What are the big challenges in data science and antimicrobial resistance (AMR)? New technologies are allowing us to generate ever increasing volumes of data e.g. metagenomics of bacterial communities, proteomic/metabolomic/transcriptomic data or mass spectroscopy which may inform us about antibiotic resistance phenotypes.
However, interpreting this explosion of data is challenging and requires new statistical and computational approaches. A parallel problem is how do we integrate key data from multiple sources e.g. on prescribing behaviour and rates of resistance observed in humans, animals and the environment and how do these relate to each other.
There are also potential governance and legislative challenges around making data accessible which contain personal or commercially sensitive information. Whilst increasing antibiotic use is associated with increasing levels of resistance, it is not so clear that reducing use can also reduce resistance and these relationships may be highly non-linear.
The Jean Golding Institute and Bristol Bridge are holding a workshop to share the current research to address these big challenges in data and AMR and identify future collaborative opportunities for funding applications
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