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Peer-reviewedCC BY-NC-NDNature

Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy

  • Patrick Varilly1
  • Mark Schifferli2
  • Katherine Yang2
  • Paul Cronan2
  • Ivan Specht3,4
  • Timothy Burcham2
  • Olivia Glennon2
  • Olivia Jacks2
  • Ellory Laning2
  • Libby Marrs2
  • Kyle Oba2
  • Shannon Yeung2
15 more authors
  • Karlie Wenran Zhao2
  • Edyth Parker5
  • Ifeanyi F. Omah6,7
  • Jonathan E. Pekar7
  • Laura Luebbert8,9,10
  • Kristian G. Andersen11,12
  • Daniel J. Park3
  • Stephen F. Schaffner3,13
  • Bronwyn L. MacInnis3,13,14
  • Christian Tientcha Happi15,13,5
  • Jacob E. Lemieux3,16
  • Al Ozonoff3,17
  • Michael Mitzenmacher18
  • Ben Fry2
  • Pardis Christine Sabeti19,20,21,22,23
23 affiliations
  1. 1Broad Institute of Harvard and MIT, Cambridge, MA, USA. [email protected]
  2. 2Fathom Information Design, Boston, MA, USA
  3. 3Broad Institute of Harvard and MIT, Cambridge, MA, USA
  4. 4Institute for Computational and Mathematical Engineering, School of Engineering, Stanford University, Stanford, CA, USA
  5. 5The Institute of Genomics and Global Health, Redeemer's University, Ede, Nigeria
  6. 6Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka, Nigeria
  7. 7Institute of Ecology and Evolution, University of Edinburgh, Edinburgh, UK
  8. 8Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
  9. 9Eric and Wendy Schmidt Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA
  10. 10FutureHouse, San Francisco, CA, USA
  11. 11Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
  12. 12Scripps Research Translational Institute, La Jolla, CA, USA
  13. 13Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
  14. 14Massachusetts Consortium on Pathogen Readiness, Harvard Medical School, Harvard University, Boston, MA, USA
  15. 15Department of Biological Sciences, Faculty of Natural Sciences, Redeemer's University, Ede, Nigeria
  16. 16Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  17. 17Department of Pediatrics, Harvard Medical School, Boston, MA, USA
  18. 18Department of Computer Science, School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
  19. 19Broad Institute of Harvard and MIT, Cambridge, MA, USA. [email protected]
  20. 20Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. [email protected]
  21. 21Department of Organismic and Evolutionary Biology, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA. [email protected]
  22. 22Howard Hughes Medical Institute, Chevy Chase, MD, USA. [email protected]
  23. 23Massachusetts Consortium on Pathogen Readiness, Harvard Medical School, Harvard University, Boston, MA, USA. [email protected]
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Abstract

Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1–13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14–17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy’s central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18–20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola1,21, Zika2, SARS-CoV-2 (ref. 22), mpox3,4 and H5N1 (refs. 23,24), we demonstrate state-of-the-art accuracy with up to 2–3 orders of magnitude improvements in speed. Assessing Delphy’s scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user’s machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response. Delphy makes near-real-time and scalable Bayesian phylogenetics possible for growing viral outbreaks, enabling public health bodies anywhere to analyse and react to their own data with state-of-the-art accuracy and minimal friction.

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Cite

BibTeXRIS
BibTeX
@article{varilly2026scalable,
  title = {{Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy}},
  author = {Patrick Varilly and Mark Schifferli and Katherine Yang and Paul Cronan and Ivan Specht and Timothy Burcham and Olivia Glennon and Olivia Jacks and Ellory Laning and Libby Marrs and Kyle Oba and Shannon Yeung and Karlie Wenran Zhao and Edyth Parker and Ifeanyi F. Omah and Jonathan E. Pekar and Laura Luebbert and Kristian G. Andersen and Daniel J. Park and Stephen F. Schaffner and Bronwyn L. MacInnis and Christian Tientcha Happi and Jacob E. Lemieux and Al Ozonoff and Michael Mitzenmacher and Ben Fry and Pardis Christine Sabeti},
  journal = {Nature},
  year = {2026},
  month = sep,
  doi = {10.1038/s41586-026-11012-6},
  url = {https://doi.org/10.1038/s41586-026-11012-6},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Scalable near-real-time Bayesian phylogenetics for outbreaks with Delphy
AU  - Patrick Varilly
AU  - Mark Schifferli
AU  - Katherine Yang
AU  - Paul Cronan
AU  - Ivan Specht
AU  - Timothy Burcham
AU  - Olivia Glennon
AU  - Olivia Jacks
AU  - Ellory Laning
AU  - Libby Marrs
AU  - Kyle Oba
AU  - Shannon Yeung
AU  - Karlie Wenran Zhao
AU  - Edyth Parker
AU  - Ifeanyi F. Omah
AU  - Jonathan E. Pekar
AU  - Laura Luebbert
AU  - Kristian G. Andersen
AU  - Daniel J. Park
AU  - Stephen F. Schaffner
AU  - Bronwyn L. MacInnis
AU  - Christian Tientcha Happi
AU  - Jacob E. Lemieux
AU  - Al Ozonoff
AU  - Michael Mitzenmacher
AU  - Ben Fry
AU  - Pardis Christine Sabeti
PY  - 2026
DA  - 2026/09/16
JO  - Nature
DO  - 10.1038/s41586-026-11012-6
UR  - https://doi.org/10.1038/s41586-026-11012-6
AB  - Pathogen genomic analysis is central to tracking, understanding and containing outbreaks1–13, but the complexity and cost of state-of-the-art phylogenetic tools limit global access and impact. Here we introduce Delphy, an exact reformulation of Bayesian phylogenetics14–17 designed to transform its speed, scalability and accessibility while retaining Bayesian state-of-the-art accuracy. Delphy’s central data structure, an explicit mutation-annotated tree, takes advantage of the high sequence similarity of large-scale epidemic datasets18–20 for efficient tree exploration and convergence. By reproducing key analyses from recent major epidemics, including Ebola1,21, Zika2, SARS-CoV-2 (ref. 22), mpox3,4 and H5N1 (refs. 23,24), we demonstrate state-of-the-art accuracy with up to 2–3 orders of magnitude improvements in speed. Assessing Delphy’s scalability, we show that a simulated dataset of 100,000 sequences can be analysed within a day. We distribute Delphy as a client-side web application that enables local, interactive analysis of raw data on the user’s machine. Delphy automatically identifies key viral lineages and mutations, as well as their emergence and prevalence through time, with quantified uncertainties grounded in Bayesian theory. Delphy establishes Bayesian phylogenetics as a fast, accessible frontline tool for future outbreak response. Delphy makes near-real-time and scalable Bayesian phylogenetics possible for growing viral outbreaks, enabling public health bodies anywhere to analyse and react to their own data with state-of-the-art accuracy and minimal friction.
SN  - 0028-0836
SN  - 1476-4687
ER  -