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

Published

Author(s)

Prajakta Bedekar, Anthony J. Kearsley, Paul Patrone

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 from a random sample changes due to changing prevalence (i.e., seroprevalence, or fraction of individuals exhibiting an immune response) of the disease in the population. 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 the error associated with classifying a value as positive (history of infection) or negative (no such history) on a given day since the start of the pandemic. 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.
Citation
Journal of Theoretical Biology
Volume
559
Issue
111375

Keywords

Time-dependent classification, SARS-CoV-2, Prevalence estimation, Optimization, Antibody testing

Citation

Bedekar, P. , Kearsley, A. and Patrone, P. (2023), Prevalence Estimation and Optimal Classification Scheme to Account for Time Dependence in Antibody Levels, Journal of Theoretical Biology, [online], https://doi.org/10.1016/j.jtbi.2022.111375, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935054 (Accessed June 25, 2024)

Issues

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Created February 21, 2023, Updated May 24, 2024