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PreprintCited by 139SARS-CoV-2 and COVID-19 ResearchCOVID-19 epidemiological studiesCOVID-19 Clinical Research Studies

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Neutralising antibodies predict protection from severe COVID-19

Open access
Cromer, Deborah, Steain, Megan, Reynaldi, Arnold, Schlub, Timothy E, Sasson, Sarah C, Kent, Stephen J, Khoury, David S, Davenport, Miles Philip·2022·Nature Communications
DOI 10.1101/2022.06.09.22275942PMC PMC10036966
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Abstract

Background: Vaccine protection from COVID-19 has been shown to decline with time-since-vaccination and against SARS-CoV-2 variants. Protection against severe COVID-19 is higher than against symptomatic infection, and also appears relatively preserved over time and against variants. Although Protection protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres, however, this relationship has been is less well described for severe COVID-19. Protection against severe COVID-19 is higher than against symptomatic infection, and also appears relatively preserved over time and against variants. Here we analyse whether neutralising antibody titre remains predictive of protection against severe COVID-19 in the face of waning neutralising antibody levels and emerging variants. Methods: We extracted data from 15 studies reporting on protection against a range of SARS-CoV-2 clinical endpoints ("any infection", "symptomatic infection" and "severe COVID-19"). We then estimated the concurrent neutralising antibody titres using existing parameters on vaccine potency, neutralising antibody decay, and loss of recognition of variants and investigated the relationship between neutralising antibody titre and vaccine effectiveness against severe COVID-19. Findings: Predicted neutralising antibody titres are strongly correlated with vaccine effectiveness against symptomatic and severe COVID-19 (Spearman rho = .94 and 0.63 respectively, p<.001 for both), consistent with previous estimates of the relationship between neutralisation and protection. Indeed 82% (137 of 167) of reported values of protection against severe COVID-19 across a range of vaccines and variants lie within the 95% confidence intervals of the published model. Interpretation: Neutralising antibody titres are predictive of vaccine effectiveness against severe COVID-19 including in realistic scenarios of waning immunity and viral variants. Funding: National Health and Medical Research Council (Australia), Medical Research Future Fund (Australia).

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Figures

Fig. 1 Summary of previously available data linking neutralising antibodies and vaccine effectiveness and data contributing to this analysis. A Previously reported relationship between neutralising antibody titre and vaccine efficacy in the prevention of symptomatic (dark red) and severe (orange) COVID-19 (reproduced from Khoury et al.4). Solid lines indicate best-fit model and shaded areas indicate 95% confidence intervals. Neutralisation titre and efficacy data used to parameterise the model are indicated as dots (95% CI indicated as whiskers). B–D Summary of the clinical studies used in this analysis (Table 1).
Fig. 1 Summary of previously available data linking neutralising antibodies and vaccine effectiveness and data contributing to this analysis. A Previously reported relationship between neutralising antibody titre and vaccine efficacy in the prevention of symptomatic (dark red) and severe (orange) COVID-19 (reproduced from Khoury et al.4). Solid lines indicate best-fit model and shaded areas indicate 95% confidence intervals. Neutralisation titre and efficacy data used to parameterise the model are indicated as dots (95% CI indicated as whiskers). B–D Summary of the clinical studies used in this analysis (Table 1).CC BY · PubMed Central
Fig. 2 Results of linear mixed effects regression model fit to vaccine effectiveness data against severe COVID-19 extracted from our systematic review. Vaccine effectiveness data against severe COVID-19 (points and whiskers for 95% CI) are shown for pre-Delta (top row, panels (A)–(D), Delta (middle row, panels (E)–(H)) and Omicron (bottom row, panels (I)–(L)) variants. Note that for panels (D) and (I), no effectiveness data was available. Opacity indicates the degree of confidence in the data as determined by the width of the confidence interval. Predicted vaccine effectiveness using a linear mixed effects regression model (solid lines) and 95% confidence intervals (shaded area) are overlaid. The figure shows effectiveness following mRNA-1273 (red), BNT162b2 (blue), any mRNA (purple) and ChAdOx-nCov-1 (pink) vaccination.
Fig. 2 Results of linear mixed effects regression model fit to vaccine effectiveness data against severe COVID-19 extracted from our systematic review. Vaccine effectiveness data against severe COVID-19 (points and whiskers for 95% CI) are shown for pre-Delta (top row, panels (A)–(D), Delta (middle row, panels (E)–(H)) and Omicron (bottom row, panels (I)–(L)) variants. Note that for panels (D) and (I), no effectiveness data was available. Opacity indicates the degree of confidence in the data as determined by the width of the confidence interval. Predicted vaccine effectiveness using a linear mixed effects regression model (solid lines) and 95% confidence intervals (shaded area) are overlaid. The figure shows effectiveness following mRNA-1273 (red), BNT162b2 (blue), any mRNA (purple) and ChAdOx-nCov-1 (pink) vaccination.CC BY · PubMed Central
Fig. 3 Correlation between estimated neutralising antibody titres and vaccine effectiveness. Correlation between estimated neutralising antibody titres (accounting for vaccine used, variant studied and time since vaccination) and clinical data for A vaccine effectiveness against symptomatic SARS-CoV-2 infection, B vaccine effectiveness against severe COVID-19. C Correlation between vaccine effectiveness against symptomatic and severe COVID-19. Solid lines indicate the predicted relationship taken from ref. 4, and shading indicates 95% CI of the model estimates. X-axis confidence intervals in (A) and (B) represent the degree of confidence in the estimate neutralising antibody titre. A breakdown of the relationship shown in panels (A) and (B) by variant and study type is shown in Supplementary Fig. S1. Figure shows effectiveness following mRNA-1273 (red), BNT162b2 (blue), any mRNA (purple) and ChAdOx-nCov-1 (pink) vaccination against pre-Delta (pink background) Delta (blue background) and Omicron (green background) variants.
Fig. 3 Correlation between estimated neutralising antibody titres and vaccine effectiveness. Correlation between estimated neutralising antibody titres (accounting for vaccine used, variant studied and time since vaccination) and clinical data for A vaccine effectiveness against symptomatic SARS-CoV-2 infection, B vaccine effectiveness against severe COVID-19. C Correlation between vaccine effectiveness against symptomatic and severe COVID-19. Solid lines indicate the predicted relationship taken from ref. 4, and shading indicates 95% CI of the model estimates. X-axis confidence intervals in (A) and (B) represent the degree of confidence in the estimate neutralising antibody titre. A breakdown of the relationship shown in panels (A) and (B) by variant and study type is shown in Supplementary Fig. S1. Figure shows effectiveness following mRNA-1273 (red), BNT162b2 (blue), any mRNA (purple) and ChAdOx-nCov-1 (pink) vaccination against pre-Delta (pink background) Delta (blue background) and Omicron (green background) variants.CC BY · PubMed Central

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Research works printed but not linked: 3

  1. [36]Walsh EE Safety and immunogenicity of two RNA-based Covid-19 vaccine candidates N. Engl. J. Med. 2020 383 2439 2450 10.1056/NEJMoa2027906 33053279 PMC7583697 · DOI · Paper page
  2. [39]Stadler, E. et al. Determinants of passive antibody effectiveness in SARS-CoV-2 infection. Preprint at medRxiv10.1101/2022.1103.1121.22272672 (2022).
  3. [47]Andrews, N. et al. Effectiveness of COVID-19 vaccines against the Omicron (B.1.1.529) variant of concern. Preprint at medRxiv10.1101/2021.1112.1114.21267615 (2021).

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Authors
Cromer, D., Steain, M., Reynaldi, A., Schlub, T. E., Sasson, S. C., Kent, S. J., Khoury, D. S., & Davenport, M. P.
Affiliations
  • UNSW Sydney (AU)
  • The University of Sydney (AU)
  • The University of Melbourne (AU)
Corresponding author
Cromer, Deborah
ORCID
Venue
Nature Communications
Publisher
openRxiv
Published
2022-06-14
Volume
14
Issue
1
Pages
1633-1633
ISSN
2041-1723 (ISSN-L)
DOI
10.1101/2022.06.09.22275942
PMC
PMC10036966
OpenAlex
W4360871696
Cited by
139
References
53
Citations by year
2026: 15 · 2025: 40 · 2024: 50 · 2023: 30 · 2022: 3
FWCI
6.39
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Nature Communications (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders
  • University of New South Wales
  • Medical Research Council
  • National Health and Medical Research Council (1149990, 1136452, 1173528)
  • Medical Research Future Fund
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
Keywords
COVID-19 vaccine effectiveness, severe COVID-19, SARS-CoV-2, neutralizing antibodies, antibody waning, SARS-CoV-2 variants of concern, time since vaccination, vaccine regimens, symptomatic infection
MeSH terms
COVID-19, SARS-CoV-2, Vaccine Efficacy, Antibodies, Viral, Humans, Vaccination, Antibodies, Neutralizing

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CoChat. (2026). Citation check: Neutralising antibodies predict protection from severe COVID-19. https://app.cochat.ai/papers/10.1101/2022.06.09.22275942
The paper
Cromer, D., Steain, M., Reynaldi, A., Schlub, T. E., Sasson, S. C., Kent, S. J., Khoury, D. S., & Davenport, M. P. (2022). Neutralising antibodies predict protection from severe COVID-19. Nature Communications. https://doi.org/10.1101/2022.06.09.22275942