Operational Strategies for Patient Isolation Units to Control Aerosol Transmission: A Computational Fluid Dynamics-based Multi-objective Optimization Approach

Abstract The COVID-19 pandemic overwhelmed hospital capacities, prompting the use of patient isolation units (PIUs) to flexibly expand containment in emergency rooms (ERs). However, existing studies primarily rely on static analyses and rarely quantify the direct infection risk to healthcare workers (HCWs). To address these, we propose an integrated framework to simultaneously minimize the aerosol dispersion index (ADI) and infection risk index (IRI) under dynamic conditions. A multistage methodology integrating computational fluid dynamics (CFD), surrogate modeling, and the non-dominated sorting genetic algorithm II (NSGA-II) was developed to analyze 120 dynamic scenarios. Optimal solutions were derived to quantify the improvements in ADI and IRI relative to general operations. Furthermore, the technique for order preference by similarity to ideal solution (TOPSIS) algorithm was employed to determine the optimal distributions of the PIU operational parameters and HCW behavioral factors based on weighted priorities. The optimized strategy demonstrated enhanced improvements in both ADI and IRI compared to the general operation. Optimal control minimized instances where IRI exceeded levels observed without a PIU, while simultaneously enhancing ADI. Variable analysis revealed that the optimal fan filter unit (FFU) operation depends heavily on the specific dynamic conditions. Furthermore, maintaining physical distance emerged as a consistently dominant factor in reducing IRI, whereas respiratory avoidance efficacy was largely negated by turbulence. This study highlights the necessity and effectiveness of adaptive optimization in PIUs. Implementing intelligent strategies and adaptive control algorithms that respond to real-world environmental changes is essential for comprehensive infection control in high-risk environments.