ArticleMolecular biology and evolution2022
Variational Phylodynamic Inference Using Pandemic-scale Data.
Article in Molecular biology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- torchtree: Flexible Phylogenetic Model Development and Inference Using PyTorch.Systematic biology · 2026Article
- ARTreeFormer: A faster attention-based autoregressive model for phylogenetic inference.PLoS computational biology · 2025Article
- Poplar: a phylogenomics pipeline.Bioinformatics advances · 2025Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.PLoS computational biology · 2024Article
- Differentiable phylogeneticsBioinformatics advances · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.bioRxiv : the preprint server for biology · 2023Article
- Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation.Genome biology and evolution · 2023Article
- Fidelity of hyperbolic space for Bayesian phylogenetic inference.PLoS computational biology · 2023Article
- Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The ongoing global pandemic has sharply increased the amount of data available to researchers in epidemiology and public health. Unfortunately, few existing analysis tools are capable of exploiting all of the information contained in a pandemic-scale data set, resulting in missed opportunities for improved surveillance and contact tracing. In this paper, we develop the variational Bayesian skyline (VBSKY), a method for fitting Bayesian phylodynamic models to very large pathogen genetic data sets. By combining recent advances in phylodynamic modeling, scalable Bayesian inference and differentiable programming, along with a few tailored heuristics, VBSKY is capable of analyzing thousands of genomes in a few minutes, providing accurate estimates of epidemiologically relevant quantities such as the effective reproduction number and overall sampling effort through time. We illustrate the utility of our method by performing a rapid analysis of a large number of SARS-CoV-2 genomes, and demonstrate that the resulting estimates closely track those derived from alternative sources of public health data.
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