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)

SUBMITTER: Castro Dopico X 

PROVIDER: S-EPMC8891432 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature