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Norovirus

Model-based methods for hospital infection control

MARS Research Fellow, Dr Jess Bridgen, is developing an innovative system to help hospitals detect and control infections before they spread.

Hospital-acquired infections are a significant burden to health systems world-wide, and are associated with increased morbidity and mortality. Effective hospital infection control requires a detailed understanding of the role of different transmission pathways, yet identifying exactly where and how infections spread within the complex structure of healthcare settings presents a unique statistical and computational challenge.

A model-based system can be used to rapidly detect at-risk areas of a hospital and quantify relative routes of transmission, providing actionable insights for infection control. This research focuses on developing a flexible mathematical modelling framework for bacterial and viral diseases, and novel statistical methodology to identify drivers of transmission.

This research began during the COVID-19 pandemic, working with NHS clinicians to develop a Bayesian framework to retrospectively identify infection times and disentangle hospital outbreak dynamics (Bridgen, 2024). This methodology is now being extended to assess the effectiveness of control measures, model a range of infectious diseases, and incorporate real-time data.

By providing hospitals with data-driven insights, this research could save lives and reduce the burden on healthcare systems.

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