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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| Vydáno v: | Molecular biology and evolution Ročník 40; číslo 6 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
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Oxford University Press
01.06.2023
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| Témata: | |
| ISSN: | 0737-4038, 1537-1719, 1537-1719 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | 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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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors supervised this work equally. |
| ISSN: | 0737-4038 1537-1719 1537-1719 |
| DOI: | 10.1093/molbev/msad132 |