Advances in sequencing technologies are providing an unprecedented opportunity to a detailed discovery of the mechanisms involved in the evolution and spread of microbes causing human infectious disease and in particular to elucidate the success factors behind multi-drug resistant bacteria. Simultaneously the developers of statistical methods have faced an enormous challenge to cope with the wealth of data brought by this opportunity. The rise of microbial Big Data gives a promise of a giant leap in making important discoveries, however, the previously existing statistical methods were 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 aimed at leveraging scalable inference methods by a unique combination of machine learning algorithms and statistical models for evolutionary epidemiology driven by population genomics. We focused 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 made it possible to answer a multitude of important questions that have previously been intractable or very challenging to solve in a reliable manner. Through close collaboration with the leading research centres in infectious disease epidemiology and genomics, the SCARABEE project aimed 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.
