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Prevalence estimation and optimal classification methods to account for time dependence in antibody levels.
- Source :
-
Journal of Theoretical Biology . Feb2023, Vol. 559, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
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. • Personal antibody levels and global disease prevalence depend on time. • Time dependence as a convolution of dual timeline effects. • Estimate prevalence using the unbiased estimator, then classify. • Optimal classification domains change with time. • Can be applied in a low prevalence setting and for different assays, infections. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00225193
- Volume :
- 559
- Database :
- Academic Search Index
- Journal :
- Journal of Theoretical Biology
- Publication Type :
- Academic Journal
- Accession number :
- 160963444
- Full Text :
- https://doi.org/10.1016/j.jtbi.2022.111375