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PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News

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Book cover Human Language Technology. Challenges for Computer Science and Linguistics (LTC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12598))

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Abstract

The Platform for Automated Extraction of Animal Disease Information from the Web (PADI-web) is a multilingual text mining tool for automatic detection, classification, and extraction of disease outbreak information from online news articles. PADI-web currently monitors the Web for nine animal infectious diseases and eight syndromes in five animal hosts. The classification module is based on a supervised machine learning approach to filter the relevant news with an overall accuracy of 0.94. The classification of relevant news between 5 topic categories (confirmed, suspected or unknown outbreak, preparedness and impact) obtained an overall accuracy of 0.75. In the first six months of its implementation (January–June 2016), PADI-web detected 73% of the outbreaks of African swine fever; 20% of foot-and-mouth disease; 13% of bluetongue, and 62% of highly pathogenic avian influenza. The information extraction module of PADI-web obtained F-scores of 0.80 for locations, 0.85 for dates, 0.95 for diseases, 0.95 for hosts, and 0.85 for case numbers.

PADI-web allows complementary disease surveillance in the domain of animal health.

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Notes

  1. 1.

    https://www.plateforme-esa.fr/.

  2. 2.

    https://padi-web.cirad.fr/en/.

  3. 3.

    http://aims.fao.org/vest-registry/vocabularies/agrovoc.

  4. 4.

    https://azure.microsoft.com/en-gb/services/cognitive-services/translator-text-api/.

  5. 5.

    This definition is only based in the news semantic, and do not take into account the official confirmation by a formal source.

  6. 6.

    https://www.oie.int/en/animal-health-in-the-world/technical-disease-cards/.

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Acknowledgements

We thank J. de Goër, B. Belot, C. Hemeury, M. Devaud, and T. Filiol for their contribution in the development of PADI-web. We also thank the members of the French Epidemic Intelligence Team in Animal Health for their constructive comments during the development of PADI-web. This work has been supported by the French General Directorate for Food (DGAL), the French Agricultural Research Centre for International Development (CIRAD), the SONGES Project (FEDER and Occitanie), and the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 (#DigitAg). This work has also been funded by the “Monitoring outbreak events for disease surveillance in a data science context” (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (https://mood-h2020.eu/).

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Authors and Affiliations

  1. UMR TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, 34398, Montpellier, France

    Sarah Valentin & Mathieu Roche

  2. UMR ASTRE, Univ Montpellier, CIRAD, INRAE, 34398, Montpellier, France

    Sarah Valentin, Elena Arsevska, Alize Mercier, Sylvain Falala & Renaud Lancelot

  3. CIRAD, Montpellier, France

    Sarah Valentin, Elena Arsevska, Alize Mercier, Julien Rabatel, Renaud Lancelot & Mathieu Roche

Authors
  1. Sarah Valentin
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  2. Elena Arsevska
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  3. Alize Mercier
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  4. Sylvain Falala
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  5. Julien Rabatel
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  6. Renaud Lancelot
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  7. Mathieu Roche
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Correspondence to Sarah Valentin .

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Editors and Affiliations

  1. Adam Mickiewicz University, Poznań, Poland

    Zygmunt Vetulani

  2. Laboratoire d’Informatique pour la Méca, Orsay, France

    Patrick Paroubek

  3. Adam Mickiewicz University, Poznań, Poland

    Marek Kubis

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Valentin, S. et al. (2020). PADI-web: An Event-Based Surveillance System for Detecting, Classifying and Processing Online News. In: Vetulani, Z., Paroubek, P., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2017. Lecture Notes in Computer Science(), vol 12598. Springer, Cham. https://doi.org/10.1007/978-3-030-66527-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-66527-2_7

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