Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.

Population-level measures of seropositivity are critical for understanding the epidemiology of an emerging pathogen, yet most antibody tests apply a strict cutoff for seropositivity that is not learnt in a data-driven manner, leading to uncertainty when classifying low-titer responses. To improve upon this, we evaluated cutoff-independent methods for their ability to assign likelihood of SARS-CoV-2 seropositivity to individual samples. Using robust ELISAs based on SARS-CoV-2 spike (S) and the receptor-binding domain (RBD), we profiled antibody responses in a group of SARS-CoV-2 PCR+ individuals ( In the training data from confirmed ancestral SARS-CoV-2 infections, 99% of participants had detectable anti-S and -RBD IgG in the circulation, with titers differing > 1000-fold between persons. In data of otherwise healthy individuals, 7.2% ( Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.

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© 2022 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.

Déclaration de conflit d'intérêts

The study authors declare no competing financial interests that could compromise the study. CW also receives funding from GlaxoSmithKline and Merck Sharp & Dohme; these funders had no role in the design, analysis or interpretation of this study. The views expressed are those of the authors.