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 method to visualize and quantify the relative impact of pathogen genome seq...

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Bibliographic Details
Published in:Molecular biology and evolution Vol. 40; no. 6
Main Authors: Featherstone, Leo A, Duchene, Sebastian, Vaughan, Timothy G
Format: Journal Article
Language:English
Published: US Oxford University Press 01.06.2023
Subjects:
Analysis
Automobile drivers
COVID-19
Development and progression
Discoveries
Disease control
Disease transmission
Epidemics
Genomes
Genomics
Health aspects
Infectious diseases
Inference
Influenza A Virus, H1N1 Subtype
Markov analysis
Medical research
Medicine, Experimental
Nucleotide sequence
Pathogens
Phylogenetics
Phylogeny
Sampling
SARS-CoV-2 - genetics
Severe acute respiratory syndrome coronavirus 2
Swine influenza
Tradeoffs
Viral diseases
Australia
Bayesian phylogenetics
phylodynamics
birth–death model
ISSN:0737-4038, 1537-1719, 1537-1719
Online Access:Get full text
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Summary: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.
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These authors supervised this work equally.
ISSN:0737-4038
1537-1719
1537-1719
DOI:10.1093/molbev/msad132