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 May 13, 2025)
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Prevalence Estimation and Optimal Classification Scheme to Account for Time Dependence in Antibody Levels
Published
Author(s)
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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
Pub Type
Journals
Keywords
Time-dependent classification, SARS-CoV-2, Prevalence estimation, Optimization, Antibody testing
Citation
Issues
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Created February 21, 2023, Updated May 24, 2024