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}

}

@article{,

title = {H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?},

author = {Rémy Decoupes and Kafando Rodrique and Mathieu Roche and Maguelonne Teisseire },

doi = {10.5194/agile-giss-2-2-2021},

year  = {2021},

date = {2021-06-01},

urldate = {2020-01-01},

journal = {AGILE: GIScience Series},

volume = {2},

number = {4},

pages = {1-8},

publisher = {European Centre for Disease Prevention and Control},

abstract = {Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.},

keywords = {COVID-19, EBS, media extraction, Model, population dynamics, Public Health, Twitter},

pubstate = {published},

tppubtype = {article}

}

@article{valentin2021monitoring,

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},

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

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Transboundary and emerging diseases},

volume = {68},

number = {3},

pages = {981--986},

publisher = {Wiley Online Library},

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, data mining, EBS, Event extraction, media extraction, monitoring, ProMed},

pubstate = {published},

tppubtype = {article}

}