Toward an Agentic AI Approach to Public Health Data Collection
4.40T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2610.31746.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryThe paper presents a multi-agent framework for public-health surveillance that discovers and assesses sources, structures evidence, links events, and validates records. Retrospective tests recovered 13 hantavirus cases, closely reconstructed Canadian measles counts, and increased recent mpox-report coverage by incorporating attributed media.
Why it mattersProvides a concrete agent-task decomposition, comparative outbreak metrics, and explicit record-quality limits. Useful as a workflow design reference, though prospective timeliness gains remain untested.
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