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

被引:25
|
作者
Arsevska, Elena [1 ,2 ]
Roche, Mathieu [3 ,4 ]
Hendrikx, Pascal [5 ]
Chavernac, David [1 ,2 ]
Falala, Sylvain [1 ,2 ]
Lancelot, Renaud [1 ,2 ]
Dufour, Barbara [6 ]
机构
[1] French Agr Res & Int Cooperat Org CIRAD, Unit Control Exot & Emerging Dis Anim UMR CMAEE, Campus Int Baillarguet, F-34398 Montpellier, France
[2] French Natl Inst Agr Res INRA, Unit Control Exot & Emerging Dis Anim UMR CMAEE, Campus Int Baillarguet, F-34398 Montpellier, France
[3] French Agr Res & Int Cooperat Org CIRAD, Unit Land Environm Remote Sensing & Spatial Infor, 500 Rue Jean Francois Breton, F-34093 Montpellier, France
[4] Univ Montpellier, French Natl Ctr Sci Res CNRS, Lab Informat Robot & Microelect LIRMM, UMR 5506, F-34000 Montpellier, France
[5] French Agcy Food Environm & Occupat Safety ANSES, Unit Coordinat & Support Surveillance UCAS, 14 Rue Pierre & Marie Curie, F-94706 Maisons Alfort, France
[6] Alfort Vet Sch ENVA, 7 Ave Gen Gaulle, F-94704 Maisons Alfort, France
关键词
Web; Disease outbreak; Text mining; Term extraction; Query; Delphi method; PUBLIC-HEALTH; CLASSIFICATION; SURVEILLANCE; INTELLIGENCE; INFORMATICS;
D O I
10.1016/j.compag.2016.02.010
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
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 Naive 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 (ALICs) 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. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:104 / 115
页数:12
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