Illness caused by the consumption of contaminated food is a major threat to public health and requires significant resources to identify the source of infections. However, due to the interconnected nature of the global food supply chain, source identification is complex and labour-intensive. Spearheaded by the UK Health Security Agency (UKHSA), public health agencies have begun to routinely utilise whole genome sequencing (WGS) for pathogen surveillance. WGS provides high-resolution information on the genetic relatedness between disease isolates, allowing for confident identification of clusters of infections arising from common sources.
This project will develop cutting-edge machine learning (ML) tools for the prediction of foodborne disease outbreaks. The student will develop a reinforcement learning (RL)-based decision support tool trained on genomic surveillance data to identify whether we can predict the occurrence of future outbreaks of gastrointestinal disease and determine the optimal time for epidemiologists to intervene.
Competition Funded PhD Project (Students Worldwide)
CLOSING DATE: Wednesday, November 02, 2022