Generalizable principles underly the evolution and shaping of population level SARS-CoV-2 immune markers

Abstract While individual level immune responses to SARS-CoV-2 are well characterized, population immunity and the factors that drive population immune markers are largely undescribed. In this study, we examined spike antibody responses that track with infection risk amongst a household cohort in the northwest and southeast of the Dominican Republic. Our sampling period was from Aug 2021 to Nov 2022, capturing sequential waves of Delta, BA.1, BA.2, and BA.4/5 transmission. We show that population antibody levels normalized from a highly irregular to a Gaussian distribution, driven by accrued infections and antibody boosting among individuals with lower baseline immunity and waning among those with higher immunity, irrespective of interval vaccination. Using a limited number of predictor variables and out-of-sample validation methods we were able to predict S-antibody changes at the Nov 2022 timepoint with a high degree of accuracy (Pearson’s correlation coefficient 0.95 for predicted vs observed change). S-antibody level at the baseline sampling timepoint was by far the most influential predictor, demonstrated by a strong association when used as the only predictor variable (Pearson’s correlation coefficient 0.92). Findings were stable across geographically distinct study regions, suggesting drivers of immune dynamics apply equally across the Dominican Republic, and likely other countries with comparable transmission profiles. Our results suggest that given sufficient transmission, generalizable and discernable principles underly population immune dynamics. We propose that these findings can be used to delineate immune dynamics in other settings, inform transmission modeling, and guide public health priorities for SARS-CoV-2, and potentially other non-immune sterilizing emerging pathogens.