Marcus Marshall (University of Leeds) – The Wells-Riley model revisited II: parameter uncertainty and population heterogeneity

In this talk we revisit the Wells-Riley model which has been widely used to estimate airborne infection risk in indoor settings. We consider a probabilistic framework which allows one to quantify infection risk as a probability distribution in the scenario that variation is observed in the model parameters (e.g. due to heterogeneity, or uncertainty in measurements). By using fitted Gamma distributions, we show how the classical Wells-Riley approach can lead to systematic inaccuracies. Population-related uncertainties (e.g. quanta emission rate, pulmonary rate, exposure time) can cause infection risk overestimation, whilst environmental uncertainties (e.g. ventilation rate) can lead to infection risk underestimation. Furthermore, we investigate the effects of simultaneously random parameters and the cases where one tends to dominate the stochasticity of the final distribution over the other. Considering entire risk distributions allows for more accurate risk assessments and can help prevent extreme infection events caused by significantly low ventilation or unusually high quanta emission.