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

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, Barbara Dufour
Copyright: © 2016 |Volume: 7 |Issue: 3 |Pages: 20
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466692008|DOI: 10.4018/IJAEIS.2016070101
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MLA

Arsevska, Elena, et al. "Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks." IJAEIS vol.7, no.3 2016: pp.1-20. http://doi.org/10.4018/IJAEIS.2016070101

APA

Arsevska, E., Roche, M., Hendrikx, P., Chavernac, D., Falala, S., Lancelot, R., & Dufour, B. (2016). Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 7(3), 1-20. http://doi.org/10.4018/IJAEIS.2016070101

Chicago

Arsevska, Elena, et al. "Identification of Associations between Clinical Signs and Hosts to Monitor the Web for Detection of Animal Disease Outbreaks," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 7, no.3: 1-20. http://doi.org/10.4018/IJAEIS.2016070101

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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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