2022/08/03 by Prajakta Bedekar, Bedekar, Prajakta, Anthony J. Kearsley +3 · 2 citations
Mathematics · Medicine · #Applications (stat.AP) #COVID-19 epidemiological studies #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Influenza Virus Research Studies #Optimization and Control (math.OC) #Probability (math.PR) #Quantitative Methods (q-bio.QM) #SARS-CoV-2 and COVID-19 Research
paper · pdf · doi:10.48550/arxiv.2208.02127
openalex publication_date 2022/08/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.