SIMPLICITY is an agent-based, multi-scale mathematical model to study SARS-CoV-2 intra- and between-host evolution.

Computational tools are frequently used to describe pathogen evolutionary dynamics either within infected hosts or at the population level. However, there is a lack of models that capture the complex interplay between within-host and between-host evolutionary dynamics, leaving a knowledge gap with regard to realistic evolutionary dynamics. We present SIMPLICITY, a multi-scale mathematical model that combines within-host disease progression and viral evolution with a population-level model of virus transmission and immune evasion. We parameterize SIMPLICITY based on SARS-CoV-2 within-host viral dynamics, observed evolutionary rates, and dynamics of immune waning. We then apply it to study the dynamics and mechanisms driving SARS-CoV-2 evolution at the population level. We compare a baseline toy model of gradually increasing transmission fitness with an adaptive fitness landscape model that accounts for infection history and immune waning. Our simulations demonstrate that escape from population immunity generates evolutionary dynamics encompassing selective sweeps, which resemble SARS-CoV-2 evolution.