Identification of terms for detecting early signals of emerging infectious disease outbreaks on the web

Elena Arsevska 1 Mathieu Roche 2, 3 Pascal Hendrikx 4 David Chavernac 1 Sylvain Falala 1 Renaud Lancelot 1 Barbara Dufour 5
1 UMR CMAEE - Contrôle des maladies animales exotiques et émergentes
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
4 UCAS - Direction des Laboratoires
5 ENVA - École nationale vétérinaire d'Alfort
Abstract : Timeliness and precision for detection of infectious animal disease outbreaks from the information published on the web is crucial for prevention against their spread. The work in this paper is part of the methodology for monitoring the web that we currently develop for the French epidemic intelligence team in animal health. We focus on the new and exotic infectious animal diseases that occur worldwide and that are of potential threat to the animal health in France. In order to detect relevant information on the web, we present an innovative approach that retrieves documents using queries based on terms automatically extracted from a corpus of relevant documents and validated with a consensus of domain experts (Delphi method). As a decision support tool to domain experts we introduce a new measure for ranking of extracted terms in order to highlight the more relevant terms. To categorise documents retrieved from the web we use Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers. We evaluated our approach on documents on African swine fever (ASF) outbreaks for the period from 2011 to 2014, retrieved from the Google search engine and the PubMed database. From 2400 terms extracted from two corpora of relevant ASF documents, 135 terms were relevant to characterise ASF emergence. The domain experts identified as highly specific to characterise ASF emergence the terms which describe mortality, fever and haemorrhagic clinical signs in Suidae. The new ranking measure correctly ranked the ASF relevant terms until position 161 and fairly until position 227, with areas under ROC curves (AUCs) of 0.802 and 0.709 respectively. Both classifiers were accurate to classify a set of 545 ASF documents (NB of 0.747 and SVM of 0.725) into appropriate categories of relevant (disease outbreak) and irrelevant (economic and general) documents. Our results show that relevant documents can serve as a source of terms to detect infectious animal disease emergence on the web. Our method is generic and can be used both in animal and public health domain.
Keywords : Web Disease outbreak Text mining Query Delphi method Term extraction
Type de document :
Article dans une revue
Computers and Electronics in Agriculture, Elsevier, 2016, 123, pp.104-115. 〈10.1016/j.compag.2016.02.010〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01348412
Contributeur : Isabelle Gouat <>
Soumis le : samedi 23 juillet 2016 - 08:11:16
Dernière modification le : mardi 19 février 2019 - 15:20:50

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Elena Arsevska, Mathieu Roche, Pascal Hendrikx, David Chavernac, Sylvain Falala, et al.. Identification of terms for detecting early signals of emerging infectious disease outbreaks on the web. Computers and Electronics in Agriculture, Elsevier, 2016, 123, pp.104-115. 〈10.1016/j.compag.2016.02.010〉. 〈lirmm-01348412〉

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