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

Research Bioinformatics AI

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TL;DR - Delphy is a scalable Bayesian phylogenetics method for analyzing expanding viral outbreaks in near real time. It aims to give public health organizations state-of-the-art analysis of their own outbreak data with minimal operational friction.

  • Designed to scale as viral outbreak datasets grow.
  • Supports near-real-time Bayesian phylogenetic analysis.
  • Intended to help public health bodies analyze local data and respond rapidly.
  • The provided excerpt does not include benchmarks, implementation details, or specific accuracy results.

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

Nature Patrick Varilly, Mark Schifferli, Katherine Yang, Paul Cronan, Ivan Specht, Tim Burcham, Olivia Glennon, Olivia Jacks, Ellory Laning, Libby Marrs, Kyle Oba, Shannon Yeung, Karlie Wenran Zhao, Edyth Parker, Ifeanyi Omah, Jonathan E. Pekar, Laura Luebbert, Kristian G. Andersen, Daniel J. Park, Stephen F. Schaffner, Bronwyn L. MacInnis, Christian Happi, Jacob E. Lemieux, Al Ozonoff, Michael Mitzenmacher, Ben Fry, Pardis C. Sabeti 2026-09-16 doi:10.1038/s41586-026-11012-6
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:18:50.330097 UTC

TL;DR - Delphy is a scalable Bayesian phylogenetics method for analyzing expanding viral outbreaks in near real time. It aims to give public health organizations state-of-the-art analysis of their own outbreak data with minimal operational friction.

  • Designed to scale as viral outbreak datasets grow.
  • Supports near-real-time Bayesian phylogenetic analysis.
  • Intended to help public health bodies analyze local data and respond rapidly.
  • The provided excerpt does not include benchmarks, implementation details, or specific accuracy results.
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