{
  "abstract": "Reconstructing who infected whom often relies on case timing and published transmission links. However, two important questions remain: can a temporal pattern learned from other diseases transfer to a new outbreak, and how reliable are the transmission links used as ground truth? We learned a temporal prior from eleven disease groups using logistic regression. The model was locked before any target-outbreak data were accessed and was then tested without refitting on 29 Andes virus (ANDV) parent-ranking tasks. The locked prior achieved a mean reciprocal rank (MRR) of 0.571, compared with 0.274 for the strongest fair source-trained temporal baseline (permutation p<0.001). Its Top-1 accuracy was 37.9%, compared with 13.8%. The MRR advantage remained significant unless 7-8 favorable task outcomes were reversed. We separately examined the reliability of published transmission links. Among 75 epidemiologically linked inter-host pairs from the 2022 New York City mpox outbreak, 54.67% were genomically unresolved or unsupported as direct transmission links (exact 95% CI: 42.75-66.21%). We also tested whether retaining uncertain transmission edges changed source prioritization. In ANDV and Guangdong Delta outbreak graphs, the top-5 priority sets changed, with Jaccard similarity ranging from 0.429 to 0.667. These results show that temporal information learned across diseases can provide a useful ranking signal without target-specific refitting. They also show that uncertainty in published transmission links can affect which source cases are prioritized for investigation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2606.30842",
  "author_total": 1,
  "authors": [
    {
      "name": "Md Ahsan Karim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8sp97f2ap4m0v.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2606.30842",
  "fulltext_url": null,
  "id": "8sp97f2ap4m0v",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2606.30842",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2713|Data-Driven Disease Surveillance"
    ]
  ],
  "openalex": "W7166819170",
  "pdf_url": "https://arxiv.org/pdf/2606.30842",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8sp97f2ap4m0v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "2606.30842",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2606.30842"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels",
  "title_text": "A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:24Z",
  "url": "https://research.draconitic.com/paper/8sp97f2ap4m0v",
  "venue": "arXiv",
  "versions": []
}