A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels
arXiv:2606.30842v1 Announce Type: new 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 pa
The proliferation of AI and advanced computational methods is enabling more sophisticated approaches to epidemiological data analysis, leading to breakthroughs in predictive modeling.
This research signifies a substantial improvement in the accuracy and reliability of outbreak transmission reconstruction, which is critical for public health decision-making and preparedness.
Epidemiologists can now leverage transferable learned temporal priors to analyze outbreak data with higher precision, reducing the reliance on deterministic and potentially inaccurate ground truth assumptions.
- · Public Health Organizations
- · Epidemiologists
- · AI/ML Research Firms developing health applications
- · Governments
- · Traditional Epidemiological Modeling firms
Improved early detection and response capabilities for infectious disease outbreaks.
More effective allocation of resources during public health crises through better predictive models.
Enhanced global health security, potentially reducing the economic and social impact of future pandemics.
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