A Bayesian mixture model accounting for individual heterogeneity in response to pathogenic infection

Abstract

The analysis of multivariate serological data derived from blood serum samples and tested for the presence of  antibodies against multiple pathogens gained attention in recent years. Despite the common use of a so-called  threshold approach to classify individuals as seronegative or -positive, limitations of such an approach have been reported in the literature, with the subjective choice of the threshold being the most important. Here, we consider a Bayesian mixture approach to model continuous IgG antibody concentrations directly while accounting for the presence of individual heterogeneity and implied association between antibody titer levels for two infections. We fitted the proposed model to Belgian bivariate serological data on the varicella-zoster virus (VZV) and parvovirus B19 (PVB19). Given the existing body of evidence with respect to possible reinfections with PVB19, we investigated whether models explicitly accounting for waning of humoral immunity improved model fit. Our results showed that although after a steep rise with age, the observed seroprevalence for PVB19 decreases between the ages of 20 and 40, the mean IgG antibody concentration remains constant with age among individuals in the seropositive component. This could provide evidence of a direct impact of reinfections with PVB19 on the observed IgG antibody levels, while individuals with loss of humoral immunity after natural infection imply an increase in susceptibility. For VZV, the mean IgG antibody levels slightly decrease with increasing age among seropositive individuals, indicating only very limited waning of humoral immunity as age-dependent seroprevalence estimates are monotonically increasing with increasing age. In general, based on our analyses, we showed that mixture models provide additional insights concerning the waning of humoral immunity as compared to more traditional frailty approaches, which focus on estimating the seroprevalence solely while the model is sufficiently flexible to capture observed dynamics in IgG antibody decay.