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Probabilistic classification of anti-SARS-CoV-2 antibody responses improves seroprevalence estimates
Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden.
Cambridge Institute of Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge, United Kingdom.
Department of Infectious Diseases, Karolinska University Hospital, Huddinge, Sweden.
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2022 (English)In: Clinical & Translational Immunology (CTI), E-ISSN 2050-0068, Vol. 11, no 3, article id e1379Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022. Vol. 11, no 3, article id e1379
Keywords [en]
antibody responses, antibody testing, COVID-19, probability, SARS-CoV-2, serology
National Category
Immunology in the medical area Infectious Medicine
Identifiers
URN: urn:nbn:se:umu:diva-193709DOI: 10.1002/cti2.1379ISI: 000773551400002PubMedID: 35284072Scopus ID: 2-s2.0-85127258616OAI: oai:DiVA.org:umu-193709DiVA, id: diva2:1653906
Funder
NIH (National Institutes of Health), 400 SUM1A44462‐02Wellcome trust, 107881Wellcome trust, 220788Swedish Research Council, 2017‐00968Available from: 2022-04-25 Created: 2022-04-25 Last updated: 2022-12-09Bibliographically approved

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Vikström, LinneaForsell, Mattias N. E.

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