Determination and Metrics for Emerging Risks Identification DEMETER: Final Report. Issue 7 (6th July 2020)

• Record Type: Journal Article
• Authors: Meijer, Nathan; Filter, Matthias; Józwiak, Ákos; Willems, Don; Frewer, Lynn; Fischer, Arnout; Liu, Ningjing; Bouzembrak, Yamine; Valentin, Lars; Fuhrmann, Marcel; Mylord, Teresa; Kerekes, Kata; Farkas, Zsuzsa; Hadjigeorgiou, Eleni; Clark, Beth; Coles, David; Comber, Rob; Simpson, Emma; Marvin, Hans
• Is Part Of: EFSA supporting publications. Volume 17:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-06
• Subjects: Food -- Europe -- Safety measures -- Periodicals; Food -- Safety measures; Europe; Periodicals; Periodicals; 363.192094
• DOI: 10.2903/sp.efsa.2020.EN-1889
• Languages: English
• ISSNs: 2397-8325

Abstract

Abstract : Identification of emerging risks in the food chain is essential if EFSA is to anticipate future needs in risk assessment, in relation to both data and methodology. The objectives and research proposed in the DEMETER project were specifically designed to support current (and future) EFSA procedures for emerging issue and risks identification by providing community resourcesto allow EFSA and EU Member State authorities to share data, knowledge and methods on emerging risks identification in a rapid and effective manner through a digital platform. To this end, an "Emerging Risk Knowledge Exchange Platform (ERKEP)" was developed as a prototype technical solution. Its design is based on a consultation on end‐users needs and the analysis of existing knowledge sharing solutions. ERKEP consists of three main components: 1) A content management system (CMS) providing the end‐user's "entry point" and Graphical User Interface (GUI) to ERKEP; 2) A web‐based data analytics platform (DAP) for sharing and executing data analytics workflows (DAWs), based on the KNIME Server infrastructure; 3) External web‐based services hosted by 3rd party service providers. Different DAWs were developed and added to the platform, these are: 1) Emerging risk identification system for the milk supply chain based on automated data retrieval; 2) NewsRadar; 3)Trending topics in news based on text mining and network analysis, and;4) Patent network analysis. Methodologies were identified to integrate social science information and data, into the emerging risk identification framework. Systematic reviews of the literature wereconducted in the areas of expert elicitation, citizen science, and behavioural science and a framework to incorporate data from Citizen Science into the EKREP platform was proposed. Finally, sustainability and maintenance of the project's outputs were conceptualized to enable use thereof beyond project DEMETER.