Real-time analysis of pathogen dispersion patterns resulting from a moving infectious person

Maintaining indoor air quality is particularly challenging in shared spaces where both healthy and infectious persons may be present. Thus, it is essential to continuously monitor such spaces and take preventive action (ventilating) to mitigate infection transmission among other users. This study proposes a novel method for real-time detection of infectious persons and dynamic modeling of pathogen dispersion during and after their presence. The objective is to inform building operators to take appropriate action such as providing more ventilation. The method must meet two key requirements: 1) It must continuously track the infectious person's location using real-time data from sensors and cameras without relying on predefined movement paths, and 2) it must provide simulation results with computational times close to real-time, enabling immediate decision making based on pathogen concentration levels. To reduce computation time, the person is modeled as a virtual pathogen-emitting zone. Results show this abstraction only affects airflow within 1-m of the source, with minimal impact beyond, aligning with previous studies. This approach by decoupling of equations significantly speeds up simulations. In the presented case study, the simulation required 3.84 s to model 1 s of real-time pathogen dispersion, with an acceptable error margin of 3.8 %, using a personal computer. This approach offers a practical and efficient solution for real-time infection risk assessment in shared indoor environments.