UMR TETIS (Land, Environment, Remote Sensing and Spatial Information), University of Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France.
UMR ASTRE (Unit for Animals, Health, Territories, Risks and Ecosystems), University of Montpellier, CIRAD, INRAE, Montpellier, France.
Sci Data. 2022 Oct 26;9(1):655. doi: 10.1038/s41597-022-01743-2.
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.
基于事件的监测（EBS）从多种数据源收集信息，包括在线新闻文章。与正式报告中的数据不同，EBS 数据是非结构化的，其解释可能会超过现有人力资源的流行情报（EI）能力。因此，已经开发了各种自动处理（全部或部分）从在线新闻文章中获取的非结构化数据的 EBS 系统。这些 EBS 系统（例如，GPHIN、HealthMap、MedISys、ProMED、PADI-web）可以使用带注释的数据来改进监测系统。本文描述了一种对动物疾病相关新闻文章中的流行病学信息进行注释的框架。我们提供了通用的注释指南，适用于动物和人畜共患传染病，无论涉及的病原体如何或其传播方式（例如，媒介传播、空气传播、接触传播）如何。该框架依赖于对新闻文章中所有句子的连续注释。注释者根据新闻文章的发布日期，在特定的流行病学背景下评估句子。
