基于Agent的校园新型冠状病毒感染与控制仿真
Agent-Based Campus Novel Coronavirus Infection and Control Simulation
摘要：
2019冠状病毒病（COVID-19）由于其极高的传染性，在全球迅速蔓延，对社会经济发展和人们的日常生活带来巨大影响。以大学生返校后可能发生的病毒传播为例，定量分析了人群密度、自我防护等关键因素对病毒传播的影响。充分考虑封闭环境中人群反复接触、流动性强的特点，提出了一种新型冠状病毒的校园病毒感染与控制仿真模型（CVICS）。具体来说，我们建立了一个基于代理的感染模型，引入平均场理论计算病毒传播的概率，微观模拟了个体之间的每日感染流行情况。实验结果表明，本文提出的模型能够有效地模拟病毒在封闭环境下频繁接触的密集人群中的传播。此外，疫情期间采取自我防护、人群分散、隔离等防控措施，可有效延缓感染高峰的到来，降低疫情流行，最终降低学生返校后COVID-19传播的风险。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
