Parameter Space Exploration in Pedestrian Queue Design to Mitigate Infectious Disease Spread

Abstract

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 forcebased
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.