A Bayesian modelling framework for inference of latent infection risk patterns from virus neutralisation assay titration data

AO_SCPLOWBSTRACTC_SCPLOWSerological assays remain the standard experimental approach for estimating the cumulative incidence of a pathogen and monitoring population immunity. The predominant approach for analysing serum titration data from virus neutralisation assays uses a nearly century-old interpolation-based method which neglects inherent imperfections in the assay and produces estimates with no measure of uncertainty. We introduce a two-part Bayesian modelling framework to estimate the underlying antibody concentrations in the raw serum samples taken from serosurveyed individuals, to improve the interpretation of serological data over age. First, we develop a mechanistic Bayesian model for serum antibody titration data that estimates latent antibody concentrations while accounting for assay variability and quantifying uncertainty. Second, we propagate this uncertainty into an age-structured serocatalytic model by integrating over posterior draws of individual antibody concentrations, allowing joint inference on latent serostate membership, force of infection, and serological waning rate. We use this framework to explore the dynamics of infection and immunity for three enterovirus serotypes: enteroviruses A71 (EV-A71) and D68 (EV-D68) and coxsackievirus A6 (CVA6). These serotypes are leading causes of outbreaks of severe respiratory illness and hand, foot, and mouth disease. Applying these approaches to three cross-sectional serosurveys, we estimated consistently higher and more persistent antibody concentrations throughout life for EV-D68 compared to EV-A71 and CVA6. Our analysis suggests that the proportion of recently infected individuals (i.e. individuals with high estimated antibody concentration levels given their age) peaks around 25% by age 7 years for both EV-A71 and CVA6 before gradually declining with age. In contrast, for EV-D68 the inferred proportion of the population in the infected state exceeds 50% by age 9 years and continues to grow with age. We also estimate that EV-D68 antibody concentration levels are higher than those of the other two serotypes, with the force of infection estimated to be highest in early childhood and declining more gradually with age than for EV-A71 and CVA6. These estimates are different to previous estimates found in the literature. Our inferential framework uncovers the wide-ranging variation in antibody levels that are often obscured by conventional endpoint titre estimation methods. We demonstrate that our framework can infer infection rates without relying on predetermined seropositivity cut-offs and without making explicit assumptions of virus-specific infection mechanisms. Author summarySerological tests measure antibody levels in blood to show how widely a virus has spread and how well populations are protected. Titre-based tests dilute blood samples in steps, mix these dilutions with virus, and add the mixture to living cells; the titre is the highest dilution where antibodies still protect cells from infection. Traditional analyses overlook test imperfections. We present a new two-part Bayesian framework to estimate antibody levels and track age-related exposure to infection. First, we estimate underlying antibody concentrations while accounting for uncertainty, then use these estimates in another model to infer age-specific transmission of three common viruses - EV-A71, EV-D68, and CVA6. Our results show that EV-D68 infections may be more common, especially in children, compared to the other viruses. This new approach provides a clearer picture of the dynamics of seroconversion, without relying on arbitrary thresholds, helping to improve public health monitoring and responses.