Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and brought huge influence to socioeconomic development as well as people's production and life. Taking for example the virus transmission that may occur after college students return to school during the outbreak, 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 designed in this paper, based on 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 simulation experiment results show that the proposed model in this paper fully illuminate how the virus spread in the dense crowd. Furthermore, preventive and control measures such as self-protection, crowd decentralization and quarantine during the epidemic can effectively delay the arrival of infection peak and reduce the prevalence, and thus lower the risk of COVID-19 transmission after the students return to school.

中文翻译：

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基于Agent的校园新型冠状病毒感染及控制模拟

由于极高的传染性，2019年冠状病毒病（COVID-19）已在世界范围内迅速传播，并给社会经济发展以及人们的生产和生活带来了巨大影响。以大学生在暴发期间重返校园后可能发生的病毒传播为例，我们分析了关键因素对病毒传播的定量影响，包括人群密度和自我保护。本文基于封闭环境中人群反复接触和人群流动性强的特点，设计了一种新型的冠状病毒校园病毒感染与控制模拟模型。具体来说，我们建立了一个基于代理的感染模型，引入了均值理论来计算病毒传播的可能性，并微观模拟个体之间的日常感染率。仿真实验结果表明，本文提出的模型充分说明了病毒如何在密集人群中传播。此外，在流行期间采取自我保护，人群下放和隔离等预防和控制措施，可以有效地延迟感染高峰的到来并降低患病率，从而降低学生返校后传播COVID-19的风险。