Anylogic-based model prediction analysis of the impact of social distance obedience behavior on the spread of epidemics

ABSTRACT

The outbreak of COVID-19 in 2020 has had a serious impact on society and drawn the attention of all sectors of society to major emergency public security incidents. So this study focused on the social distance which is one of the most effective methods to reduce the transmission rate of the epidemic to conduct research. In order to visually describe the effect of social distance obedience behavior, we use Anylogic to simulate the subway station with high pedestrian traffic in daily life and visualize the process and rate of transmission. Social distance was introduced as a primary variable, mask-wearing rate as a secondary variable, and the simulation set the movement trajectory of pedestrians and used the "controlled variable method"to analyze their effects on infection. The results show that both maintaining a social distance of more than 1.25 meters and wearing a mask rate of more than 70% can effectively inhibit the spread of the epidemic, and the combined effect of both is more effective in infection control. © 2021 ACM.