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- W4313492089 abstract "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 formal method to visualise 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 drive phylodynamic inference. Applying our method to simulations and outbreaks of 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." @default.
- W4313492089 created "2023-01-06" @default.
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- W4313492089 date "2022-06-09" @default.
- W4313492089 modified "2023-09-28" @default.
- W4313492089 title "Decoding the fundamental drivers of phylodynamic inference" @default.
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- W4313492089 doi "https://doi.org/10.1101/2022.06.07.495205" @default.
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