BIG_IDEA

Building an Integrated Genetic Infectious Disease Epidemiology Approach

 Coordinatore UNIVERSITY COLLEGE LONDON 

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 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 1˙483˙080 €
 EC contributo 1˙483˙080 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2010-StG_20091118
 Funding Scheme ERC-SG
 Anno di inizio 2010
 Periodo (anno-mese-giorno) 2010-11-01   -   2015-10-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE

 Organization address address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
city: LONDON
postcode: SW7 2AZ

contact info
Titolo: Ms.
Nome: Tatjana
Cognome: Palalic
Email: send email
Telefono: +44 20 75943866
Fax: +44 20 75943868

UK (LONDON) beneficiary 170˙401.81
2    UNIVERSITY COLLEGE LONDON

 Organization address address: GOWER STREET
city: LONDON
postcode: WC1E 6BT

contact info
Titolo: Ms.
Nome: Greta
Cognome: Borg-Carbott
Email: send email
Telefono: 442031000000
Fax: 442078000000

UK (LONDON) hostInstitution 1˙312˙678.20
3    UNIVERSITY COLLEGE LONDON

 Organization address address: GOWER STREET
city: LONDON
postcode: WC1E 6BT

contact info
Titolo: Prof.
Nome: Francois
Cognome: Balloux
Email: send email
Telefono: 442077000000
Fax: 442078000000

UK (LONDON) hostInstitution 1˙312˙678.20

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

data    framework    real    epidemics    public    pandemic    human    spread       health       infer    influenza    sequence    infectious    recent    reconstructing    disease    epidemiology    pathogens    time    methodological    genetic   

 Obiettivo del progetto (Objective)

'Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. The recent swine-derived influenza A/H1N1 pandemic may represent a tipping point in this trend, as it is arguably the first time when multiple strains of a human pathogen have been sequenced essentially in real time from the very beginning of its spread. However, the full potential of genetic information cannot be fully exploited to infer the spread of epidemics due to the lack of statistical methodologies capable of reconstructing transmission routes from genetic data structured both in time and space. To address this urgent need, we propose to develop a methodological framework for the reconstruction of the spatiotemporal dynamics of disease outbreaks and epidemics based on genetic sequence data. Rather than reconstructing most recent common ancestors as in phylogenetics, we will directly infer the most likely ancestries among the sampled isolates. This represents an entirely novel paradigm and allows for the development of statistically coherent and powerful inference software within a Bayesian framework. The methodological framework will be developed in parallel with the analysis of real genetic/genomic data from important human pathogens. We will in particular focus on the 2009 A/H1N1 pandemic influenza, methicilin-resistant Staphylococcus aureus clones (MRSAs), Batrachochytrium dendrobatidis, a fungus currently devastating amphibian populations worldwide. The tools we are proposing to develop are likely to impact radically on the field of infectious disease epidemiology and affect the way infectious emerging pathogens are monitored by biologists and public health professionals.'

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