New agentic framework generates grounded public health narratives from complex data

Researchers have developed EpiNarrate, an agentic framework designed to generate accurate and accessible public health narratives from complex epidemiological projections. This system separates numerical reasoning from natural language generation, organizing multidimensional data and deriving consistent quantitative statements. Experiments using data from the COVID-19 Scenario Modeling Hub showed that EpiNarrate produces narratives with improved factual grounding and broader coverage of epidemiological patterns compared to direct LLM summarization. AI

IMPACT This framework could improve the communication of complex health data to the public and policymakers.

RANK_REASON The cluster contains an academic paper detailing a new method for narrative generation from complex data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• COVID-19 Scenario Modeling Hub
• EpiNarrate

• paper
• product

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