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.

Deep learning of SARS-CoV-2 outbreak phylodynamics with contact tracing data

“…This underscores the value of tip dates for R0 estimation, particularly as sequence variability decreases. This is in line with recent studies that highlight the increasing importance of sampling dates for phylodynamic inference when sequence variability is low (Featherstone et al, 2023). When realistic trees were used as input, models like the Dated Resolved-Model and Dated Polytomous-Model showed excellent performance, suggesting their potential for effective and accurate R0 and infectious period predictions from sequence data.…”