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

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.