MOnitoring Outbreak events for Disease surveillance in a data science context (MOOD)

Type: International Project Project

Duration: from 2020 Jan 01 to 2024 Dec 31

Financed by: European Commission

Prime Contractor: UMR ASTRE - Cirad (Other)

Project Web Site:

The detection of infectious disease emergence relies on reporting cases, i.e. indicator-based surveillance (IBS). This method lacks sensitivity, due to non or delayed reporting of cases. In a changing environment due to climate change, animal and human mobility, population growth and urbanization, there is an increased risk of emergence of new and exotic pathogens, which may pass undetected with IBS. Hence, the need to detect signals of disease emergence using informal, multiple sources, i.e. event-based surveillance (EBS). The MOOD project aims at harness the data mining and analytical techniques to the big data originating from multiple sources to improve detection, monitoring, and assessment of emerging diseases in Europe. To this end, MOOD will establish a framework and visualisation platform allowing real-time analysis and interpretation of epidemiological and genetic data in combination with environmental and socio-economic covariates in an integrated inter-sectorial, interdisciplinary, One health approach: 1)Data mining methods for collecting and combining heterogeneous Big data, 2)A network of disease experts to define drivers of disease emergence, 3)Data analysis methods applied to the Big data to model disease emergence and spread, 4)Ready-to-use online platform destined to end users, i.e. national and international human and veterinary public health organizations, tailored to their needs, complimented with capacity building and network of disease experts to facilitate risk assessment of detected signals. MOOD output will be designed and developed with end users to assure their routine use during and beyond MOOD. They will be tested and fine-tuned on air-borne, vector-borne, water-borne model diseases, including anti-microbial resistance. Extensive consultations with end users, studies into the barriers to data sharing, dissemination and training activities and studies on the cost-effectiveness of MOOD output will support future sustainable user uptake.


  • Agence Nationale de la Sécurité Sanitaire de l'Alimentation de l'Environnement et du Travail (Other) - France
  • Avia-GIS (Company) - Belgium
  • Environmental Research Group Oxford (Other) - UK
  • ETH (University) - Zurich, Switzerland
  • Finnish Institute for Health and Welfare (Other) - Finland
  • Fondazione Edmund Mach (Other) - Italy
  • INRIA-Rennes (Other) - Rennes, France
  • Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (Other) - France
  • Institute of Public Health of Serbia (Other) - Serbia
  • Instituto de Salud Carlos III (Other) - Spain
  • International Society for Computational Biology (Other) - Bethesda, USA
  • International Society for Infectious Diseases Incorporated (Other) - USA
  • Istituto Superior di Sanitá - ISS (Other) - Roma, Italy
  • Katholicke Universiteit Leuven (University) - Leuven, Belgium
  • Mundialis GmbH & Co KG (Company) - Germany
  • National Research Institute in Science and Technology for Environment and Agriculture (Other) - France
  • OpenGeoHub (Other) - Netherland
  • Swiss Institute of Bioinformatics (Other) - Switzerland
  • UMR ASTRE - Cirad (Other)
  • Universidade de Lisboa (University) -Portugal
  • Université de Montpellier (University) - France
  • Université Libre de Bruxelles (University) - Brussels, Belgium
  • University of Antwerp (University) - Antwerp, Belgium
  • University of Oxford (University)
  • University of Southampton (University) - Southampton, England

Principal Investigators