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Infection Risk Mitigation Using Pedestrian Dynamics
Book chapter

Infection Risk Mitigation Using Pedestrian Dynamics

Ashok Srinivasan and Sirish Namilae
Architectural Factors for Infection and Disease Control, pp.93-108
Routledge, 1
2023
DOI: https://doi.org/10.4324/9781003214502-8

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Abstract

Increased contact due to the congregation of people in public locations elevate the risk of infectious disease spread, including measles, influenza, and COVID-19. The movement and interaction of people in built environments is the primary driving force of infection risk in such situations. Current approaches to lowering infection risk typically reduce the number of people congregating, thus disrupting human activity. Instead, the built environment and processes can be designed to minimize social proximity without as much disruption. Pedestrian dynamics is a mathematical modeling technique for simulating the fine-scale movement and interaction patterns of people in a crowd, which can help with such designs. Results from pedestrian dynamics simulations can be linked with an infection spread model to estimate infection risk. However, there are challenges in using pedestrian dynamics models in complex situations due to discretionary human behavior that is not captured in the models and from inherent uncertainty in human movement patterns. The former can be addressed by using new data sources, such as location-based services data from cell phone apps. The latter can be addressed using vulnerability analysis with a low-discrepancy parameter sweep to analyze risk under various possible scenarios. The authors illustrate this approach with an analysis of infection risk in the Orlando airport and show that it can be used to identify design choices for airport security queues, leading to a substantial reduction in infection risk with a suitable option. Pedestrian dynamics models human movement using simple features, such as the desire to move toward a destination and a tendency to avoid running into other pedestrians or fixed surfaces. Subsequently, different queue configurations are analyzed using pedestrian dynamics in order to identify a good queue layout at security checkpoints to minimize the contacts by 75% and infection risk by 25%. The outline for the rest of the chapter is as follows: the second section introduces popular pedestrian dynamics techniques. The first is that initial conditions for starting the pedestrian dynamics simulations may not be known. LBS, explained in the following sections, can suitably inform the pedestrian dynamics model. Based on the LBS data analysis, we use the pedestrian dynamics and infection spread modeling for analyzing if the layout and processes at the security checkpoints can be modified to mitigate infectious disease spread.

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