Decoding the Fundamental Drivers of Phylodynamic Inference
© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:40 |
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| Enthalten in: |
Molecular biology and evolution - 40(2023), 6 vom: 01. Juni |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Featherstone, Leo A [VerfasserIn] |
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| Links: |
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| Themen: |
Bayesian phylogenetics |
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| Anmerkungen: |
Date Completed 23.06.2023 Date Revised 01.07.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/molbev/msad132 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. | ||
| 520 | |a 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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| 700 | 1 | |a Vaughan, Timothy G |e verfasserin |4 aut | |
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