genomic surveillance meets machine learning: predicting the origins of salmonella outbreaks with machine learning

Salmonella enterica is a leading cause of human gastroenteritis worldwide, with non-typhoidal Salmonella estimated to account for ~1 billion infections and ~150,000 deaths annually. This gastrointestinal pathogen therefore represents a major public health concern, necessitating real-time epidemiological monitoring and follow-up. Outbreak investigations, however, are often confounded by the complexity of international food-trade networks which distribute zoonotic food-borne pathogens across the globe. This project aims to address this gap by utilising machine learning (ML) to predict the geographical source of gastrointestinal pathogens directly from genomic surveillance data, allowing for improved public health response and more rapid outbreak resolution. The project has three main objectives: co-producing knowledge for effective source attribution, investigating phylogeographical signals in Salmonella enterica genomes, and optimizing source attribution models using machine learning techniques.