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MOnitoring Outbreak events for Disease surveillance in a data science context

Project description

Big data collection may streamline timely surveillance of public health threats

Public health officials tasked with safeguarding citizens against infectious disease outbreaks typically rely on official reports about specific diseases from healthcare providers (indicator-based surveillance or IBS). But increasingly, they are using event-based surveillance (EBS), utilising reports, stories, rumours and other information transmitted through formal or informal channels including blogs, hotlines and social media. The benefit of EBS is its timeliness, as it reflects events before many patients have visited healthcare providers or received positive test results. The EU-funded MOOD project is taking advantage of data mining and analysis of big data to enhance the utility of EBS. Of course, it would not be complete without an online platform designed to encourage routine use, allow real-time analysis and enhance data collection and interpretation.


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

Call for proposal


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Sub call



Net EU contribution
€ 2 662 355,90
75016 Paris

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Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
Total cost
€ 2 836 969,25

Participants (26)