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Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks
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Author(s): Elena Arsevska (French Agricultural Research and International Cooperation Organization (CIRAD), France), Mathieu Roche (French Agricultural Research and International Cooperation Organization (CIRAD), France), Pascal Hendrikx (French Agency for Food, Environmental and Occupational Safety (ANSES), France), David Chavernac (French Agricultural Research and International Cooperation Organization (CIRAD), France), Sylvain Falala (French National Institute for Agricultural Research (INRA), France), Renaud Lancelot (French Agricultural Research and International Cooperation Organization (CIRAD), France)and Barbara Dufour (Université Paris Est, France)
Copyright: 2017
Pages: 22
Source title:
Public Health and Welfare: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1674-3.ch026
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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.
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