1. Bayesian approach to infer the duration of antibody seropositivity and neutralizing responses to SARS-CoV-2
- Author
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Royer-Carenzi, Manuela, Freyermuth, Jean-Marc, Institut de Mathématiques de Marseille (I2M), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
[STAT]Statistics [stat] ,viral neutralization ,posterior predictive distribution ,antibody antigen ,[SDV]Life Sciences [q-bio] ,COVID-19 ,Bayesian mixture model - Abstract
Estimating the duration of natural immunity induced by SARS-CoV2 infection is crucial in health policy strategies. A patient infected by the SARS-CoV2 quickly produces three antibody isotypes IgM, IgG, and IgA that reveal an infection. In this paper, we use a Bayesian twocomponent mixture of random coefficient model to capture the longitudinal/temporal evolution of antibody levels, as well as viral neutralization on the dataset reported by Seow et al. in [1]. We observe that the more severe the symptoms, the more intense antibodies and immunity responses. And their decline is decelerated with the severity. Moreover, it appears that viral neutralization is best predicted by the level of IgM or IgA antibody, rather than by IgG level. Furthermore, our model is particularly suitable to estimate the Probability of being Out of Detection. Thus, we observe that although antibodies persist for up to 5 months in the plasma, the probability of becoming undetectable exceeds 50% after 3 months.
- Published
- 2023