Prevalence estimation and optimal classification methods to account for time dependence in antibody levels.
J Theor Biol
; 559: 111375, 2023 02 21.
Artículo
en Inglés
| 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.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
SARS-CoV-2
/
COVID-19
Tipo de estudio:
Estudio de cohorte
/
Estudios diagnósticos
/
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Humanos
Idioma:
Inglés
Revista:
J Theor Biol
Año:
2023
Tipo del documento:
Artículo
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