# Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution

## TL;DR

This paper introduces a new multi-scale model for simulating pandemics, capturing pathogen evolution, human interactions, and public health responses, validated using SARS-CoV-2 data.

## Contribution

A novel computational framework integrating phylodynamics with agent-based modeling to simulate pandemic spread and pathogen evolution across multiple scales.

## Key findings

- The model successfully replicates key features of the SARS-CoV-2 pandemic and its punctuated evolution.
- It captures the emergence and spread of viral variants in line with real-world observations.
- The framework demonstrates how public health interventions and social interactions influence pathogen evolution.

## 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.

Accurate modelling of pandemic spread is highly challenging due to the unpredictable nature of emerging and evolving pathogens, the diversity of populations with unevenly distributed risks, and the dynamic public health interventions that adapt to rapidly changing situations. In this work, we developed a novel computational framework designed to capture the complexities of infectious disease transmission across heterogeneous populations, accounting for the impact of public health measures. Additionally, our model incorporates a phylodynamic approach to simulate the evolution of pathogens within individual hosts. By integrating contemporary genomic, demographic, and mobility data, we validated our model through a case study that replicated key characteristics of the recent COVID-19 pandemic. Notably, our simulation, in line with real-world observations, demonstrated the punctuated evolution of SARS-CoV-2 and the emergence and spread of various viral variants. Specifically, the model linked pathogen evolution to the dynamics of social interactions and the effects of public health interventions, showcasing the power of multi-scale modelling in exploring the complexities of pandemic scenarios.