Abstract
In phylogeography, ancestral state inference methods are used to identify the geographic or host species origin of viral or bacterial lineages and reconstruct their transmission histories over time. However, differences in sampling among states can bias these inference methods. Here, we introduce sampling-aware ancestral state inference (SAASI), a method that accounts for sampling differences. We apply SAASI to the multi-host spread of the H5N1 virus in the United States in 2024 and find that the key transmission event from wild birds to cattle is estimated to occur later under lower sampling in wild birds (compared to other species) than when sampling is not accounted for. Using simulation, we find that SAASI infers past viral locations/host species considerably more accurately than standard methods when sampling bias exists, is computationally feasible for large datasets, and scales to trees with 100,000 tips.
Similar content being viewed by others
Acknowledgements
We emphasize our strong appreciation for the authors of Nguyen et al.2, the Flu crew at the U.S. National Animal Disease Center, and the authors of the GitHub repository at https://github.com/flu-crew/dairy-cattle-hpai-2024, who published their H5N1 repository under a license permitting reuse and publication without restriction (among other permissions). Such data sharing is essential for method development efforts, and it is greatly appreciated. This work was supported by a grant to Dr. William Hsiao at Simon Fraser University, from the Public Health Agency of Canada (Arrangement: 2223-HQ-000265). Dr. Hsiao’s salary was partially supported by a Michael Smith Health Research BC Scholar Award. This work is supported by NSERC (CC; IG; YS: CANMOD, the Canadian Network for Modelling Infectious Disease, 560516-2020; AM: CRC-2021-00276 and CC: RGPIN-2019-06624), the Federal Government of Canada’s Canada 150 Research Chair programme, and the Michael Smith Foundation for Health Research Scholar Programme.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Song, Y., Gill, I., MacPherson, A. et al. SAASI: Sampling Aware Ancestral State Inference. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72851-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-72851-5
