Prevalence Estimation and Optimal Classification Methods to Account for Time Dependence in Antibody Levels

TL;DR

This paper introduces a mathematical model and adaptive classification method for serology testing that accounts for time-dependent antibody responses and changing disease prevalence, improving accuracy in estimating infection history.

Contribution

It develops a novel adaptive prevalence estimation model and a time-dependent probabilistic classification scheme that optimally minimizes errors considering antibody dynamics.

Findings

Validated with real-world and synthetic SARS-CoV-2 data

Demonstrated improved accuracy over static models

Discussed requirements for longitudinal studies

Abstract

Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement changes due to prevalence as the disease progresses. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes error. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.