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Area of Science:

  • Immunology
  • Epidemiology
  • Mathematical Biology

Background:

  • Antibody levels change over time after infections or vaccinations.
  • The sequence and timing of immune events significantly impact antibody dynamics.
  • Current models struggle to capture population-level antibody responses due to individual variability and time-dependent disease prevalence.

Purpose of the Study:

  • To develop a novel mathematical framework for modeling antibody responses to arbitrary sequences of immune events.
  • To characterize individual immune event histories, termed personal trajectories.
  • To provide a tool for analyzing population-level immune measurements and informing public health strategies.

Main Methods:

  • Developed a time-inhomogeneous Markov chain model for immune event transitions.
  • Integrated a probabilistic framework to model post-event antibody kinetics.
  • Constructed probability density models for population response using conditional probability and the law of total probability.
  • Simultaneously tracked immune state and antibody response.

Main Results:

  • Introduced the first antibody response modeling framework for an arbitrary number of multiclass immune events.
  • Applied the framework to longitudinal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data.
  • Demonstrated the model's generalizability to other diseases with waning immunity (e.g., influenza, RSV).

Conclusions:

  • The novel framework offers a comprehensive understanding of antibody kinetics for infectious diseases.
  • Enables effective analysis of natural immunity and vaccination effectiveness.
  • Facilitates prediction of missed immune events and informs optimal vaccine booster timing.