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MOnitoring Outbreak events for Disease surveillance in a data science context

Descrizione del progetto

La raccolta di megadati può ottimizzare una sorveglianza tempestiva delle minacce alla salute pubblica

I funzionari della sanità pubblica incaricati di salvaguardare i cittadini contro le epidemie di malattie infettive fanno generalmente affidamento alle relazioni ufficiali relative a malattie specifiche redatte dagli operatori sanitari (sorveglianza basata sugli indicatori o IBS, Indicator-Based Surveillance). Tuttavia, essi utilizzano sempre più la sorveglianza basata sugli eventi (EBS, Event-Based Surveillance) servendosi di relazioni, storie, voci e altre informazioni trasmesse attraverso canali formali o informali, tra cui blog, hotline e social media. Il vantaggio dell’EBS è quello di essere tempestiva, poiché riflette gli eventi prima che molti pazienti visitino i prestatori di assistenza sanitaria o ricevano risultati positivi ai test. Il progetto MOOD, finanziato dall’UE, sta sfruttando l’estrazione dei dati e l’analisi dei megadati per migliorare l’utilità dell’EBS. Esso non sarebbe ovviamente completo senza una piattaforma online progettata per incoraggiare l’utilizzo quotidiano, permettere l’analisi in tempo reale e rafforzare la raccolta e l’interpretazione dei dati.

Obiettivo

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

Invito a presentare proposte

H2020-SC1-BHC-2018-2020

Vedi altri progetti per questo bando

Bando secondario

H2020-SC1-2019-Single-Stage-RTD

Meccanismo di finanziamento

RIA - Research and Innovation action

Coordinatore

CENTRE DE COOPERATION INTERNATIONALE EN RECHERCHE AGRONOMIQUE POUR LEDEVELOPPEMENT - C.I.R.A.D. EPIC
Contribution nette de l'UE
€ 2 662 355,90
Indirizzo
RUE SCHEFFER 42
75016 Paris
Francia

Mostra sulla mappa

Regione
Ile-de-France Ile-de-France Paris
Tipo di attività
Research Organisations
Collegamenti
Costo totale
€ 2 836 969,25

Partecipanti (26)