MOOD publications

@article{nokey,

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

author = {Sarah Valentin and Alizé Mercier and Renaud Lancelot and Mathieu Roche and Elena Arsevska},

url = {https://onlinelibrary.wiley.com/doi/10.1111/tbed.13738},

doi = {https://doi.org/10.1111/tbed.13738},

year  = {2021},

date = {2021-07-19},

journal = {Transboundary and Emerging Diseases},

volume = {68},

number = {3},

pages = {981-986},

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.},

keywords = {COVID-19, disease x, EBS, infectious diseases, media extraction, monitoring},

pubstate = {published},

tppubtype = {article}

}