Innovating Works

MOOD

Financiado
MOnitoring Outbreak events for Disease surveillance in a data science context
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 ch... 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 ver más
31/12/2024
CIRAD
15M€
Duración del proyecto: 60 meses Fecha Inicio: 2019-12-06
Fecha Fin: 2024-12-31

Línea de financiación: concedida

El organismo H2020 notifico la concesión del proyecto el día 2019-12-06
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
SC1-BHC-13-2019: Mining big data for early detection of infectious disease threats driven by climate change and other factors
Cerrada hace 5 años
Presupuesto El presupuesto total del proyecto asciende a 15M€
Líder del proyecto
CENTRE DE COOPERATION INTERNATIONALE EN RECHE... No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5