Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks

Elena Arsevska, Mathieu Roche, Pascal Hendrikx, David Chavernac, Sylvain Falala, Renaud Lancelot and Barbara Dufour
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Elena Arsevska: Unit for Control of Exotic and Emerging Diseases in Animals (UMR CMAEE), French Agricultural Research and International Cooperation Organization (CIRAD), Montpellier, France
Mathieu Roche: Unit for Land, Environment, Remote Sensing and Spatial Information (UMR TETIS), French Agricultural Research and International Cooperation Organization (CIRAD), Montpellier, France
Pascal Hendrikx: Unit for Coordination and Support to Surveillance (UCAS), French Agency for Food, Environmental and Occupational Safety (ANSES), Maisons-Alfort, France
David Chavernac: Unit for Control of Exotic and Emerging Diseases in Animals (UMR CMAEE), French Agricultural Research and International Cooperation Organization (CIRAD), Montpellier, France
Sylvain Falala: Unit for Control of Exotic and Emerging Diseases in Animals (UMR CMAEE), French National Institute for Agricultural Research (INRA), Montpellier, France
Renaud Lancelot: Unit for Control of Exotic and Emerging Diseases in Animals (UMR CMAEE), French Agricultural Research and International Cooperation Organization (CIRAD), Montpellier, France
Barbara Dufour: Ecole vétérinaire d'Alfort, EpiMAI USC Anses, Université Paris Est, Maisons-Alfort, France

International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2016, vol. 7, issue 3, 1-20

Abstract: In a context of intensification of international trade and travels, the transboundary spread of emerging human or animal pathogens represents a growing concern. One of the missions of the national veterinary services is to implement international epidemiological intelligence for a timely and accurate detection of emerging animal infectious diseases (EAID) worldwide, and take early actions to prevent their introduction on the national territory. For this purpose, an efficient use of the information published on the web is essential. The authors present a comprehensive method for identification of relevant associations between terms describing clinical signs and hosts to build queries to monitor the web for early detection of EAID. Using text and web mining approaches, they present statistical measures for automatic selection of relevant associations between terms. In addition, expert elicitation is used to highlight the most relevant terms and associations among those automatically selected. The authors assessed the performance of the combination of the automatic approach and expert elicitation to monitor the web for a list of selected animal pathogens.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jaeis0:v:7:y:2016:i:3:p:1-20

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