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- Title
Decoding the Fundamental Drivers of Phylodynamic Inference.
- Authors
Featherstone, Leo A; Duchene, Sebastian; Vaughan, Timothy G
- 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 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.
- Subjects
INFECTIOUS disease transmission; H1N1 influenza; COMMUNICABLE diseases
- Publication
Molecular Biology & Evolution, 2023, Vol 40, Issue 6, p1
- ISSN
0737-4038
- Publication type
Article
- DOI
10.1093/molbev/msad132