Speaker
Description
There is a significant gap in the theoretical and experimental understanding of the time- dependent, person-specific viral response to infections and vaccinations. Such events pose considerable modeling challenges, and a mathematical characterization of antibody kinetics is crucial for tackling future questions related to herd immunity and optimal decision-making regarding vaccination policy. We address the important task of tracking the antibody response to multiple infections or vaccinations or combinations of the two immune events, which was previously unresolved. We describe event-to-event transitions for post-infection or post-vaccination antibody kinetics by employing a novel combination of probability distribution models with a time-inhomogeneous Markov chain framework. This approach is ideal to model sequences of infections and vaccinations and predict missed events. We validate our work using SARS-CoV-2 antibody measurements from two datasets with different measurement systems. This work paves the way to future frameworks that can analyze the protective power of natural immunity or vaccination, predict missed immune events at an individual level, and inform booster timing recommendations for both emerging and endemic diseases.
