 This paper reviews the use of whole genome sequencing, WGS, and machine learning in infection prevention. It finds that WGS surveillance is an effective tool for detecting and investigating outbreaks, while machine learning can be used to complement traditional methods of gathering epidemiological data and identifying transmission pathways. The authors suggest that broader adoption of these techniques could revolutionise the way outbreaks are detected and controlled. This article was authored by Alexander J. Sunderman, Jisha Chen, James K. Miller, and others.