Pedestrian Modeling for Mitigation of Disease Transmission in a Simulated University Environment

Open Access
Article
Conference Proceedings
Authors: Michael SchwartzCortnee StainrodIrin Nizam

Abstract: Understanding the spread of COVID-19 through mathematical modeling is an effective method of evaluating control interventions and the impact of infectious diseases. It is important to understand how individuals move and gather within indoor spaces as early awareness of specified strategies act as decision-making tools to riskier alternatives. On university campuses, indoor spaces pose unique threats due to high traffic spaces in the building hallways, restrooms and bottleneck points that lead to mass congregation and therefore increased risk of transmission. Evaluation of infectious diseases transmission as a result of pedestrian dynamics (e.g., pedestrian density, crowding, queue and wait times) was used to determine time-varying social distancing during pedestrian interactions/movements. Multiple campus buildings were modeled to demonstrate environments with varying size and complexity. Building models were constructed using the pedestrian features of AnyLogic. The proposed solution makes the following contributions by tracking the control measures of pedestrian dynamics at the microscopic level through temporal and spatial separation. This is done by enforcing social distancing through reducing the number of individual occupants at one time (i.e., segmented student population) and staggering start and end arrival times.The two greatest risk factors in the models were time and space. Entrances and exits to buildings, classrooms, and restrooms, and other queues forced simulated agents to cross the danger threshold as these building features were physical bottlenecks. Model results demonstrated sharp, but brief increases in transmission due to not staggering class arrival and departure times. Results indicated that controlling scheduling or forcing space assignments/social distancing were effective in reducing contacts and risk of spreading disease; however, the greatest reduction in risk of disease transmission occurred when both methods were used in conjunction. When class arrival and departure times are staggered, transmission between people not in the same class is only possible during chance encounters due to restroom visits, late arrivals, or early departures.

Keywords: COVID-19, modeling, built environments, scheduling

DOI: 10.54941/ahfe1001358

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