Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.

减少拥挤环境中行人之间的互动可能会遏制包括 COVID-19 在内的传染病的传播。在许多高密度的人造环境（例如等候队列）中易感和传染性个体的混合涉及行人运动，这在疾病动态的建模研究中通常不考虑在内。在本文中，基于社会力的行人动力学方法用于评估邻近行人之间的接触，然后将其与随机流行病学模型相结合，以估计局部爆发中的传染病传播。这种多尺度模型在现实生活场景中的实际应用可能会受到人类行为的不确定性、早期流行期间缺乏数据以及问题固有的随机性的限制。我们对不确定性的来源进行参数化，并使用一种新颖的高效参数扫描算法探索相关的参数空间。我们展示了低差异序列 (LDS) 参数扫描在减少此多尺度问题中有效参数空间探索所需的模拟次数方面的有效性。该算法应用于类似于机场安检点的行人队列中传播的传染病模型问题。我们发现，即使对于多尺度模型的一个组件，利用基于低差异序列的参数扫描，也可以将计算需求降低一个数量级。我们展示了低差异序列 (LDS) 参数扫描在减少此多尺度问题中有效参数空间探索所需的模拟次数方面的有效性。该算法应用于类似于机场安检点的行人队列中传播的传染病模型问题。我们发现，即使对于多尺度模型的一个组件，利用基于低差异序列的参数扫描，也可以将计算需求降低一个数量级。我们展示了低差异序列 (LDS) 参数扫描在减少此多尺度问题中有效参数空间探索所需的模拟次数方面的有效性。该算法应用于类似于机场安检点的行人队列中传播的传染病模型问题。我们发现，即使对于多尺度模型的一个组件，利用基于低差异序列的参数扫描，也可以将计算需求降低一个数量级。