Multi-scale phylodynamic modelling of rapid punctuated pathogen evolution

Abstract: Computational multi-scale pandemic modelling remains a major and timely challenge. Here we identify specific requirements for a new class of pandemic models operating across three scales: (1) rapid pathogen evolution, punctuated by 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 multi-scale dynamic feedback loops. The developed framework comprises a stochastic agent-based model of pandemic spread, coupled with a phylodynamic model of the within-host pathogen evolution. It is validated with a case study, modelling a rapid punctuated evolution of SARS-CoV-2, based on global and contemporary genomic surveillance data, during the COVID-19 transmission within a large heterogeneous population. We demonstrate that the model captures the essential features of the COVID-19 pandemic and the novel coronavirus evolution, while retaining computational tractability and scalability.