Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.
Authors
Muschiol, Sandra
Ahl, Marcus
Vikström, Linnea
Publication Date
2022Journal Title
Clin Transl Immunology
ISSN
2050-0068
Publisher
Wiley
Volume
11
Issue
3
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Castro Dopico, X., Muschiol, S., Grinberg, N. F., Aleman, S., Sheward, D. J., Hanke, L., Ahl, M., et al. (2022). Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates.. Clin Transl Immunology, 11 (3) https://doi.org/10.1002/cti2.1379
Abstract
OBJECTIVES: 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. METHODS: 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 (n = 138). Using these data, we trained probabilistic learners to assign likelihood of seropositivity to test samples of unknown serostatus (n = 5100), identifying a support vector machines-linear discriminant analysis learner (SVM-LDA) suited for this purpose. RESULTS: 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% (n = 367) of samples were of uncertain serostatus, with values in the range of 3-6SD from the mean of pre-pandemic negative controls (n = 595). In contrast, SVM-LDA classified 6.4% (n = 328) of test samples as having a high likelihood (> 99% chance) of past infection, 4.5% (n = 230) to have a 50-99% likelihood, and 4.0% (n = 203) to have a 10-49% likelihood. As different probabilistic approaches were more consistent with each other than conventional SD-based methods, such tools allow for more statistically-sound seropositivity estimates in large cohorts. CONCLUSION: Probabilistic antibody testing frameworks can improve seropositivity estimates in populations with large titer variability.
Keywords
Original Article, antibody responses, antibody testing, COVID‐19, probability, SARS‐CoV‐2, serology
Sponsorship
Wellcome Trust (107881/Z/15/Z)
National Institute for Health Research (IS-BRC-1215-20014)
Wellcome Trust (220788/Z/20/Z)
Identifiers
cti21379
External DOI: https://doi.org/10.1002/cti2.1379
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334624
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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