Agent-Based Campus Novel Coronavirus Infection and Control Simulation

TL;DR

This paper presents an agent-based simulation model for COVID-19 spread in campus environments, analyzing how crowd density and self-protection influence infection dynamics and control effectiveness.

Contribution

It introduces a novel campus-specific infection model incorporating repeated contact and mobility, using mean field theory for transmission probability calculation.

Findings

The model effectively simulates virus spread in dense, mobile crowds.

Preventive measures delay infection peaks and reduce overall prevalence.

Control strategies lower COVID-19 transmission risk in campus settings.

Abstract

Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation model (CVICS) of the novel coronavirus is proposed in this paper, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and micro-simulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this paper efficiently simulate how the virus spread in the dense crowd in frequent contact under closed environment. Furthermore, preventive and control measures such as self-protection, crowd decentralization and isolation during the epidemic can effectively delay the arrival of infection peak and reduce the prevalence, and finally lower the risk of COVID-19 transmission after the students return to school.