Decoding the Fundamental Drivers of Phylodynamic Inference.
Mol Biol Evol
; 40(6)2023 06 01.
Article
in English
| MEDLINE | ID: covidwho-20235458
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Influenza A Virus, H1N1 Subtype
/
COVID-19
Language:
English
Journal subject:
Molecular Biology
Year:
2023
Document Type:
Article
Affiliation country:
Molbev
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