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

TL;DR

SIMPLICITY is a new model that combines within-host and population-level dynamics to study how SARS-CoV-2 evolves over time.

Contribution

The novelty lies in integrating within-host and between-host evolution into a single multi-scale model for SARS-CoV-2.

Findings

Escape from population immunity leads to selective sweeps similar to real-world SARS-CoV-2 evolution.

An adaptive fitness landscape model better captures immune evasion dynamics than a baseline model.

The model incorporates viral dynamics, immune waning, and transmission to simulate realistic evolutionary patterns.

Abstract

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.

SIMPLICITY models multi-scale SARS-CoV-2 evolution by integrating within-host viral dynamics and population-level transmission. Our adaptive fitness landscape model reveals how immune escape prompts evolutionary dynamics akin to the real-world evolution of SARS-CoV-2.

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