An evaluation framework for comparing epidemic intelligence systems

Arinik Nejat, Interdonato Roberto, Roche Mathieu, Teisseire Maguelonne. 2023. An evaluation framework for comparing epidemic intelligence systems. IEEE Access, 11 : 31880- 31901.

https://doi.org/10.1109/ACCESS.2023.3262462

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Résumé : 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.

Mots-clés Agrovoc : surveillance épidémiologique, épidémiologie, grippe aviaire, prévention des maladies, analyse spatiale

Mots-clés libres : Event-based surveillance, Epidemic intelligence, Spatio-temporal analysis, Evaluation framework, Avian influenza, PADI-Web, ProMED

Classification Agris : L73 - Maladies des animaux

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

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