A new contribution to epidemiological surveillance: presentation of the MOOD epidemic intelligence platform

Houben Sarah, Matheussen Tom, Roche Mathieu, Teisseire Maguelonne. 2024. A new contribution to epidemiological surveillance: presentation of the MOOD epidemic intelligence platform. In : Tropical veterinary medicine in challenging times. Book of abstracts. AITVM, STVM, CIRAD. Montpellier : AITVM, Résumé, p. 24. Joint AITVM–STVM International Conference. 3, Montpellier, France, 21 Mai 2024/24 Mai 2024.

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Résumé : The MOOD project is committed to advancing epidemic intelligence {El), encompassing early identification, verification, assessment, and investigation of potential health hazards by integrating bath indicator-based and event-based components. This holistic approach involves utilizing structured data from routine surveillance systems and unstructured data from diverse sources. Implemented as the MOOD platform, innovations within the project unfold through six case studies, facilitated and researched by case study experts. These studies are intricately linked to specific disease models, including West Nile virus infection (WNV), tick-borne encephalitis (TBE), antimicrobial resistance (AMR), COVID-19, Leptospirosis (LEPTO), Chikungunya (CHIK), Dengue (DEN), and highly pathogenic avian influenza (HPAI). The platform, central to managing epidemic intelligence, encompasses a Core database housing covariates, vector data, and a disease database. Raster layers of covariates and disease models are stored reliably and with scalability. Comprehensive metadata associated with disease and covariates data are stored in the MOOD GeoNetwork, with a selected subset directly accessible within the MOOD Core platform. The platform ensures open access to various data sources through an easily navigable interface. This presentation will delve into the architecture of the information system and provide a platform demonstration, highlighting its capabilities in linking disease and non-disease data. The platform achieves consistency in disease and covariate datasets through the normalization of thematic, temporal, and geospatial dimensions, enhancing its ability to discern and consolidate related events for more insightful and streamlined analysis in the realm of epidemic intelligence.

Mots-clés libres : Epidemic intelligence, Epidemiological surveillance

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) MOnitoring Outbreak events for Disease surveillance in a data science context

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