面向隐形群体的复杂网络疾病传播模型  

Epidemic model with invisible patients on complex networks

作　　者：王淑娴[1,2] 刘方爱[1,2] 吴楠[1,2] 

机构地区：[1]山东师范大学信息科学与工程学院,济南250014 [2]山东省分布式计算机软件新技术重点实验室,济南250014

出　　处：《计算机应用研究》2014年第10期2986-2989,共4页Application Research of Computers

基　　金：山东省自然科学基金资助项目(ZR2013FM008);山东省科技发展计划资助项目(2011GGH20123);济南市高校院所自主创新基金资助项目(2010040072)

摘　　要：针对复杂网络经典疾病传播模型中没有考虑隔离制度的情况,研究了患有病毒、但因患病特征不明显暂未被隔离的隐形群体对疾病传播的影响。引入隔离率等因素对SIR疾病传播模型进行了改进,建立了疾病传播的SIQ(susceptible-invisible-quarantined)模型。以2013年爆发于我国的H7N9流感病毒为研究背景,利用微分方程,对传染率、接触率和隔离率在SIQ疾病传播模型中的作用进行了仿真实验。结果证明,隔离率对疾病传播影响最大,所以,扩大疑似病例的范围,增加隔离率,可以有效控制疫情大暴发。For the canonical epidemic model on complex networks did not consider the real situation of quarantine, this paper studied invisible patients＇ influence on the spread of diseases, The invisible patient refers to persons who were suffering from virus, but the symptoms were not obvious. It added the isolation rate to improve the SIR epidemic model and built the SIQ epi- demic model. The research＇ s background was the outbreak of H7N9 flu virus in 2013 in China. By using differential equation, the experiment simulated infection rates, contact rate and isolation rate in the role of the spread of virus. The simulation results show that isolation rate has the greatest influence on the spread of disease. Expanding the treatment range of suspected cases to increase the isolation rate can control epidemic effectively.

关 键 词：复杂网络 传播模型 隐形群体 微分方程 隔离率 

分 类 号：O231.5[理学—运筹学与控制论] N945.12[理学—数学]