Evaluating Individual Infection Risk: The Interplay between Air Mixing Intensities and Occupant Dynamics

Abstract Aerosol transmission remains a critical route for the spread of respiratory infectious diseases, yet real-time exposure mapping in complex public spaces is computationally demanding. This study presents a robust and adaptable framework for estimating dynamic individual exposure (i.e., viral dose) by integrating high-fidelity computational fluid dynamics (CFD) with computer vision (CV) analysis. By overlaying validated steady-state viral aerosol plumes onto dynamic human interaction data, we evaluated the interplay between airflow patterns and human behaviors under two distinct air change rates (ACH: 25 h–1 and 3 h–1). Each scenario was simulated both with and without MERV-13 filtration. Applications in both a stochastic-movement hotel meeting room and a stationary-occupant office demonstrate that while high turbulence due to high ACH limits the spatial extent of plumes, sustained close proximity in structured settings can result in disproportionately high viral doses for specific individuals. MERV-13 filtration effectively reduced exposure within high-concentration zones. We propose a Reynolds number (Re)-based modeling guideline: steady-state CFD-CV for moderate mixing (900 ≤ Re ≤ 2300), a well-mixed assumption for high mixing (Re > 2300), and transient CFD-CV for low mixing (500 ≤ Re < 900) during breathing and speaking, and steady-state CFD-CV for coughing and sneezing regardless of Re value. This framework supports real-time monitoring and targeted interventions, providing a data-driven foundation for indoor occupant safety.