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

arXiv: https://arxiv.org/abs/2208.02127 · HTML: https://mlsanity.com/paper/2208.02127/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2022.

## Built on by (most cited first, 2 of 2)

- [Analysis of Diagnostics (Part I): Prevalence, Uncertainty Quantification, and Machine Learning](https://mlsanity.com/paper/2309.00645/) (2023)
- [Inequalities for Optimization of Classification Algorithms: A Perspective Motivated by Diagnostic Testing](https://mlsanity.com/paper/2508.01065/) (2025)

## Abstract (arXiv)

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.