ArticleNature communications2026
SAASI: Sampling Aware Ancestral State Inference.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- SAASI: Sampling Aware Ancestral State Inference.Nature communications · 2026Article
- Genomic epidemiology ofMicrobial genomics · 2026Article
- The global phylogeography of rapidly expanding multidrug resistant Ural lineage 4.2 Mycobacterium tuberculosis.Nature communications · 2026Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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