Predicting vaccine effectiveness against severe COVID-19 over time and against variants: a meta-analysis

Deborah Cromer (), Megan Steain, Arnold Reynaldi, Timothy E. Schlub, Shanchita R. Khan, Sarah C. Sasson, Stephen J. Kent, David S. Khoury and Miles P. Davenport
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Deborah Cromer: University of New South Wales
Megan Steain: The University of Sydney
Arnold Reynaldi: University of New South Wales
Timothy E. Schlub: University of New South Wales
Shanchita R. Khan: University of New South Wales
Sarah C. Sasson: University of New South Wales
Stephen J. Kent: University of Melbourne at the Peter Doherty Institute for Infection and Immunity
David S. Khoury: University of New South Wales
Miles P. Davenport: University of New South Wales

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract Vaccine protection from symptomatic SARS-CoV-2 infection has been shown to be strongly correlated with neutralising antibody titres; however, this has not yet been demonstrated for severe COVID-19. To explore whether this relationship also holds for severe COVID-19, we performed a systematic search for studies reporting on protection against different SARS-CoV-2 clinical endpoints and extracted data from 15 studies. Since matched neutralising antibody titres were not available, we used the vaccine regimen, time since vaccination and variant of concern to predict corresponding neutralising antibody titres. We then compared the observed vaccine effectiveness reported in these studies to the protection predicted by a previously published model of the relationship between neutralising antibody titre and vaccine effectiveness against severe COVID-19. We find that predicted neutralising antibody titres are strongly correlated with observed vaccine effectiveness against symptomatic (Spearman $$\rho$$ ρ = 0.95, p

Date: 2023
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DOI: 10.1038/s41467-023-37176-7

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