Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics

Pedestrian dynamics is an approach for modeling the fine-scaled movement of people. It is finding increasing application in the analysis of infection risk for directly transmitted diseases during air travel. A parameter sweep is often needed to evaluate infection risk for a variety of possible scenarios to account for inherent variability in human behavior. A low discrepancy parameter sweep was recently introduced to reduce the computational effort by one to three orders of magnitude. However, it has the following limitations: (i) a low-overhead parallelization leads to significant load imbalance, and (ii) the convergence rate worsens with dimension. This paper examines whether pseudorandom and hybrid sequences can overcome these defects and whether the convergence criteria can be changed to yield accurate solutions faster. We simulate the deplaning process of an airplane using different parameter sweep strategies and evaluate their relative computational efficiencies. Our results show that hybrid and pseudorandom parameter sweeps are advantageous for moderate accuracy, while a low discrepancy sweep is preferable for high accuracy. Our results also show that the convergence criteria could be relaxed substantially to yield accurate solutions around a factor of 20 faster. They promise to help a variety of applications that employ large parameter sweeps for modeling infection risk.