Decoding the fundamental drivers of phylodynamic inference

Abstract

Despite its increasing role in the understanding of infectious disease transmission at the applied and theoretical levels, phylodynamics lacks a well-defined notion of ideal data and optimal sampling. We introduce a formal method to visualise and quantify the relative impact of pathogen genome sequence and sampling times--two fundamental sources of data for phylodynamics under birth-death-sampling models--to understand how each drive phylodynamic inference. Applying our method to simulations and outbreaks of SARS-CoV-2 and H1N1 Influenza data, we use this insight to elucidate fundamental trade-offs and guidelines for phylodynamic analyses to draw the most from sequence data. Phylodynamics promises to be a staple of future responses to infectious disease threats globally. Continuing research into the inherent requirements and trade-offs of phylodynamic data and inference will help ensure phylodynamic tools are wielded in ever more targeted and efficient ways.