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

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.

Vaccination Against SARS-CoV-2 in Lung Transplant Recipients: Immunogenicity, Efficacy and Safety

“…The interpretation of the vaccination efficacy based on serological responses bears certain limitations, such as highly variable antibody titers and kinetics among individuals ( 15 , 16 ). The strict cutoff values that are being applied by most antibody tests may not efficiently classify the seropositivity as negative control values may overlap true positive low-titer values ( 17 ). Furthermore, it is crucial to investigate virus-neutralizing antibody responses in vaccinated individuals, including those after lung transplantation.…”

At-home sampling to meet geographical challenges for serological assessment of SARS-CoV-2 exposure in a rural region of northern Sweden, March to May 2021: a retrospective cohort study

“…There, they found that in house Region Västerbotten had the highest sensitivity and specificity of all assays tested [23]. For this reason, the assay was subsequently used on venous blood samples for probabilistic classification of anti-SARS-CoV-2 antibody responses [24].…”

Serological assessment of SARS-CoV-2 exposure in northern Sweden by the use of at-home sampling to meet geographical challenges in rural regions

“…As demonstrated by us, and by independent studies by the Swedish Public Health Agency, our assay for detection of S-specific IgG had the highest standard with regards to sensitivity and specificity (17). For this reason, the assays was subsequently used on venous blood samples for probabilistic classification of anti-SARS-CoV-2 antibody responses (18).…”