mlsanityData to 14 Oct 2026

An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News

arXiv 2509.02258 ↗First posted 2 Sep 2025cs.AIcited by 3

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Abstract (arXiv)

The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, are marking a pivotal moment in epidemiology and public health research. Leveraging the power of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract valuable actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs). DONs is a collection of regular reports on global outbreaks curated by the WHO and the adopted decision-making processes to respond to them. The extracted information is made available in a daily-updated dataset and a knowledge graph, referred to as eKG, derived to provide a nuanced representation of the public health domain knowledge. We provide an overview of this new dataset and describe the structure of eKG, along with the services and tools used to access and utilize the data that we are building on top. These innovative data resources open altogether new opportunities for epidemiological research, and the analysis and surveillance of disease outbreaks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 113 references that are in the map. First posted on arXiv 2 Sep 2025.

Builds on

16 of 113 references.

References · 16

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4 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Responsecited by 1
  2. 2026Large language models for spreading dynamics in complex systemscited by 0
  3. 2026A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population datacited by 0