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 (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) and Barbara Dufour (Ecole vétérinaire d'Alfort, EpiMAI USC Anses, Université Paris Est, Maisons-Alfort, France)
DOI: 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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The Argus system, hosted at Georgetown University Medical Centre (USA), uses a simple method the to detect articles on the web, using search terms of multilingual disease names (Nelson et al., 2010; Nelson et al., 2012).

More complete, web search criteria are proposed by the HealthMap team, which include disease names (scientific and common), clinical signs, keywords, and phrases. The terms originate from a dictionary of pathogens (human, plant, and animal diseases) and geographic names (country, province, state, and city). HealthMap integrates outbreak data from multiple electronic sources, including news feed aggregators (e.g., Google News), expert curated accounts (e.g., ProMED-mail), multinational surveillance reports (e.g., Eurosurveillance), and validated official alerts e.g., from WHO and OIE (Brownstein, Freifeld, Reis, & Mandl, 2008).

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