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

Modelling airborne transmission and associated infection risk to susceptible occupants in indoor environments is important for improving preparedness of emerging pathogens and future pandemics. This is especially the case in hospitals, notably respiratory wards in the United Kingdom (UK), where patients are particularly vulnerable to new infections. To date, many quantitative tools used to assess airborne infection risk are limited to very simplified scenarios that fail to capture common features of real life. This thesis aims to use airflow and airborne transmission models to better understand transient and multi-zone infection risks in indoor environments. Chapter 2 introduces novel mathematical formalisms, providing extensions to the existing Wells-Riley framework, a ubiquitous tool for assessing airborne infection risks. Results show the non-negligible infection risk to a susceptible individual who remains in the space after the infector leaves and highlight the importance of incorporating stochasticity. In Chapters 3 and 4, an adapted Wells-Riley model underpins a new transient multi-zone transmission model that simulates concentration of pathogen in the air, coupled with a compartmental epidemic model. This is applied to a naturally ventilated UK respiratory ward, where results from an airflow simulation are incorporated, better representing the space. The impact of transient occupancy, weather conditions and ventilation are illustrated. Chapter 5 extends this further by using a Monte Carlo simulation, enabling a random choice of outbreak parameters. A random day is chosen for the outbreak, influencing airflow, weather conditions and ventilation, alongside a random infectiousness of the infector, representing population heterogeneity. The findings presented in this thesis demonstrate the importance of considering transient effects, modelling multi-zone environments, and including stochasticity to capture outbreak-defining features that have previously been overlooked. Through exploration of specific scenarios, results highlight the likely impact of occupant behaviour, infectiousness, ventilation, weather conditions, and airflow on airborne transmission and consequent infection risk.