Neutralising antibodies predict protection from severe COVID-19
Open accessAbstract
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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Neutralizing antibodies can also protect against severe disease, albeit at lower concentrations than needed to protect against symptomatic disease.
“It has been postulated that neutralizing antibodies can also protect against severe disease, albeit at lower concentrations than needed to protect against symptomatic disease [36]”
From Assessing COVID-19 vaccine effectiveness against Omicron subvariants: Report from a meeting of the World Health Organization (2023) · cites this paper as [36]
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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)
- PMC
- PMC10036966
- OpenAlex
- W4360871696
- Cited by
- 139
- References
- 53
- Citations by year
- 2026: 15 · 2025: 40 · 2024: 50 · 2023: 30 · 2022: 3 · 2021: 1
- FWCI
- 6.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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