ABSTRACT
South Africa was among the first countries to detect the SARS-CoV-2 Omicron variant. Propelled by increased transmissibility and immune escape properties, Omicron displaced other globally circulating variants within 3 months of its emergence. Due to limited testing, Omicrons attenuated clinical severity, and an increased risk of reinfection, the size of the Omicron BA.1 and BA.2 subvariants (BA.1/2) wave remains poorly understood in South Africa and in many other countries. Using South African data from urban and rural cohorts closely monitored since the beginning of the pandemic, we analyzed sequential serum samples collected before, during, and after the Omicron BA.1/2 wave to infer infection rates and monitor changes in the immune histories of participants over time. Omicron BA.1/2 infection attack rates reached 65% (95% CI, 60% - 69%) in the rural cohort and 58% (95% CI, 61% - 74%) in the urban cohort, with repeat infections and vaccine breakthroughs accounting for >60% of all infections at both sites. Combined with previously collected data on pre-Omicron variant infections within the same cohorts, we identified 14 distinct categories of SARS-CoV-2 antigen exposure histories in the aftermath of the Omicron BA.1/2 wave, indicating a particularly fragmented immunologic landscape. Few individuals (<6%) remained naive to SARS-CoV-2 and no exposure history category represented over 25% of the population at either cohort site. Further, cohort participants were more than twice as likely to get infected during the Omicron BA.1/2 wave, compared to the Delta wave. Prior infection with the ancestral strain (with D614G mutation), Beta, and Delta variants provided 13% (95% CI, -21% - 37%), 34% (95% CI, 17% - 48%), and 51% (95% CI, 39% - 60%) protection against Omicron BA.1/2 infection, respectively. Hybrid immunity (prior infection and vaccination) and repeated prior infections (without vaccination) reduced the risks of Omicron BA.1/2 infection by 60% (95% CI, 42% - 72%) and 85% (95% CI, 76% - 92%) respectively. Reinfections and vaccine breakthroughs had 41% (95% CI, 26% - 53%) lower risk of onward transmission than primary infections. Our study sheds light on a rapidly shifting landscape of population immunity, along with the changing characteristics of SARS-CoV-2, and how these factors interact to shape the success of emerging variants. Our findings are especially relevant to populations similar to South Africa with low SARS-CoV-2 vaccine coverage and a dominant contribution of immunity from prior infection. Looking forward, the study provides context for anticipating the long-term circulation of SARS-CoV-2 in populations no longer naive to the virus.
ABSTRACT
BackgroundData on risk factors for COVID-19-associated hospitalisation and mortality in high HIV prevalence settings are limited. MethodsUsing existing syndromic surveillance programs for influenza-like-illness and severe respiratory illness at sentinel sites in South Africa, we identified factors associated with COVID-19 hospitalisation and mortality. ResultsFrom April 2020 through March 2022, SARS-CoV-2 was detected in 24.0% (660/2746) of outpatient and 32.5% (2282/7025) of inpatient cases. Factors associated with COVID-19-associated hospitalisation included: older age (25-44 [adjusted odds ratio (aOR) 1.8, 95% confidence interval (CI) 1.1-2.9], 45-64 [aOR 6.8, 95%CI 4.2-11.0] and [≥]65 years [aOR 26.6, 95%CI 14.4-49.1] vs 15-24 years); black race (aOR 3.3, 95%CI 2.2-5.0); obesity (aOR 2.3, 95%CI 1.4-3.9); asthma (aOR 3.5, 95%CI 1.4-8.9); diabetes mellitus (aOR 5.3, 95%CI 3.1-9.3); HIV with CD4 [≥]200/mm3 (aOR 1.5, 95%CI 1.1-2.2) and CD4<200/mm3 (aOR 10.5, 95%CI 5.1-21.6) or tuberculosis (aOR 12.8, 95%CI 2.8-58.5). Infection with Beta (aOR 0.5, 95%CI 0.3-0.7) vs Delta variant and being fully vaccinated (aOR 0.1, 95%CI 0.1-0.3) were less associated with COVID-19 hospitalisation. In-hospital mortality was increased in older age (45-64 years [aOR 2.2, 95%CI 1.6-3.2] and [≥]65 years [aOR 4.0, 95%CI 2.8-5.8] vs 25-44 years) and male sex (aOR1.3, 95%CI 1.0-1.6) and was lower in Omicron -infected (aOR 0.3, 95%CI 0.2-0.6) vs Delta-infected individuals. ConclusionActive syndromic surveillance encompassing clinical, laboratory and genomic data identified setting-specific risk factors associated with COVID-19 severity that will inform prioritization of COVID-19 vaccine distribution. Elderly, people with tuberculosis or people living with HIV, especially severely immunosuppressed should be prioritised for vaccination. Summary of articles viewpointCompared to the Delta variant, the Omicron variant was associated with reduced risk of mortality and Beta associated with decreased risk of hospitalisation. Active syndromic surveillance combining clinical, laboratory and genomic data can be used to describe the epidemic timing, epidemiological characteristics of cases, early detection of variants of concern and how these impact disease severity and outcomes; and presents a viable surveillance approach in settings where national surveillance is not possible.
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In this South African phase 1/2b study, we demonstrated vaccine efficacy (VE) of two doses of AZD1222 for asymptomatic and symptomatic SARS-CoV-2 infection: 90.6% against wild-type and 77.1% against the Delta variant [≥]9 months after vaccination. VE against infection with the Beta variant, which preceded circulation of Delta, was 6.7%. Clinical trial identifierCT.gov NCT04444674
ABSTRACT
Investment in Africa over the past year with regards to SARS-CoV-2 genotyping has led to a massive increase in the number of sequences, exceeding 100,000 genomes generated to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence within their own borders, coupled with a decrease in sequencing turnaround time. Findings from this genomic surveillance underscores the heterogeneous nature of the pandemic but we observe repeated dissemination of SARS-CoV-2 variants within the continent. Sustained investment for genomic surveillance in Africa is needed as the virus continues to evolve, particularly in the low vaccination landscape. These investments are very crucial for preparedness and response for future pathogen outbreaks. One-Sentence SummaryExpanding Africa SARS-CoV-2 sequencing capacity in a fast evolving pandemic.
ABSTRACT
BackgroundIn South Africa 19% of the adult population aged 15-49 years are living with HIV (LWH). Few data on the influence of HIV on SARS-CoV-2 household transmission are available. MethodsWe performed a case-ascertained, prospective household transmission study of symptomatic index SARS-CoV-2 cases LWH and HIV-uninfected adults and their contacts in South Africa. Households were followed up thrice weekly for 6 weeks to collect nasal swabs for SARS-CoV-2 testing. We estimated household cumulative infection risk (HCIR), duration of SARS-CoV-2 positivity (at cycle threshold value<30 as proxy for high viral load), and assessed associated factors. ResultsWe recruited 131 index cases and 457 household contacts. HCIR was 59% (220/373); not differing by index HIV status (60% [50/83] in cases LWH vs 58% [173/293] in HIV-uninfected cases, OR 1.0, 95%CI 0.4-2.3). HCIR increased with index case age (35-59 years: aOR 3.4 95%CI 1.5-7.8 and [≥]60 years: aOR 3.1, 95%CI 1.0-10.1) compared to 18-34 years, and contacts age, 13-17 years (aOR 7.1, 95%CI 1.5-33.9) and 18-34 years (aOR 4.4, 95%CI 1.0-18.4) compared to <5 years. Mean positivity duration at high viral load was 7 days (range 2-28), with longer positivity in cases LWH (aHR 0.3, 95%CI 0.1-0.7). ConclusionsHIV-infection was not associated with higher HCIR, but cases LWH had longer positivity duration at high viral load. Adults aged >35 years were more likely to transmit, and individuals aged 13-34 to acquire SARS-CoV-2 in the household. Health services must maintain HIV testing with initiation of antiretroviral therapy for those HIV-infected. SummaryIn this case-ascertained, prospective household transmission study, household cumulative infection risk was 59% from symptomatic SARS-CoV-2 index cases, not differing based on index HIV status. Index cases living with HIV were positive for SARS-CoV-2 for longer at higher viral loads.
ABSTRACT
By November 2021, after the third SARS-CoV-2 wave in South Africa, seroprevalence was 60% (95%CrI 56%-64%) in a rural and 70% (95%CrI 56%-64%) in an urban community; highest in individuals aged 13-18 years. High seroprevalence prior to Omicron emergence may have contributed to reduced severity observed in the 4th wave. Article Summary LineIn South Africa, after a third wave of SARS-CoV-2 infections, seroprevalence was 60% in a rural and 70% in an urban community, with case-to-infection, - hospitalization and -fatality ratios similar to the second wave.
ABSTRACT
Understanding the build-up of immunity with successive SARS-CoV-2 variants and the epidemiological conditions that favor rapidly expanding epidemics will facilitate future pandemic control. High-resolution infection and serology data from longitudinal household cohorts in South Africa reveal high cumulative infection rates and durable cross-protective immunity conferred by prior infection in the pre-Omicron era. Building on the cohorts history of past exposures to different SARS-CoV-2 variants and vaccination, we use mathematical models to explore the fitness advantage of the Omicron variant and its epidemic trajectory. Modelling suggests the Omicron wave infected a large fraction of the population, leaving a complex landscape of population immunity primed and boosted with antigenically distinct variants. Future SARS-CoV-2 resurgences are likely under a range of scenarios of viral characteristics, population contacts, and residual cross-protection. One Sentence SummaryClosely monitored population in South Africa reveal high cumulative infection rates and durable protection by prior infection against pre-Omicron variants. Modelling indicates that a large fraction of the population has been infected with Omicron; yet epidemic resurgences are plausible under a wide range of epidemiologic scenarios.
ABSTRACT
Among the 30 non-synonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any genomes within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.
ABSTRACT
BackgroundThe SARS-CoV-2 Omicron variant of concern (VOC) almost completely replaced other variants in South Africa during November 2021, and was associated with a rapid increase in COVID-19 cases. We aimed to assess clinical severity of individuals infected with Omicron, using S Gene Target Failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy. MethodsWe performed data linkages for (i) SARS-CoV-2 laboratory tests, (ii) COVID-19 case data, (iii) genome data, and (iv) the DATCOV national hospital surveillance system for the whole of South Africa. For cases identified using Thermo Fisher TaqPath COVID-19 PCR, infections were designated as SGTF or non-SGTF. Disease severity was assessed using multivariable logistic regression models comparing SGTF-infected individuals diagnosed between 1 October to 30 November to (i) non-SGTF in the same period, and (ii) Delta infections diagnosed between April and November 2021. ResultsFrom 1 October through 6 December 2021, 161,328 COVID-19 cases were reported nationally; 38,282 were tested using TaqPath PCR and 29,721 SGTF infections were identified. The proportion of SGTF infections increased from 3% in early October (week 39) to 98% in early December (week 48). On multivariable analysis, after controlling for factors associated with hospitalisation, individuals with SGTF infection had lower odds of being admitted to hospital compared to non-SGTF infections (adjusted odds ratio (aOR) 0.2, 95% confidence interval (CI) 0.1-0.3). Among hospitalised individuals, after controlling for factors associated with severe disease, the odds of severe disease did not differ between SGTF-infected individuals compared to non-SGTF individuals diagnosed during the same time period (aOR 0.7, 95% CI 0.3-1.4). Compared to earlier Delta infections, after controlling for factors associated with severe disease, SGTF-infected individuals had a lower odds of severe disease (aOR 0.3, 95% CI 0.2-0.5). ConclusionEarly analyses suggest a reduced risk of hospitalisation among SGTF-infected individuals when compared to non-SGTF infected individuals in the same time period. Once hospitalised, risk of severe disease was similar for SGTF- and non-SGTF infected individuals, while SGTF-infected individuals had a reduced risk of severe disease when compared to earlier Delta-infected individuals. Some of this reducton is likely a result of high population immunity.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants respectively1-3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.
ABSTRACT
Global genomic surveillance of SARS-CoV-2 has identified variants associated with increased transmissibility, neutralization resistance and disease severity. Here we report the emergence of the PANGO lineage C.1.2, detected at low prevalence in South Africa and eleven other countries. The emergence of C.1.2, associated with a high substitution rate, includes changes within the spike protein that have been associated with increased transmissibility or reduced neutralization sensitivity in SARS-CoV-2 VOC/VOIs. Like Beta and Delta, C.1.2 shows significantly reduced neutralization sensitivity to plasma from vaccinees and individuals infected with the ancestral D614G virus. In contrast, convalescent donors infected with either Beta or Delta showed high plasma neutralization against C.1.2. These functional data suggest that vaccine efficacy against C.1.2 will be equivalent to Beta and Delta, and that prior infection with either Beta or Delta will likely offer protection against C.1.2.
ABSTRACT
BackgroundSARS-CoV-2 infections may be underestimated due to limited testing access, particularly in sub-Saharan Africa. South Africa experienced two SARS-CoV-2 waves, the second associated with emergence of variant 501Y.V2. In this study, we report longitudinal SARS-CoV-2 seroprevalence in cohorts in two communities in South Africa. MethodsWe measured SARS-CoV-2 seroprevalence two monthly in randomly selected household cohorts in a rural and an urban community (July 2020-March 2021). We compared seroprevalence to laboratory-confirmed infections, hospitalisations and deaths reported in the districts to calculate infection-case (ICR), infection-hospitalisation (IHR) and infection-fatality ratio (IFR) in the two waves of infection. FindingsSeroprevalence after the second wave ranged from 18% (95%CrI 10-26%) and 28% (95%CrI 17-41%) in children <5 years to 37% (95%CrI 28-47%) in adults aged 19-34 years and 59% (95%CrI 49-68%) in adults aged 35-59 years in the rural and urban community respectively. Individuals infected in the second wave were more likely to be from the rural site (aOR 4.7, 95%CI 2.9-7.6), and 5-12 years (aOR 2.1, 95%CI 1.1-4.2) or [≥]60 years (aOR 2.8, 95%CI 1.1-7.0), compared to 35-59 years. The in-hospital IFR in the urban site was significantly increased in the second wave 0.36% (95%CI 0.28-0.57%) compared to the first wave 0.17% (95%CI 0.15-0.20%). ICR ranged from 3.69% (95%CI 2.59-6.40%) in second wave at urban community, to 5.55% (95%CI 3.40-11.23%) in first wave in rural community. InterpretationThe second wave was associated with a shift in age distribution of cases from individuals aged to 35-59 to individuals at the extremes of age, higher attack rates in the rural community and a higher IFR in the urban community. Approximately 95% of SARS-CoV-2 infections in these two communities were not reported to the national surveillance system, which has implications for contact tracing and infection containment. FundingUS Centers for Disease Control and Prevention Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSeroprevalence studies provide better estimates of SARS-CoV-2 burden than laboratory-confirmed cases because many infections may be missed due to restricted access to care and testing, or differences in disease severity and health-care seeking behaviour. This underestimation may be amplified in African countries, where testing access may be limited. Seroprevalence data from sub-Saharan Africa are limited, and comparing seroprevalence estimates between countries can be challenging because populations studied and timing of the study relative to country-specific epidemics differs. During the first wave of infections in each country, seroprevalence was estimated at 4% in Kenya and 11% in Zambia. Seroprevalence estimates in South African blood donors is estimated to range between 32% to 63%. South Africa has experienced two waves of infection, with the emergence of the B.1.351/501Y.V2 variant of concern after the first wave. Reported SARS-CoV-2 cases may not be a true reflection of SARS-CoV-2 burden and specifically the differential impact of the first and second waves of infection. Added value of this studyWe collected longitudinal blood samples from prospectively followed rural and urban communities, randomly selected, household cohorts in South Africa between July 2020 and March 2021. From 668 and 598 individuals included from the rural and urban communities, respectively, seroprevalence was found to be 7% (95%CrI 5-9%) and 27% (95%CrI 23-31%), after the first wave of infection, and 26% (95%CrI 22-29%) and 41% (95%CrI 37-45%) after the second wave, in rural and urban study districts, respectively. After standardising for age, we estimated that only 5% of SARS-CoV-2 infections were laboratory-confirmed and reported. Infection-hospitalisation ratios in the urban community were higher in the first (2.01%, 95%CI 1.57-2.57%) and second (2.29%, 95%CI 1.63-3.94%) wave than the rural community where there was a 0.75% (95%CI 0.49-1.41%) and 0.66% (95%CI 0.50-0.98%) infection-hospitalisation ratio in the first and second wave, respectively. When comparing the infection fatality ratios for the first and second SARS-CoV-2 waves, at the urban site, the ratios for both in-hospital and excess deaths to cases were significantly higher in the second wave (0.36%, 95%CI 0.28-0.57% in-hospital and 0.51%, 95%CI 0.34-0.93% excess deaths), compared to the first wave in-hospital (0.17%, 95%CI 0.15-0.20%) and excess (0.13%, 95%CI 0.10-0.17%) fatality ratios, p<0.001 and p<0.001, respectively). In the rural community, the point estimates for infection-fatality ratios also increased in the second wave compared to the first wave for in-hospital deaths, 0.13% (95%CI 0.10-0.23%) first wave vs 0.20% (95%CI 0.13%-0.28%) second wave, and excess deaths (0.51%, 95%CI 0.30-1.06% vs 0.70%, 95%CI 0.49-1.12%), although neither change was statistically significant. Implications of all the available evidenceIn South Africa, the overall prevalence of SARS-CoV-2 infections is substantially underestimated, resulting in many cases being undiagnosed and without the necessary public health action to isolate and trace contacts to prevent further transmission. There were more infections during the first wave in the urban community, and the second wave in the rural community. Although there were less infections during the second wave in the urban community, the infection-fatality ratios were significantly higher compared to the first wave. The lower infection-hospitalisation ratio and higher excess infection-fatality ratio in the rural community likely reflect differences in access to care or prevalence of risk factors for progression to severe disease in these two communities. In-hospital infection-fatality ratios for both communities during the first wave were comparable with what was experienced during the first wave in India (0.15%) for SARS-CoV-2 confirmed deaths. To our knowledge, these are the first longitudinal seroprevalence data from a sub-Saharan Africa cohort, and provide a more accurate understanding of the pandemic, allowing for serial comparisons of antibody responses in relation to reported laboratory-confirmed SARS-CoV-2 infections within diverse communities.
ABSTRACT
IntroductionSouth Africa experienced its first wave of COVID-19 peaking in mid-July 2020 and a larger second wave peaking in January 2021, in which the SARS-CoV-2 501Y.V2 lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves of COVID-19. MethodsWe analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations. We defined four wave periods using incidence risk for hospitalisation, pre-wave 1, wave 1, pre-wave 2 and wave 2. We compared the characteristics of hospitalised COVID-19 cases in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using multivariable logistic regression. ResultsPeak rates of COVID-19 cases, admissions and in-hospital deaths in the second wave exceeded the rates in the first wave (138.1 versus 240.1; 16.7 versus 28.9; and 3.3 versus 7.1 respectively per 100,000 persons). The weekly average incidence risk increase in hospitalisation was 22% in wave 1 and 28% in wave 2 [ratio of growth rate in wave two compared to wave one: 1.04, 95% CI 1.04-1.05]. On multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave (adjusted OR 1.2, 95% CI 1.2-1.3). In-hospital case fatality-risk (CFR) increased in weeks of peak hospital occupancy, from 17.9% in weeks of low occupancy (<3,500 admissions) to 29.6% in weeks of very high occupancy (>12,500 admissions) (adjusted OR 1.5, 95% CI 1.4-1.5). Compared to the first wave, individuals hospitalised in the second wave, were more likely to be older, 40-64 years [OR 1.1, 95% CI 1.0-1.1] and [≥]65 years [OR 1.1, 95% CI 1.1-1.1] compared to <40 years; and admitted in the public sector [OR 2.2, 95% CI 1.7-2.8]; and less likely to have comorbidities [OR 0.5, 95% CI 0.5-0.5]. ConclusionsIn South Africa, the second wave was associated with higher incidence and more rapid increase in hospitalisations, and increased in-hospital mortality. While some of this is explained by increasing pressure on the health system, a residual increase in mortality of hospitalised patients beyond this, could be related to the new lineage 501Y.V2. RESEARCH IN CONTEXT O_TEXTBOXEvidence before this studyMost countries have reported higher numbers of COVID-19 cases in the second wave but lower case-fatality risk (CFR), in part due to new therapeutic interventions, increased testing and better prepared health systems. South Africa experienced its second wave which peaked in January 2021, in which the variant of concern, SARS-CoV-2 501Y.V2 predominated. New variants have been shown to be more transmissible and in the United Kingdom, to be associated with increased hospitalisation and mortality rates in people infected with variant B.1.1.7 compared to infection with non-B.1.1.7 viruses. There are currently limited data on the severity of lineage 501Y.V2. Added value of this studyWe analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations, comparing in-hospital mortality and other patient characteristics between the first and second waves of COVID-19. The study revealed that after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave. Our study also describes the demographic shift from the first to the second wave of COVID-19 in South Africa, and quantifies the impact of overwhelmed hospital capacity on in-hospital mortality. Implications of all the available evidenceOur data suggest that the new lineage (501Y.V2) in South Africa may be associated with increased in-hospital mortality during the second wave. Our data should be interpreted with caution however as our analysis is based on a comparison of mortality in the first and second wave as a proxy for dominant lineage and we did not have individual-level data on lineage. Individual level studies comparing outcomes of people with and without the new lineage based on sequencing data are needed. To prevent high mortality in a potential third wave, we require a combination of strategies to slow the transmission of SARS-CoV-2, to spread out the peak of the epidemic, which would prevent hospital capacity from being breached. C_TEXTBOX
ABSTRACT
SARS-CoV-2 501Y.V2 (B.1.351), a novel lineage of coronavirus causing COVID-19, contains substitutions in two immunodominant domains of the spike protein. Here, we show that pseudovirus expressing 501Y.V2 spike protein completely escapes three classes of therapeutically relevant antibodies. This pseudovirus also exhibits substantial to complete escape from neutralization, but not binding, by convalescent plasma. These data highlight the prospect of reinfection with antigenically distinct variants and foreshadows reduced efficacy of spike-based vaccines.
ABSTRACT
Continued uncontrolled transmission of the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in many parts of the world is creating the conditions for significant virus evolution. Here, we describe a new SARS-CoV-2 lineage (501Y.V2) characterised by eight lineage-defining mutations in the spike protein, including three at important residues in the receptor-binding domain (K417N, E484K and N501Y) that may have functional significance. This lineage emerged in South Africa after the first epidemic wave in a severely affected metropolitan area, Nelson Mandela Bay, located on the coast of the Eastern Cape Province. This lineage spread rapidly, becoming within weeks the dominant lineage in the Eastern Cape and Western Cape Provinces. Whilst the full significance of the mutations is yet to be determined, the genomic data, showing the rapid displacement of other lineages, suggest that this lineage may be associated with increased transmissibility.
