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Journal Articles (Data Paper) Scientific Data Year : 2022

Elaboration of a new framework for fine-grained epidemiological annotation

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Abstract

Event-based surveillance (EBS) gathers information from a variety of data sources, including online news articles. Unlike the data from formal reporting, the EBS data are not structured, and their interpretation can overwhelm epidemic intelligence (EI) capacities in terms of available human resources. Therefore, diverse EBS systems that automatically process (all or part of) the acquired nonstructured data from online news articles have been developed. These EBS systems (e.g., GPHIN, HealthMap, MedISys, ProMED, PADI-web) can use annotated data to improve the surveillance systems. This paper describes a framework for the annotation of epidemiological information in animal disease-related news articles. We provide annotation guidelines that are generic and applicable to both animal and zoonotic infectious diseases, regardless of the pathogen involved or its mode of transmission (e.g., vector-borne, airborne, by contact). The framework relies on the successive annotation of all the sentences from a news article. The annotator evaluates the sentences in a specific epidemiological context, corresponding to the publication date of the news article.
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Dates and versions

hal-03856038 , version 1 (16-11-2022)

Licence

Attribution - CC BY 4.0

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Sarah Valentin, Elena Arsevska, Aline Vilain, Valérie de Waele, Renaud Lancelot, et al.. Elaboration of a new framework for fine-grained epidemiological annotation. Scientific Data , 2022, 9 (1), pp.655. ⟨10.1038/s41597-022-01743-2⟩. ⟨hal-03856038⟩
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