A pedestrian-based model for simulating COVID-19 transmission on college campus
Chuanyao Li, Jie Yin
Abstract
Nowadays the integration of pedestrian dynamics and epidemiology is heating up due to the pandemic of COVID-19. In this paper, we introduce a pedestrian-based epidemic transmission model that combines cellular automata-based pedestrian dynamics with stochastic infection spread dynamics. Based on this model, we simulate COVID-19 transmission in different indoor scenarios on the college campus. We confirm that COVID-19 patients' infectivity during the incubation period and the presence of asymptomatic patients are key reasons for the difficulty in controlling the epidemic. Then, several non-pharmaceutical interventions at different operational levels are proposed and their effectiveness is evaluated by using computational models. We find that indoor-level interventions can slow the speed of disease transmission while quarantine can downsize the scale of disease transmission. And the combination of these two levels of intervention is superior to any single intervention in reducing the number of new infections.