基于异质平均场复杂网络模型的新型冠状病毒传染性分析

Analysis of Novel Coronavirus Infectivety Based on Heterogeneous Mean Field Complex Network Model

传染病的传播是典型的突发公共卫生事件,也是人类在21世纪面临的重大公共安全问题之一。传统传染病研究通常利用微分方程建立传染病仓室模型进行分析,而新型冠状病毒肺炎的传播能力更强,潜伏期同样具有传染性,现有模型对真实人群的接触尺度缺乏合理描述。探索了一种复杂网络的动力学模型,通过异质平均场中的SEIR传播模型,对疾病进展和消除进行决策评估,通过使用截止到2020年3月15日公开的新型冠状病毒肺炎(COVID-19)数据,分别对国内的武汉、上海和北京三个典型城市,以及目前疫情严重且容易取得公开数据的意大利、日本和韩国三国展开了详细分析和准确预测,精确地展现了疫情期间人群隔离方案产生的效果,以及医疗资源保障的重要性。

The spread of infectious diseases is a typical public health emergency and one of the major public security problems in the 21st century.The traditional infectious disease research uses differential equation to establish the compartment model of infectious disease for analysis,while the new coronavirus pneumonia has stronger transmission capacity and the incubation period is also infectious,and the existing model lacks a reasonable description of the contact scale of real population.This article explores the dynamic model of a complex network,by SEIR propagation in heterogeneous mean-field model,based on the basic reproductive number(namely threshold R0),that is the threshold for disease progression and eliminate decision-making assessment,by using as of March 15,2020,open a new type of coronavirus pneumonia(COVID-19)data,respectively,for domestic,wuhan,Shanghai and Beijing three typical cities,as well as the current outbreaks is serious,and easy to achieve open data of Italy,Japan and South Korea launched analysis and forecasting,accurately show the outbreak population isolation during the effect of the scheme,and the importance of medical resources.