Transient models to assess transmission and control of airborne infection risks in a respiratory ward

Airborne transmission is an infection route for many diseases including influenza, COVID-19 and opportunist pathogens in hospitals. Quantifying risks are necessary to determine appropriate control strategies, both in terms of engineering approaches such as ventilation, and management strategies such as treating, locating and scheduling hospital patients. However airborne transmission is complex to evaluate as it requires understanding of the airflows, infection dynamics and human-environment interactions. The COVID-19 pandemic has shown that we have significant lack of knowledge in this area, particularly around the role of airflows in managing different sizes of airborne particles and the effectiveness of different solutions. Previous studies have developed models to link airflow and infection dynamics to assess the hospital environment, including looking at the influence of airflow patterns in multi-room environments and stochastic effects in small populations. However current models generally assume that both the infection parameters and airflows are steady-state as the outbreak evolves in time. This project aims to explore the influence of transient effects in both time and space to evaluate how short term events can influence individual risk and the overall dynamics of an outbreak. To this aim, we will combine transient airflow models from CFD simulations with stochastic infection dynamics models. The models will be applied to the respiratory wards at St James's Hospital in Leeds, with the objective of evaluating the effectiveness of interventions, including application of air cleaning devices.