ArticleNature communications2022
Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Rapid Phylogeographic Inference of Regional Epidemic Dynamics for Routine Genomic Surveillance.Research square · 2026Article
- Leveraging synthetic and genetic data to improve epidemic forecasting.PLoS computational biology · 2026Article
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 2026Article
- PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies.Systematic biology · 2026Article
- Global approaches to infectious disease surveillance and modeling.Nature medicine · 2026Review
- Article
- Bioinformatics of Combined Nuclear and Mitochondrial Phylogenomics to Define Key Nodes for the Classification of Coleoptera.Systematic biology · 2026Article
- Ancestral state reconstruction with discrete characters using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Parameter Estimation from Phylogenetic Trees Using Neural Networks and Ensemble Learning.Systematic biology · 2026Article
- Phylogenetic estimation of diversity-dependent biogeographic rates using deep learning.bioRxiv : the preprint server for biology · 2026Article
- Phylogenetics and molecular evolution to understand and curb the HIV pandemic.Nature reviews. Microbiology · 2026Review
- A Systematic Review: Deep Learning for Analyzing Genomic Data to Discover Evolutionary Patterns.Scientifica · 2026Review
- phyddle: Software for Exploring Phylogenetic Models with Deep Learning.Systematic biology · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- Article
- Tracing SARS-CoV-2 clusters across local scales using genomic data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- [Applications, challenges and a trustworthy use of artificial intelligence in public health].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Foundation models and intelligent decision-making: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2025Review
- Accounting for contact tracing in epidemiological birth-death models.PLoS computational biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Widely applicable, accurate and fast inference methods in phylodynamics are needed to fully profit from the richness of genetic data in uncovering the dynamics of epidemics. Standard methods, including maximum-likelihood and Bayesian approaches, generally rely on complex mathematical formulae and approximations, and do not scale with dataset size. We develop a likelihood-free, simulation-based approach, which combines deep learning with (1) a large set of summary statistics measured on phylogenies or (2) a complete and compact representation of trees, which avoids potential limitations of summary statistics and applies to any phylodynamics model. Our method enables both model selection and estimation of epidemiological parameters from very large phylogenies. We demonstrate its speed and accuracy on simulated data, where it performs better than the state-of-the-art methods. To illustrate its applicability, we assess the dynamics induced by superspreading individuals in an HIV dataset of men-having-sex-with-men in Zurich. Our tool PhyloDeep is available on github.com/evolbioinfo/phylodeep .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.