Prevalence estimation and optimal classification methods to account for time dependence in antibody levels.
J Theor Biol
; 559: 111375, 2023 02 21.
Article
in English
| MEDLINE | ID: covidwho-2150215
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
SARS-CoV-2
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
J Theor Biol
Year:
2023
Document Type:
Article
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