Probabilistic classification of anti‐SARS‐CoV‐2 antibody responses improves seroprevalence estimates. Issue 3 (2nd March 2022)

• Record Type: Journal Article
• Authors: Castro Dopico, Xaquin; Muschiol, Sandra; Grinberg, Nastasiya F; Aleman, Soo; Sheward, Daniel J; Hanke, Leo; Ahl, Marcus; Vikström, Linnea; Forsell, Mattias; Coquet, Jonathan M; McInerney, Gerald; Dillner, Joakim; Bogdanovic, Gordana; Murrell, Ben; Albert, Jan; Wallace, Chris; Karlsson Hedestam, Gunilla B
• Is Part Of: Clinical & translational immunology. Volume 11:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-02
• Subjects: antibody responses -- antibody testing -- COVID‐19 -- probability -- SARS‐CoV‐2 -- serology; Immunologic diseases -- Periodicals; Immunology -- Periodicals; Clinical medicine -- Periodicals; Immune System Diseases -- therapy; Immunotherapy; Immunologic Factors -- therapeutic use; Translational Medical Research; Molecular Targeted Therapy; Clinical medicine; Immunologic diseases; Immunology; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.079
• DOI: 10.1002/cti2.1379
• Languages: English
• ISSNs: 2050-0068

Abstract

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. Abstract : Correctly classifying low‐titer antibody responses is challenging using conventional assay cutoffs. To address this issue, we trained suitable probabilistic learners to assign likelihood of seropositivity. These more quantitative methods improve seroprevalence estimates and have potential application to the clinical setting.