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Monitoring online media reports for early detection of unknown diseases: Insight from a retrospective study of COVID‐19 emergence
Author(s) -
Valentin Sarah,
Mercier Alizé,
Lancelot Renaud,
Roche Mathieu,
Arsevska Elena
Publication year - 2021
Publication title -
transboundary and emerging diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.392
H-Index - 63
eISSN - 1865-1682
pISSN - 1865-1674
DOI - 10.1111/tbed.13738
Subject(s) - pandemic , covid-19 , disease , outbreak , coronavirus , pneumonia , medicine , vocabulary , event (particle physics) , identification (biology) , infectious disease (medical specialty) , virology , biology , pathology , linguistics , philosophy , physics , botany , quantum mechanics
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.