Objective Advances in sequencing technologies are currently providing an unprecedented opportunity to a detailed discovery of the mechanisms involved in the evolution and spread of microbes causing human infectious disease. Simultaneously the developers of statistical methods face an enormous challenge to cope with the wealth of data brought by this opportunity. We have very recently demonstrated the ability of our advanced computational approaches to deliver breakthroughs in understanding pathogen evolution and transmission in numerous highlight results published in Science, PNAS and top-ranking Nature journals. The rise of microbial Big Data gives a promise of a giant leap in making causal discoveries, however, the existing statistical methods are neither able to cope with the size and complexity of the emerging data sets nor designed to answer the novel biological questions they enable. To fulfil the promise of giant leaps SCARABEE will leverage scalable inference methods by a unique combination of machine learning algorithms and Bayesian statistical models for evolutionary epidemiology. We focus on central biological questions about adaptation, epistasis, genome evolution and transmission of microbes causing infectious disease. The Big Data combined with the novel inference methods will make it possible to answer a multitude of important questions that remain currently intractable. Through our close collaboration with the leading research centres in infectious disease epidemiology and genomics we expect the SCARABEE project to considerably advance understanding of the evolution and transmission of numerous pathogens that pose a major threat to human health, which will be important for reducing their disease burden in the future. Large-scale biological data will be used to benchmark the developed methods, which will be made publicly available as free software packages to benefit the wide community of microbiologists and infectious disease epidemiologists. Fields of science medical and health scienceshealth sciencespublic healthepidemiologynatural sciencescomputer and information sciencesdata sciencebig datamedical and health scienceshealth sciencesinfectious diseasesnatural sciencesmathematicsapplied mathematicsstatistics and probabilitynatural sciencescomputer and information sciencesartificial intelligencemachine learning Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Topic(s) ERC-2016-ADG - ERC Advanced Grant Call for proposal ERC-2016-ADG See other projects for this call Funding Scheme ERC-ADG - Advanced Grant Host institution UNIVERSITETET I OSLO Net EU contribution € 2 499 961,00 Address PROBLEMVEIEN 5-7 0313 Oslo Norway See on map Region Norge Oslo og Viken Oslo Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 499 961,00 Beneficiaries (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all UNIVERSITETET I OSLO Norway Net EU contribution € 2 499 961,00 Address PROBLEMVEIEN 5-7 0313 Oslo See on map Region Norge Oslo og Viken Oslo Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 2 499 961,00