Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

SARS-CoV-2 correlates of protection from infection against variants of concern

Nature Medicine volume 30, pages 2805–2812 (2024)Cite this article

Subjects

Abstract

Serum neutralizing antibodies (nAbs) induced by vaccination have been linked to protection against symptomatic and severe coronavirus disease 2019. However, much less is known about the efficacy of nAbs in preventing the acquisition of infection, especially in the context of natural immunity and against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immune-escape variants. Here we conducted mediation analysis to assess serum nAbs induced by prior SARS-CoV-2 infections as potential correlates of protection against Delta and Omicron infections, in rural and urban household cohorts in South Africa. We find that, in the Delta wave, D614G nAbs mediate 37% (95% confidence interval: 34–40%) of the total protection against infection conferred by prior exposure to SARS-CoV-2, and that protection decreases with waning immunity. In contrast, Omicron BA.1 nAbs mediate 11% (95% confidence interval: 9–12%) of the total protection against Omicron BA.1 or BA.2 infections, due to Omicron’s neutralization escape. These findings underscore that correlates of protection mediated through nAbs are variant specific, and that boosting of nAbs against circulating variants might restore or confer immune protection lost due to nAb waning and/or immune escape. However, the majority of immune protection against SARS-CoV-2 conferred by natural infection cannot be fully explained by serum nAbs alone. Measuring these and other immune markers including T cell responses, both in the serum and in other compartments such as the nasal mucosa, may be required to comprehensively understand and predict immune protection against SARS-CoV-2.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Timing of cohort sample collections with respect to SARS-CoV-2 variants’ circulations in the two study sites.
Fig. 2: D614G and BA.1 nAb titers for the Delta wave and the Omicron wave analysis.
Fig. 3: Causal diagrams for the mediation analyses.

Similar content being viewed by others

Data availability

Aggregate data to reproduce the figures are available at Zenodo via https://doi.org/10.5281/zenodo.11375487 (ref. 58). Individual-level data cannot be publicly shared because of ethical restrictions and the potential for identifying included individuals. Accessing individual participant data and a data dictionary defining each field in the dataset would require provision of protocol and ethics approval for the proposed use. To request individual participant data access, please submit a proposal to C.C. who will respond within 1 month of request. Upon approval, data can be made available through a data sharing agreement.

Code availability

Code to reproduce the figures, using Python version 3.8.11 and SciPy version 1.7.1, is available at Zenodo via https://doi.org/10.5281/zenodo.11375487 (ref. 58).

References

  1. World Health Organization. Statement on the Fifteenth Meeting of the IHR (2005) Emergency Committee on the COVID-19 pandemic. https://www.who.int/news/item/05-05-2023-statement-on-the-fifteenth-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-coronavirus-disease-(covid-19)-pandemic (2023).

  2. World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/ (accessed 20 June 2024).

  3. World Health Organization. WHO COVID19 Vaccine Tracker. https://covid19.trackvaccines.org/agency/who/ (accessed 20 June 2024).

  4. Watson, O. J. et al. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. Lancet Infect. Dis. 22, 1293–1302 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Gozzi, N. et al. Estimating the impact of COVID-19 vaccine inequities: a modeling study. Nat. Commun. 14, 3272 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, Q. et al. Mapping global acceptance and uptake of COVID-19 vaccination: a systematic review and meta-analysis. Commun. Med. 2, 113 (2022).

    PubMed  PubMed Central  Google Scholar 

  7. Lazarus, J. V. et al. A survey of COVID-19 vaccine acceptance across 23 countries in 2022. Nat. Med. 29, 366–375 (2023).

    CAS  PubMed  Google Scholar 

  8. Bergeri, I. et al. Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: a systematic review and meta-analysis of standardized population-based studies. PLoS Med. 19, e1004107 (2022).

    PubMed  PubMed Central  Google Scholar 

  9. Lewis, H. C. et al. SARS-CoV-2 infection in Africa: a systematic review and meta-analysis of standardised seroprevalence studies, from January 2020 to December 2021. BMJ Glob. Health 7, e008793 (2022).

    PubMed  Google Scholar 

  10. Wang, Q. et al. Antibody evasion by SARS-CoV-2 Omicron subvariants BA.2.12.1, BA.4 and BA.5. Nature 608, 603–608 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Ito, J. et al. Convergent evolution of SARS-CoV-2 Omicron subvariants leading to the emergence of BQ.1.1 variant. Nat. Commun. 14, 2671 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Cao, Y. et al. BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infection. Nature 608, 593–602 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cao, Y. et al. Imprinted SARS-CoV-2 humoral immunity induces convergent Omicron RBD evolution. Nature 614, 521–529 (2023).

    CAS  PubMed  Google Scholar 

  14. Goldblatt, D., Alter, G., Crotty, S. & Plotkin, S. A. Correlates of protection against SARS-CoV-2 infection and COVID-19 disease. Immunol. Rev. 310, 6–26 (2022).

    CAS  PubMed  Google Scholar 

  15. Gilbert, P. B. et al. Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial. Science 375, 43–50 (2022).

    CAS  PubMed  Google Scholar 

  16. Fong, Y. et al. Immune correlates analysis of the PREVENT-19 COVID-19 vaccine efficacy clinical trial. Nat. Commun. 14, 331 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Fong, Y. et al. Immune correlates analysis of the ENSEMBLE single Ad26.COV2.S dose vaccine efficacy clinical trial. Nat. Microbiol 7, 1996–2010 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Feng, S. et al. Correlates of protection against symptomatic and asymptomatic SARS-CoV-2 infection. Nat. Med. 27, 2032–2040 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Khoury, D. S. et al. Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection. Nat. Med. 27, 1205–1211 (2021).

    CAS  PubMed  Google Scholar 

  20. Earle, K. A. et al. Evidence for antibody as a protective correlate for COVID-19 vaccines. Vaccine 39, 4423–4428 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Atti, A. et al. Antibody correlates of protection against Delta infection after vaccination: a nested case–control within the UK-based SIREN study. J. Infect. 87, 420–427 (2023).

    CAS  PubMed  Google Scholar 

  22. Zhang, B. et al. Omicron COVID-19 immune correlates analysis of a third dose of mRNA-1273 in the COVE trial. Preprint at bioRxiv https://doi.org/10.1101/2023.10.15.23295628 (2023).

  23. Hertz, T. et al. Correlates of protection for booster doses of the SARS-CoV-2 vaccine BNT162b2. Nat. Commun. 14, 4575 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Gilboa, M. et al. Factors associated with protection from SARS-CoV-2 Omicron variant infection and disease among vaccinated health care workers in Israel. JAMA Netw. Open 6, e2314757 (2023).

    PubMed  PubMed Central  Google Scholar 

  25. Maier, H. E. et al. An immune correlate of SARS-CoV-2 infection and severity of reinfections. Preprint at medRxiv https://doi.org/10.1101/2021.11.23.21266767 (2021).

  26. Tang, J. et al. Respiratory mucosal immunity against SARS-CoV-2 after mRNA vaccination. Sci. Immunol. 7, eadd4853 (2022).

    CAS  PubMed  Google Scholar 

  27. Knisely, J. M. et al. Mucosal vaccines for SARS-CoV-2: scientific gaps and opportunities-workshop report. NPJ Vaccines 8, 53 (2023).

    PubMed  PubMed Central  Google Scholar 

  28. Miyamoto, S. et al. Infectious virus shedding duration reflects secretory IgA antibody response latency after SARS-CoV-2 infection. Proc. Natl Acad. Sci. USA 120, e2314808120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Markov, P. V. et al. The evolution of SARS-CoV-2. Nat. Rev. Microbiol. 21, 361–379 (2023).

    CAS  PubMed  Google Scholar 

  30. Cohen, C. et al. SARS-CoV-2 incidence, transmission, and reinfection in a rural and an urban setting: results of the PHIRST-C cohort study, South Africa, 2020-21. Lancet Infect. Dis. 22, 821–834 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Sun, K. et al. Rapidly shifting immunologic landscape and severity of SARS-CoV-2 in the Omicron era in South Africa. Nat. Commun. 14, 246 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Pulliam, J. R. C. et al. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science 376, eabn4947 (2022).

    CAS  PubMed  Google Scholar 

  33. Sun, K. et al. SARS-CoV-2 transmission, persistence of immunity, and estimates of Omicron’s impact in South African population cohorts. Sci. Transl. Med. 14, eabo7081 (2022).

    CAS  PubMed  Google Scholar 

  34. Roche Diagnostics. Elecsys Anti-SARS-CoV-2. https://diagnostics.roche.com/us/en/products/params/elecsys-anti-sars-cov-2.html (accessed June 20, 2024).

  35. Cowling, B. J. et al. Influenza hemagglutination-inhibition antibody titer as a mediator of vaccine-induced protection for influenza B. Clin. Infect. Dis. 68, 1713–1717 (2019).

    CAS  PubMed  Google Scholar 

  36. Lim, W. W., Shuo, F., Wong, S.-S., Sullivan, S. G. & Cowling, B. J. Hemagglutination inhibition antibody titers mediate influenza vaccine efficacy against symptomatic influenza A(H1N1), A(H3N2), and B/Victoria infections. J. Infect. Dis. 7, jiae122 (2024).

    Google Scholar 

  37. Halloran, M. E. & Struchiner, C. J. Causal inference in infectious diseases. Epidemiology 6, 142–151 (1995).

    CAS  PubMed  Google Scholar 

  38. Cohen, K. W. et al. Longitudinal analysis shows durable and broad immune memory after SARS-CoV-2 infection with persisting antibody responses and memory B and T cells. Cell Rep. Med. 2, 100354 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Dan, J. M. et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science 371, eabf4063 (2021).

    CAS  PubMed  Google Scholar 

  40. Eser, T. M. et al. Nucleocapsid-specific T cell responses associate with control of SARS-CoV-2 in the upper airways before seroconversion. Nat. Commun. 14, 2952 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Havervall, S. et al. Anti-spike mucosal IgA protection against SARS-CoV-2 Omicron infection. N. Engl. J. Med. 387, 1333–1336 (2022).

    PubMed  Google Scholar 

  42. Plotkin, S. A. Vaccines: correlates of vaccine-induced immunity. Clin. Infect. Dis. 47, 401–409 (2008).

    PubMed  Google Scholar 

  43. Morens, D. M., Taubenberger, J. K. & Fauci, A. S. Rethinking next-generation vaccines for coronaviruses, influenzaviruses, and other respiratory viruses. Cell Host Microbe 31, 146–157 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Topol, E. J. & Iwasaki, A. Operation Nasal Vaccine-Lightning speed to counter COVID-19. Sci. Immunol. 7, eadd9947 (2022).

    PubMed  Google Scholar 

  45. US Department of Health and Human Services. Fact Sheet: HHS details $5 billion ‘Project NextGen’ initiative to stay ahead of COVID-19. https://aspr.hhs.gov/newsroom/Pages/ProjectNextGen-May2023.aspx (accessed 20 June 2024).

  46. Liu, W. et al. Predictors of nonseroconversion after SARS-CoV-2 infection. Emerg. Infect. Dis. 27, 2454–2458 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Lindeboom, R. G. H. et al. Human SARS-CoV-2 challenge uncovers local and systemic response dynamics. Nature https://doi.org/10.1038/s41586-024-07575-x (2024).

  48. McCallum, M. et al. Molecular basis of immune evasion by the Delta and Kappa SARS-CoV-2 variants. Science 374, 1621–1626 (2021).

    CAS  PubMed  Google Scholar 

  49. Wilks, S. H. et al. Mapping SARS-CoV-2 antigenic relationships and serological responses. Science 382, eadj0070 (2023).

    CAS  PubMed  Google Scholar 

  50. Tegally, H. et al. Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa. Nat. Med. https://doi.org/10.1038/s41591-022-01911-2 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Fourati, S. et al. Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination. Nat. Immun. 23, 1777–1787 (2022).

  52. Wibmer, C. K. et al. SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma. Nat. Med. https://doi.org/10.1038/s41591-021-01285-x (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Netzl, A. et al. Analysis of SARS-CoV-2 Omicron neutralization data up to 2022-01-28. Preprint at bioRxiv https://doi.org/10.1101/2021.12.31.474032 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Cox, D. R. Planning of Experiments (Wiley, 1958).

  55. Imbens, G. W. & Rubin, D. B. Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, 2015).

  56. Longini, I. M. Jr & Koopman, J. S. Household and community transmission parameters from final distributions of infections in households. Biometrics 38, 115–126 (1982).

    PubMed  Google Scholar 

  57. Yang, Y., Longini, I. M. Jr, Halloran, M. E. & Obenchain, V. A hybrid EM and Monte Carlo EM algorithm and its application to analysis of transmission of infectious diseases. Biometrics 68, 1238–1249 (2012).

    PubMed  PubMed Central  Google Scholar 

  58. Kaiyuan, S. Data and code for SARS-CoV-2 correlates of protection from infection against variants of concern. Zenodo https://doi.org/10.5281/zenodo.11375487 (2024).

Download references

Acknowledgements

We thank all the participants who kindly agreed to take part in the study, as well as the PHIRST-C group. We are grateful to B. J. Cowling and M. E. Halloran for their insightful feedback on the paper. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institutes of Health or the US Centers for Disease Control and Prevention. This work was supported by the National Institute for Communicable Diseases of the National Health Laboratory Service and the US Centers for Disease Control and Prevention (cooperative agreement no. 6 U01IP001048) and the Wellcome Trust (grant no. 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth and Development Office, United Kingdom. P.L.M. and J.N.B. are supported by the Bill and Melinda Gates Foundation through the Global Immunology and Immune Sequencing for Epidemic Response (GIISER) program (INV-030570) and receive funding from the Wellcome Trust (226137/Z/22/Z). P.L.M. is supported by the South African Research Chairs Initiative of the Department of Science and Innovation and National Research Foundation of South Africa and the SA Medical Research Council SHIP program.

Author information

Author notes

  1. These authors contributed equally: Kaiyuan Sun, Jinal N. Bhiman.

  2. These authors jointly supervised this work: Cécile Viboud, Penny L. Moore, Cheryl Cohen.

Authors and Affiliations

  1. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

    Kaiyuan Sun & Cécile Viboud

  2. SAMRC Antibody Immunity Research Unit, University of the Witwatersrand, Johannesburg, South Africa

    Jinal N. Bhiman, Vimbai Sharon Madzorera, Qiniso Mkhize, Haajira Kaldine & Penny L. Moore

  3. Centre for HIV and STIs, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa

    Jinal N. Bhiman, Vimbai Sharon Madzorera, Qiniso Mkhize, Haajira Kaldine & Penny L. Moore

  4. Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa

    Stefano Tempia, Jackie Kleynhans, Nicole Wolter, Jocelyn Moyes, Maimuna Carrim, Thulisa Mkhencele, Anne von Gottberg, Cheryl Cohen, Amelia Buys, Maimuna Carrim, Linda de Gouveia, Mignon du Plessis, Retshidisitswe Kotane, Tumelo Moloantoa, Anne von Gottberg & Nicole Wolter

  5. School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

    Stefano Tempia, Jackie Kleynhans, Jocelyn Moyes & Cheryl Cohen

  6. Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA

    Stefano Tempia, Meredith L. McMorrow & Meredith L. McMorrow

  7. COVID-19 Response, Centers for Disease Control and Prevention, Atlanta, GA, USA

    Meredith L. McMorrow & Meredith L. McMorrow

  8. School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

    Nicole Wolter, Maimuna Carrim, Anne von Gottberg, Maimuna Carrim, Mignon du Plessis, Anne von Gottberg & Nicole Wolter

  9. Perinatal HIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa

    Neil A. Martinson, Limakatso Lebina & Kgaugelo Patricia Kgasago

  10. Johns Hopkins University Center for TB Research, Baltimore, MD, USA

    Neil A. Martinson

  11. MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

    Kathleen Kahn, Jacques D. du Toit, Jacques du Toit, Francesc Xavier Gómez-Olivé, Stephen Tollman & Floidy Wafawanaka

  12. Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa

    Penny L. Moore

Authors
  1. Kaiyuan Sun
    View author publications

    Search author on:PubMed Google Scholar

  2. Jinal N. Bhiman
    View author publications

    Search author on:PubMed Google Scholar

  3. Stefano Tempia
    View author publications

    Search author on:PubMed Google Scholar

  4. Jackie Kleynhans
    View author publications

    Search author on:PubMed Google Scholar

  5. Vimbai Sharon Madzorera
    View author publications

    Search author on:PubMed Google Scholar

  6. Qiniso Mkhize
    View author publications

    Search author on:PubMed Google Scholar

  7. Haajira Kaldine
    View author publications

    Search author on:PubMed Google Scholar

  8. Meredith L. McMorrow
    View author publications

    Search author on:PubMed Google Scholar

  9. Nicole Wolter
    View author publications

    Search author on:PubMed Google Scholar

  10. Jocelyn Moyes
    View author publications

    Search author on:PubMed Google Scholar

  11. Maimuna Carrim
    View author publications

    Search author on:PubMed Google Scholar

  12. Neil A. Martinson
    View author publications

    Search author on:PubMed Google Scholar

  13. Kathleen Kahn
    View author publications

    Search author on:PubMed Google Scholar

  14. Limakatso Lebina
    View author publications

    Search author on:PubMed Google Scholar

  15. Jacques D. du Toit
    View author publications

    Search author on:PubMed Google Scholar

  16. Thulisa Mkhencele
    View author publications

    Search author on:PubMed Google Scholar

  17. Anne von Gottberg
    View author publications

    Search author on:PubMed Google Scholar

  18. Cécile Viboud
    View author publications

    Search author on:PubMed Google Scholar

  19. Penny L. Moore
    View author publications

    Search author on:PubMed Google Scholar

  20. Cheryl Cohen
    View author publications

    Search author on:PubMed Google Scholar

Consortia

PHIRST-C group

  • Jinal N. Bhiman
  • , Amelia Buys
  • , Maimuna Carrim
  • , Cheryl Cohen
  • , Linda de Gouveia
  • , Mignon du Plessis
  • , Jacques du Toit
  • , Francesc Xavier Gómez-Olivé
  • , Kathleen Kahn
  • , Kgaugelo Patricia Kgasago
  • , Jackie Kleynhans
  • , Retshidisitswe Kotane
  • , Limakatso Lebina
  • , Neil A. Martinson
  • , Meredith L. McMorrow
  • , Tumelo Moloantoa
  • , Jocelyn Moyes
  • , Stefano Tempia
  • , Stephen Tollman
  • , Anne von Gottberg
  • , Floidy Wafawanaka
  •  & Nicole Wolter

Contributions

K.S., J.N.B., S.T., J.K., A.v.G., M.L.M., N.W., J.M., N.A.M., K.K., L.L., C.V., P.L.M. and C.C. designed the experiments. J.N.B., C.C., J.K., P.L.M. and S.T. accessed and verified the underlying data. J.N.B., S.T., J.K., V.S.M., Q.M., H.K., A.v.G., M.L.M., N.W., J.M., M.C., N.A.M., K.K., L.L., J.d.T., T.M., P.L.M. and C.C. collected the data and performed laboratory experiments. K.S., J.N.B., S.T., J.K., A.v.G., M.L.M., N.W., J.M., M.C., N.A.M., K.K., L.L., J.d.T., T.M., C.V. and C.C. analyzed the data and interpreted the results. K.S., J.N.B., C.V., P.L.M. and C.C. drafted the paper. All authors critically reviewed the paper. All authors had access to all the data reported in the study.

Corresponding authors

Correspondence to Kaiyuan Sun or Cheryl Cohen.

Ethics declarations

Competing interests

C.C. has received grant support from Sanofi Pasteur, the US Centers for Disease Control and Prevention, the Bill & Melinda Gates Foundation, the Taskforce for Global Health, the Wellcome Trust and the South African Medical Research Council. A.v.G. has received grant support from Sanofi Pasteur, Pfizer related to pneumococcal vaccine, the US Centers for Disease Control and Prevention and the Bill & Melinda Gates Foundation. N.W. reports grants from Sanofi Pasteur and the Bill & Melinda Gates Foundation. N.A.M. has received an institutional grant from Pfizer to conduct research in patients with pneumonia and from Roche to collect specimens to assess a novel tuberculosis assay. J.M. has received grant support from Sanofi Pasteur. The other authors declare no competing interests.

Ethics

The PHIRST-C protocol was approved by the University of Witwatersrand Human Research Ethics Committee (ref. 150808), and the US Centers for Disease Control and Prevention’s Institutional Review Board relied on the local review (no. 6840). The protocol was registered on ClinicalTrials.gov on 6 August 2015 and updated on 30 December 2020 (NCT02519803). Participants receive grocery store vouchers of ZAR50 (USD 3) per visit to compensate for time required for specimen collection and interview. All participants provided written informed consent for study participation. For minors, consent was obtained from the parent or guardian.

Peer review

Peer review information

Nature Medicine thanks Sophie Valkenburg, Bo Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alison Farrell, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Flowchart of participants included in the Delta-wave subgroup analysis.

Grey boxes represent participants excluded from the Delta-wave subgroup analysis. *Based on a previously published study31. †Household with more than 6 infected individuals would be computationally intractable to track all possible transmission chain configurations (Methods Section 3).

Extended Data Fig. 2 Flowchart of participants included in the Omicron-wave subgroup analysis.

Grey boxes represent participants excluded from the Omicron-wave subgroup analysis. *Based on a previously published study31. †Household with more than 6 infected individuals would be computationally intractable to track all possible transmission chain configurations (Methods Section 3).

Extended Data Fig. 3 D614G spike binding antibody (bAb) level for the Delta wave and the Omicron wave analysis.

a, for Delta wave subgroup, the distribution of the peak bAb level to BD5 (light blue dots) and the D614G spike bAb level at BD5 (dark blue dots), among individuals who had one prior SARS-CoV-2 infection before blood draw 5. Each dot represents one individual, with two measurements of the same individual connected through a gray line. OD: absorbance at 450 nm, measured in optical density; \(\bar{\text{OD}}:\) the average of OD; \(\bar{\text{OD}}:\) the average drop of OD. b, for Delta wave subgroup, the distribution of the peak D614G spike bAb up to BD5, stratified by individuals who were infected during the Delta wave (solid bar) vs those who were not infected (dashed bar). Independent samples t-test (two-sided) is used to determine the statistical significance (anti reported on the legend) of difference between the \(\bar{\text{OD}}\) of the two groups. c, same as b but for D614G spike bAb level at BD5. d, same as b but for \(\Delta {bA}{b}^{W}\). e, for Omicron wave subgroup, the distribution of the peak bAb level to BD8 (light red dots) and the D614G spike bAb level at BD8 (dark red dots), among individuals who had one prior SARS-CoV-2 infection before BD8. Each dot represents one individual, with two measurements of the same individual connected through a gray line. f, for the Omicron wave subgroup, the distribution of the D614G spike bAb level at BD8, stratified by individuals who were infected during the Omicron wave (solid bar) vs those who were not infected (dashed bar). Independent samples t-test (two-sided) is used to determine the statistical significance (p-value reported on the legend) of difference between the \(\bar{\text{OD}}\) s of the two groups. g, same as f but for D614G spike bAb level at BD8. h, same as f but for \(\Delta {bA}{b}^{W}\).

Extended Data Table 1 Positivity rate of different serologic assays by the variant type of prior exposure for the Delta and Omicron wave subgroup
Full size table
Extended Data Table 2 Mediation analysis for nAbs as CoPs against serologically ascertained Delta and Omicron wave infections, with a variant-specific model for direct effect
Full size table
Extended Data Table 3 Mediation analysis for nAbs as CoPs against Delta (ascertained by both serology and PCR) and Omicron wave infections, with a waning model for direct effect
Full size table
Extended Data Table 4 Mediation analysis for D614G spike binding antibody as CoPs against serologically ascertained Delta and Omicron wave infections, with a variant-specific model for direct effect
Full size table

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, K., Bhiman, J.N., Tempia, S. et al. SARS-CoV-2 correlates of protection from infection against variants of concern. Nat Med 30, 2805–2812 (2024). https://doi.org/10.1038/s41591-024-03131-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41591-024-03131-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing