In the context of Epidemic Intelligence, many Event-Based Surveillance (EBS) systems have been proposed in the literature to promote the early identification and characterization of potential health threats from online sources of any nature. Each EBS system has its own surveillance definitions and priorities, therefore this makes the task of selecting the most appropriate EBS system for a given situation a challenge for end-users. In this work, we propose a new evaluation framework to address this issue. It first transforms the raw input epidemiological event data into a set of normalized events with multi-granularity, then conducts a descriptive retrospective analysis based on four evaluation objectives: spatial, temporal, thematic and source analysis. We illustrate its relevance by applying it to an Avian Influenza dataset collected by a selection of EBS systems, and show how our framework allows identifying their strengths and drawbacks in terms of epidemic surveillance.

翻译：在疫情情报背景下，文献中提出了许多基于事件的监测（EBS）系统，以促进从各种在线来源中早期识别和表征潜在健康威胁。每个EBS系统都有其自身的监测定义和优先级，因此，对于最终用户而言，如何在特定情境下选择最合适的EBS系统成为一项挑战。在本研究中，我们提出了一种新的评估框架以解决这一问题。该框架首先将原始输入的流行病学事件数据转化为具有多粒度性的标准化事件集，然后基于四个评估目标（空间、时间、主题和来源分析）进行回顾性描述分析。我们通过将其应用于由一组选定EBS系统收集的禽流感数据集来展示其相关性，并展示我们的框架如何能够识别这些系统在疫情监测方面的优势与不足。