Managing occupancy density and ventilation strategies for mitigating airborne transmission during pandemics by using infection risk model

Abstract

During pandemics, the infection risk model can serve as a tool for optimizing the occupancy density, ventilation rates, and preventive measures to reduce airborne transmissions by controlling the environment reduces infection risk for occupants. This study introduces a novel model designed for real-time, iterative control of airborne transmission risk. The model uniquely integrates mask efficiency, social distancing, and dynamic ventilation, achieving a low average deviation of 10.78 % from actual airborne transmission data across 11 scenarios. Case studies of controlling COVID-19 in office, restaurant, and bar settings demonstrate the model's application in determining (a) optimal occupancy with limited ventilation and (b) ventilation rates needed for desired occupancy. With a limited ventilation rate based on ASHRAE 62.1 standards, the allowed occupancy density is much less than the designed value. Also, it is further decreased as the duration of time spent indoors increases. Wearing face masks also allows for increased occupancy density with percentage increases ranging from 51.52 % to 800.00 %. Instead of limiting the number of people in an area, to achieve the desired occupancy while maintaining an acceptable infection risk, the ventilation rates must be greatly increased up to 3904 % for non-wearing face mask cases compared with the standard ventilation rates. By wearing face masks, the required ventilation rates can be reduced by 5.67 times. Furthermore, the model shows that dynamically adjusting ventilation based on occupancy can reduce energy consumption by approximately 70 %.