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, which stated in January 2020, aims to harness data mining and analytical techniques to the ingest big data originating from multiple sources use these data to improve detection, monitoring, and assessment of emerging diseases in Europe.
To this end, MOOD will establis 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