Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution

TL;DR

This paper introduces a multi-scale pandemic model that integrates pathogen evolution, human interactions, and public health responses, effectively capturing COVID-19 dynamics and virus evolution with computational efficiency.

Contribution

It presents a novel stochastic agent-based framework combining phylodynamics and within-host evolution, capable of simulating feedback across multiple pandemic scales.

Findings

Model accurately replicates COVID-19 pandemic features

Captures punctuated SARS-CoV-2 evolution from genomic data

Maintains scalability and computational efficiency

Abstract

Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of models simulating pandemics across three scales: (1) pathogen evolution, often punctuated by the rapid emergence of new variants, (2) human interactions within a heterogeneous population, and (3) public health responses which constrain individual actions to control the disease transmission. We then present a pandemic modelling framework satisfying these requirements and capable of simulating feedback loops between dynamics unfolding at these different scales. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model that incorporates within-host pathogen evolution. It is validated with a case study, modelling the punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, which captures a large heterogeneous population. We demonstrate that the model replicates the essential features of the COVID-19 pandemic and virus evolution, while retaining computational tractability and scalability.