARS-CoV-2 VOC resulted in heavier COVID-19

*Important message

medRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered convincing, guiding clinical practice / health-related behavior, or treated as established information.

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