Quantitative Microbial Risk Assessment (QMRA) is a well-established framework for assessing the risk of airborne transmission. However, deterministic approaches fail to identify the relative importance of variable factors affecting infection risk such as natural ventilation. In this work, a QMRA model of a naturally ventilated UK hospital respiratory ward is extended using a Monte Carlo simulation, incorporating stochastic effects. The model couples transient airflow data from a network-based ventilation model, CONTAM, with an airborne infection model. The stochasticity allows for the variation of the infectiousness of the infector, accounting for population heterogeneity, and weather on the day of the outbreak, influencing airflow and natural ventilation. Results show that effects of external weather conditions on indoor airflow dominate infection risk outcomes (i.e., particular days experience inherently high or low risk), regardless of the infector’s infectiousness. This is predominantly driven by the wind direction and, consequently, inter-zonal indoor airflow patterns. Results demonstrate the complexity of natural ventilation, with higher ventilation rates not always leading to decreased infection risk but instead, increasing the transport of infectious pathogens between zones and therefore, exposure. The interplay between natural and mechanical ventilation is also explored. This work highlights nuances present when assessing outbreaks and further highlights the complex role that indoor airflow and ventilation play in long-range airborne transmission. By extending existing QMRA models to include stochastic effects, it is possible to investigate a wider range of scenarios and thus, provide a more realistic quantification of infection risk and the factors that affect airborne transmission. • A transient, multi-zone airborne infection model is coupled with CONTAM airflow data. • A Monte Carlo formulation incorporates stochastic effects into outbreak parameters. • The quanta emission rate and day of the outbreak are sampled from distributions. • Airflow on certain days causes inherently high or low risk, due to weather effects. • Higher natural ventilation rates do not always lead to lower infection risk.
Edwards et al. (Fri,) studied this question.