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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences software
- medical and health sciences health sciences public health epidemiology pandemics
- natural sciences biological sciences microbiology mycology
- medical and health sciences health sciences infectious diseases RNA viruses influenza
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
ERC-2010-StG_20091118
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Host institution
WC1E 6BT LONDON
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.