Explicit Modelling of Antibody Levels for Infectious Disease Simulations in the Context of SARS-CoV-2

Abstract

Measurable levels of immunoglobulin G antibodies develop after infections with and vaccinations against SARS-CoV-2. These antibodies are temporarily dynamic; due to waning, antibody levels will drop below detection thresholds over time. As a result, epidemiological studies could underestimate population protection, given that antibodies are a marker for protective immunity. During the COVID-19 pandemic, multiple models predicting infection dynamics were used by policymakers to plan public health policies. Explicitly integrating antibody and waning effects into the models is crucial for reliable calculations of individual infection risk. However, only few approaches have been suggested that explicitly treat these effects. This paper presents a methodology that explicitly models antibody levels and the resulting protection against infection for individuals within an agent-based model. This approach can be integrated in general frameworks, allowing complex population studies with explicit antibody and waning effects. We demonstrate the usefulness of our model in two use cases.