Agent-based Simulation of Pedestrian Dynamics for Exposure Time Estimation in Epidemic Risk Assessment

TL;DR

This paper presents an agent-based simulation model to evaluate pedestrian behavior and exposure times for COVID-19 transmission, providing insights into effective physical distancing measures in public spaces.

Contribution

It introduces a novel agent-based simulation approach to assess contact transmission risk and physical distancing effectiveness during pandemics.

Findings

A density of one person per 16m^2 or less reduces infection risk.

Simulation results support physical distancing as an effective containment measure.

Insights help optimize public space management during epidemics.

Abstract

With the Corona Virus Disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so-called physical distancing or social distancing. In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess behaviour of pedestrians in public places in the context of contact-transmission of infectious diseases like COVID-19, and to gather insights about exposure times and the overall effectiveness of distancing measures. To abide the minimum distance of $1.5m$ stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per $16m^2$ or below is sufficient. The results of this study give insight about how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.