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.
Objective
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
Fields of science
Not validated
Not validated
- natural sciencescomputer and information sciencesdata sciencebig data
- medical and health scienceshealth sciencesinfectious diseases
- natural sciencescomputer and information sciencesdata sciencedata mining
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance
Keywords
Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
75016 Paris
France
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Participants (26)
2000 ANTWERPEN
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38098 San Michele All'Adige
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
38122 Trento
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8092 Zuerich
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1000 029 Lisboa
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HR6 8QA Leominster
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1206 Geneve
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75654 Paris
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1050 Bruxelles / Brussel
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3000 Leuven
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34090 Montpellier
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SO17 1BJ Southampton
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2980 Zoersel
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
53111 Bonn
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
6865 HK DOORWERTH
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
OX1 2JD Oxford
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00161 Roma
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00271 Helsinki
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49100 Angers
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11000 Beograd
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28029 Madrid
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94700 Maisons Alfort
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75007 Paris
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
69280 Marcy L Etoile
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
91120 Palaiseau
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02446 Brookline Ma
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