Monitoring online media reports for the early detection of unknown
diseases: insights from a retrospective study of COVID-19 emergence
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. Following the emergence of a newly
identified coronavirus –so-called COVID-19, in humans in December 2019
in Wuhan, China, we conducted a retrospective analysis of the capacity
of three Event-Based Systems (EBS) systems (ProMED, HealthMap and
PADI-web) to detect early signals of this emergence. We evaluated the
changes in the online news vocabulary coinciding with the period before
/ after the identification of COVID-19, as well as the assessment of 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 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 the three EBS systems are complementary regarding data
sources, and all need improvements regarding timeliness. EBS methods
should be adapted to the different stages of disease emergence to
improve the early detection of future emergence of unknown pathogens.