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A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread

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  • Xiao, Tianyi
  • Mu, Tong
  • Shen, Sunle
  • Song, Yiming
  • Yang, Shufan
  • He, Jie

Abstract

Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible–exposed–infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments.

Suggested Citation

  • Xiao, Tianyi & Mu, Tong & Shen, Sunle & Song, Yiming & Yang, Shufan & He, Jie, 2022. "A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437121009390
    DOI: 10.1016/j.physa.2021.126734
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    References listed on IDEAS

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