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

Description:

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. 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 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.

Project information

Lead

CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR LEDEVELOPPEMENT - C.I.R.A.D. EPIC [FR]

Partners

PRINS LEOPOLD INSTITUUT VOOR TROPISCHE GENEESKUNDE; UNIVERSITE LIBRE DE BRUXELLES; KATHOLIEKE UNIVERSITEIT LEUVEN; AVIA-GIS NV [BE]

FONDAZIONE EDMUND MACH; ISTITUTO SUPERIORE DI SANITA [IT]

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH; SIB INSTITUT SUISSE DE BIOINFORMATIQUE [CH]

INESC ID - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, INVESTIGACAO E DESENVOLVIMENTO EM LISBOA [PT]

ENVIRONMENTAL RESEARCH GROUP OXFORD LIMITED; UNIVERSITY OF SOUTHAMPTON; THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD [UK]

INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE; UNIVERSITE DE MONTPELLIER; GROUPE D'EXPERIMENTATION ET DE RECHERCHE: DEVELOPPEMENT ET ACTIONS LOCALISEES; AGENCE NATIONALE DE LA SECURITE SANITAIRE DE L ALIMENTATION DE L ENVIRONNEMENT ET DU TRAVAIL; INSTITUT NATIONAL DE RECHERCHE POUR L'AGRICULTURE, L'ALIMENTATION ET L'ENVIRONNEMENT [FR]

MUNDIALIS GMBH & CO KG [DE]

STICHTING OPENGEOHUB [NL]

TERVEYDEN JA HYVINVOINNIN LAITOS [FI]

INSTITUT ZA ZASTITU ZDRAVLJA SRBIJEDR MILAN JOVANOVIC BATUT [RS]

INSTITUTO DE SALUD CARLOS III [SP]

INTERNATIONAL SOCIETY FOR INFECTIOUS DISEASES INCORPORATED [US]

 

Source of funding

SC1-BHC-13-2019 - Mining big data for early detection of infectious disease threats driven by climate change and other factors