Decoding the Fundamental Drivers of Phylodynamic Inference

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 method to visualize 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 drives phylodynamic inference. Applying our method to simulated data and real-world 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.

Automated summary

Despite its increasing role in understanding infectious disease transmission, phylodynamics lacks clarity on ideal data and optimal sampling. This study introduces a method to visualize and quantify the impact of pathogen genome sequence and sampling times on phylodynamic inference. By applying the method to simulated and real-world data, the study provides insights and guidelines for maximizing the use of sequence data in phylodynamic analyses. The continued research on phylodynamic data and inference is crucial for targeted and efficient responses to infectious disease threats.