Enhancing airborne transmission modelling of respiratory viruses in enclosed spaces through a CO

The COVID-19 pandemic has emphasized the critical need for accurate risk assessment and mitigation strategies in indoor settings. This study proposes an extension to the CERN’s Airborne Model for Indoor Risk Assessment (CAiMIRA), by incorporating a CO2 concentration fitting algorithm to better assume the environmental conditions of the room. This addition aims at predicting equivalent exhalation rates of the occupants and ventilation profiles, thereby reducing user inputs while enhancing the model’s accuracy. In typical risk assessment models, the exhalation and air exchange rates are user inputs which, in most of the cases, are considered as best guess estimates with large uncertainties. In this study, we obtain these parameters from measured data of indoor CO2 concentration, enhancing the accuracy of the model. Such approach can be used in applications where the occupancy profile is dynamic and/or the ventilation system is unknown or unpredictable, such as schools and offices. A series of experiments in offices buildings were performed to benchmark the algorithm, showing up to a 4-fold increase in the accuracy of the model compared to user-estimated inputs. By integrating the predicted exhalation rate and ventilation profiles obtained through the fitting algorithm, decision-makers and facility managers can rely on measured data to achieve more accurate and efficient results.