Modeling and predicting individual variation in COVID-19 vaccine-elicited antibody response in the general population

As we learned during the COVID-19 pandemic, vaccines are one of the most important tools in infectious disease control. To date, an unprecedentedly large volume of high-quality data on COVID-19 vaccinations have been accumulated. For preparedness in future pandemics beyond COVID-19, these valuable datasets should be analyzed to best shape an effective vaccination strategy. We are collecting longitudinal data from a community-based cohort in Fukushima, Japan, that consists of 2,407 individuals who underwent serum sampling two or three times after a two-dose vaccination with either BNT162b2 or mRNA-1273. Using the individually reconstructed time courses of the vaccine-elicited antibody response based on mathematical modeling, we first identified basic demographic and health information that contributed to the main features of the antibody dynamics, i.e., the peak, the duration, and the area under the curve. We showed that these three features of antibody dynamics were partially explained by underlying medical conditions, adverse reactions to vaccinations, and medications, consistent with the findings of previous studies. We then applied to these factors a recently proposed computational method to optimally fit an “antibody score”, which resulted in an integer-based score that can be used as a basis for identifying individuals with higher or lower antibody titers from basic demographic and health information. The score can be easily calculated by individuals themselves or by medical practitioners. Although the sensitivity of this score is currently not very high, in the future, as more data become available, it has the potential to identify vulnerable populations and encourage them to get booster vaccinations. Our mathematical model can be extended to any kind of vaccination and therefore can form a basis for policy decisions regarding the distribution of booster vaccines to strengthen immunity in future pandemics.Author summary: This study investigates the dynamics of antibody responses following COVID-19 vaccination, with the aim of elucidating individual-level variability in immune responses. Using a mathematical model and longitudinal antibody measurements from a vaccination cohort in Fukushima, Japan, we reconstructed the time course of antibody dynamics after vaccination. The study showed that not everyone’s immune system responds equally to the vaccine. Some people may have lower antibody levels after vaccination, which could make them more susceptible to disease. We identified key factors that influence antibody responses, such as age, adverse reactions, comorbidities and medication use, and developed a personalized antibody score to predict individual antibody levels. These factors play a critical role in shaping the immune landscape following vaccination, highlighting the need for tailored approaches to assessing vaccine efficacy and recommending booster doses for vulnerable populations. The development of a personalized antibody score provides a practical tool for healthcare professionals to stratify individuals based on their predicted antibody levels, facilitating targeted interventions to enhance immune protection. The study underscores the importance of considering personal characteristics when assessing vaccine efficacy and suggests potential applications in guiding booster vaccination strategies.