Distributed probability of infection risk of airborne respiratory diseases

Abstract

Infection risk is commonly used to predict potential health impacts of airborne respiratory diseases such as ‘severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)' and associated environment conditions and mitigation measures. The assumption of perfect air-mixing in spaces is widely applied in prediction, which projects a single mean risk of infection in the space. Detailed distribution of infection risk, especially for large spaces such as large lecture hall, indoor stadium and ballroom, will be highly desired for evaluating indoor risks and improvement performance of mitigating strategies. This study developed new formulae for calculating the spatial distribution of infection risk, stemming from the original Wells–Riley model but integrating the spatial distribution of pathogen concentrations. Case studies were presented for two typical large public spaces (i.e. restaurant and ballroom). Distributed infection risks were predicted with and without mitigation measures, upon which critical parameters of portable air cleaners can be optimized. The method can be employed for estimating local infection risks of airborne respiratory diseases using either measured or simulated pathogen concentration.