ArticleMolecular biology and evolution2023
Decoding the Fundamental Drivers of Phylodynamic Inference.
Article in Molecular biology and evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- How does date-rounding affect phylodynamic inference for public health?PLoS computational biology · 2025Article
- Integrating Contact Tracing Data to Enhance Outbreak Phylodynamic Inference: A Deep Learning Approach.Molecular biology and evolution · 2024Article
- HIV-1 transmission dynamics among people who inject drugs on the US/Mexico border during the COVID-19 pandemic: a prosepective cohort study.Lancet regional health. Americas · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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