A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels
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Computer Science > Machine Learning
Title:A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels
Abstract:Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematically evaluated. We trained a logistic regression temporal prior on eleven disease families, locked all parameters before accessing any target outbreak data, and applied it without refitting to a strict Andes virus (ANDV) parent-ranking benchmark of 29 tasks. The locked prior achieved mean reciprocal rank (MRR) 0.571 versus 0.274 and Top-1 accuracy 37.9% versus 13.8% against the best source-trained parametric baseline (permutation p <= 0.0002; 7-8 reversals to lose MRR significance). A phylogenetic concordance audit of 75 NYC mpox inter-host pairs - independent label-reliability evidence rather than a prior validation - found that 54.67% (exact 95% CI: 42.75-66.21%) were genomically unresolved or unsupported. Retaining uncertain edges in ANDV and Guangdong Delta graphs shifted top-5 source-priority sets (Jaccard 0.429-0.667). Transmission-label uncertainty was measurable in the outbreak evidence modules examined, and retaining uncertain links changed which source cases were prioritized for intervention.
| Comments: | 30 pages, 7 figures, 15 tables, 2 algorithms, 26 references |
| Subjects: | Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2606.30842 [cs.LG] |
| (or arXiv:2606.30842v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30842
arXiv-issued DOI via DataCite (pending registration)
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