Peiter, C. | Search bioRxiv

bioRxiv2026

Linking Host Covariates to COVID-19 Vaccine-Induced Antibody Dynamics in a Large Healthcare Worker Cohort

Antibody responses to COVID-19 vaccination vary widely across individuals, but the factors shaping these differences and their relation to underlying host-response processes are still not fully understood. Here, we combined longitudinal serological data from 4,553 healthcare workers with a nonlinear mixed-effects model to quantify determinants of vaccine-induced antibody dynamics. This framework captured both population-level trends and inter-individual variability in anti-spike antibody trajectories after vaccination. We found that age, sex, and pre-vaccine infection significantly influenced response dynamics. Male sex and increasing age were associated with weaker initial antibody production but slower waning, while individuals with a pre-vaccine infection had pre-existing anti-spike antibodies and slower initial antibody production and faster waning. These effects translated into substantial long-term differences in predicted antibody levels, reaching up to a 27% decrease in antibody levels one year after the third vaccination. Our results provide a dynamic view of the determinants of vaccine-induced humoral immunity and establish a framework for analyzing heterogeneous immune responses in large longitudinal cohorts.

Systems Biology↗

bioRxiv2023

An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation

Non-linear mixed-effects models are a powerful tool for studying heterogeneous populations in various fields, including biology, medicine, economics, and engineering. Here, the aim is to find a distribution over the parameters that describe the whole population using a model that can generate simulations for an individual of that population. However, fitting these distributions to data is computationally challenging if the description of individuals is complex and the population is large. To address this issue, we propose a novel machine learning-based approach: We exploit neural density estimation based on conditional normalizing flows to approximate individual-specific posterior distributions in an amortized fashion, thereby allowing for efficient inference of population parameters. Applying this approach to problems from cell biology and pharmacology, we demonstrate its unseen flexibility and scalability to large data sets compared to established methods.

systems biology↗