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Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks

Author

Listed:
  • J. Voznica

    (Université Paris Cité, Unité Bioinformatique Evolutive
    Université de Paris
    Université Paris Sciences et Lettres)

  • A. Zhukova

    (Université Paris Cité, Unité Bioinformatique Evolutive
    Université Paris Cité, Bioinformatics and Biostatistics Hub
    Université Paris Cité, Epidemiology and Modelling of Antibiotic Evasion
    Université Paris-Saclay, UVSQ, Inserm, CESP)

  • V. Boskova

    (University of Vienna and Medical University of Vienna)

  • E. Saulnier

    (Université Paris Cité, Unité Bioinformatique Evolutive)

  • F. Lemoine

    (Université Paris Cité, Unité Bioinformatique Evolutive
    Université Paris Cité, Bioinformatics and Biostatistics Hub)

  • M. Moslonka-Lefebvre

    (Université Paris Cité, Unité Bioinformatique Evolutive)

  • O. Gascuel

    (Université Paris Cité, Unité Bioinformatique Evolutive
    Institut de Systématique, Evolution, Biodiversité (UMR 7205 - CNRS, Muséum National d’Histoire Naturelle, SU, EPHE, UA))

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 .

Suggested Citation

  • J. Voznica & A. Zhukova & V. Boskova & E. Saulnier & F. Lemoine & M. Moslonka-Lefebvre & O. Gascuel, 2022. "Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31511-0
    DOI: 10.1038/s41467-022-31511-0
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    References listed on IDEAS

    as
    1. Emma Saulnier & Olivier Gascuel & Samuel Alizon, 2017. "Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-31, March.
    2. David A Rasmussen & Erik M Volz & Katia Koelle, 2014. "Phylodynamic Inference for Structured Epidemiological Models," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
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    Cited by:

    1. Mark P. Khurana & Jacob Curran-Sebastian & Neil Scheidwasser & Christian Morgenstern & Morten Rasmussen & Jannik Fonager & Marc Stegger & Man-Hung Eric Tang & Jonas L. Juul & Leandro Andrés Escobar-He, 2024. "High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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