Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence

Valentin Sarah, Mercier Alizé, Lancelot Renaud, Roche Mathieu, Arsevska Elena. 2020. Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID-19 emergence. Transboundary and Emerging Diseases, 6 p.

Journal article ; Article de recherche ; Article de revue à facteur d'impact
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Additional Information : Ce travail soutenu par les projets MOOD (H2020 - No. 874850), SONGES (FEDER & Occitanie), #DigiTag (ANR‐16‐CONV‐0004) est un prolongement de la communication réalisée dans le cadre du Symposium AgriNumA 2019 (Dakar, Sénégal).

Abstract : Event‐based surveillance (EBS) systems monitor a broad range of information sources to detect early signals of disease emergence, including new and unknown diseases. In December 2019, a newly identified coronavirus emerged in Wuhan (China), causing a global coronavirus disease (COVID‐19) pandemic. A retrospective study was conducted to evaluate the capacity of three event‐based surveillance (EBS) systems (ProMED, HealthMap and PADI‐web) to detect early COVID‐19 emergence signals. We focused on changes in online news vocabulary over the period before/after the identification of COVID‐19, while also assessing its contagiousness and pandemic potential. ProMED was the timeliest EBS, detecting signals one day before the official notification. At this early stage, the specific vocabulary used was related to 'pneumonia symptoms' and 'mystery illness'. Once COVID‐19 was identified, the vocabulary changed to virus family and specific COVID‐19 acronyms. Our results suggest that the three EBS systems are complementary regarding data sources, and all require timeliness improvements. EBS methods should be adapted to the different stages of disease emergence to enhance early detection of future unknown disease outbreaks.

Mots-clés Agrovoc : Coronavirus, fouille de données, vocabulaire, Logiciel, Système d'information, Efficacité, Intelligence artificielle, Surveillance épidémiologique, Virose

Mots-clés complémentaires : COVID-19, maladie à coronavirus 2019, pandémie, fouille de texte, Système événementiel (eng EBS), Intelligence épidémiologique

Mots-clés libres : Epidemic intelligence, Text Mining, COVID-19, One Health, Online news, PADI‐web

Classification Agris : L73 - Animal diseases
U40 - Surveying methods
000 - Autres thèmes

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

Agence(s) de financement européenne(s) : European Commission

Programme de financement européen : H2020

Projet(s) de financement européen(s) : MOnitoring Outbreak events for Disease surveillance in a data science context

Auteurs et affiliations

  • Valentin Sarah, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-9028-681X
  • Mercier Alizé, CIRAD-BIOS-UMR ASTRE (FRA)
  • Lancelot Renaud, CIRAD-BIOS-UMR ASTRE (FRA)
  • Roche Mathieu, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0003-3272-8568
  • Arsevska Elena, CIRAD-BIOS-UMR ASTRE (FRA) - auteur correspondant

Source : Cirad-Agritrop (

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