An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
This provides new data resources for epidemiological research and disease surveillance, though it is incremental as it applies existing AI methods to a specific domain.
The authors tackled the problem of extracting actionable epidemiological information from WHO Disease Outbreak News by using an ensemble of Large Language Models, resulting in a daily-updated dataset and knowledge graph called eKG.
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.