Convergence of SARS-CoV-2 spike antibody levels to a population immune setpoint

Individual immune responses to SARS-CoV-2 are well-studied, while the combined effect of these responses on population-level immune dynamics remains poorly understood. Given the key role of population immunity on pathogen transmission, delineation of the factors that drive population immune evolution has critical public health implications. We enrolled individuals 5 years and older selected using a multistage cluster survey approach in the Northwest and Southeast of the Dominican Republic. Paired blood samples were collected mid-pandemic (Aug 2021) and late pandemic (Nov 2022). We measured serum pan-immunoglobulin antibodies against the SARS-CoV-2 spike protein. Generalized Additive Models (GAMs) and random forest models were used to analyze the relationship between changes in antibody levels and various predictor variables. Principal component analysis and partial dependence plots further explored the relationships between predictors and antibody changes. We found a transformation in the distribution of antibody levels from an irregular to a normalized single peak Gaussian distribution that was driven by titre-dependent boosting. This led to the convergence of antibody levels around a common immune setpoint, irrespective of baseline titres and vaccination profile. Our results suggest that titre-dependent kinetics driven by widespread transmission direct the evolution of population immunity in a consistent manner. These findings have implications for targeted vaccination strategies and improved modeling of future transmission, providing a preliminary blueprint for understanding population immune dynamics that could guide public health and vaccine policy for SARS-CoV-2 and potentially other pathogens. The study was primarily funded by the Centers for Disease Control and Prevention grant U01GH002238 (EN). Salary support was provided by Wellcome Trust grant 206250/Z/17/Z (AK) and the Australian National Health and Medical Research Council Investigator grant APP1158469 (CLL).