医院环境下的耐抗生素细菌流行病模型分析

讨论了由G.W ebb等人建立的医院环境下耐抗生素的细菌流行病模型,得到了该模型的两个基本再生数的表达式、边界平衡点和正平衡点的存在性条件以及边界平衡点的稳定性条件.     

一类年龄依赖的流行病模型的动力学行为

对一类年龄依赖的流行病模型进行了系统的定性分析。运用泛函分析中的线性算子理论和C0-半群理论等，首先得到相应的流行病算子的谱特征，其次，证明了该模型非负解的存在惟一性，最后讨论了系统的稳定性。     

Dynamic spreading behavior of homogeneous and heterogeneous networks

The detailed investigation of the dynamic epidemic spreading on homogeneous and heterogeneous networks was carried out. After the analysis of the basic epidemic models, the susceptible-infected-susceptible (SIS) model on homogenous and heterogeneous networks is established, and the dynamical evolution of the density of the infected individuals in these two different kinds of networks is analyzed theoretically. It indicates that heterogeneous networks are easier to propagate for the epidemics and the leading spreading behavior is dictated by the exponential increasing in the initial outbreaks. Large-scale simulations display that the infection is much faster on heterogeneous networks than that on homogeneous ones. It means that the network topology can have a significant effect on the epidemics taking place on complex networks. Some containment strategies of epidemic outbreaks are presented according to the theoretical analyses and numerical simulations.     

Stochastic dynamic model of SARS spreading

Based upon the simulation of the stochastic process of infection, onset and spreading of each SARS patient, a system dynamic model of SRAS spreading is constructed. Data from Vietnam is taken as an example for Monte Carlo test. The preliminary results indicate that the time-dependent infection rate is the most important control factor for SARS spreading. The model can be applied to prediction of the course with fluctuations of the epidemics, if the previous history of the epidemics and the future infection rate under control measures are known.     

Control dynamics of severe acute respiratory syndrome transmission

Severe acute respiratory syndrome (SARS) is a serious disease with many puzzling features. We present a simple, dynamic model to assess the epidemic potential of SARS and the effectiveness of control measures. With this model, we analysed the SARS epidemic data in Beijing.The data fitting gives the basic case reproduction number of 2.16 leading to the outbreak, and the variation of the effective reproduction number reflecting the control effect. Noticeably, our study shows that the response time and the strength of control measures have significant effects on the scale of the outbreak and the lasting time of the epidemic.     

基于代理的IP包源追踪系统

提出了一种不需要ISP合作的基于代理的IP包源追查体系结构,描述了支持这种体系结构的分布式包记录代理服务器和集中式反向追查安全控制服务器的实现步骤,扩充了UnixIP包转发算法,实现了包摘要算法和自动追查系统,解决了诸如可疑包的识别、记载、追踪的安全性,节省路由器资源·通过仿真实验证明基于代理的IP包追踪方法是行之有效的·     

命名数据网络中VIP转发方案的性能优化

命名数据网络(named data networking,NDN)是信息中心网络(information centric network,ICN)的一个典型实例,是未来网络一种可能的架构.为优化NDN的转发性能,要求转发平面能根据不同的网络环境自适应地调整转发策略、选出最优的转发接口.“虚拟兴趣分组”(virtual interest packet,VIP)架构是一种基于NDN的联合动态转发与缓存的网络架构,通过VIP架构对转发方案进行了性能优化.VIP架构的转发方案使用了背压算法,该算法在网络处于轻负载状态时时延较大,为此引入偏差函数进行优化.为检测优化效果,对原始VIP架构和优化后的VIP架构的转发性能进行了仿真实验对比.实验结果显示,优化后的转发方案相比原方案在转发时延上有一定降低.且由于偏差函数的引入不会增加原转发方案的时间复杂度,因此提出的方法可以优化VIP转发方案的性能.     

基于多智能体模拟的流行病消息传播研究

设计了并实现了消息传播多智能体模拟模型(ENS),研究了个人的社会联系数量、对流行病消息的信任度和传播网络结构对流行病消息传播的影响。研究发现,随着社会联系数量的增加,流行病消息传播速度迅速增加,而直接听到消息的比例减少;随着信任度的增加,流行病消息的传播速度迅速增加,而直接听到消息的比例变化不显著;在小世界网络结构下,流行病消息的传播和遗忘速度比随机网络中慢。     

An SEIRS epidemic model with two delays and pulse vaccination*

Pulse vaccination is an effective and important strategy to eradicate an infectious disease. The authors investigate an SEIRS epidemic model with two delays and pulse vaccination. By using the discrete dynamical system determined by stroboscopic map, the authors obtain that the infectious population dies out if R△ 〈 1, and the infectious population is uniformly persistent if R^△ 〉 1. The results indicate that a short period of pulse vaccination or a large pulse vaccination rate is a sufficient condition to eradicate the disease.     

新个体的加入及初始条件对疾病传播的影响

用复杂网络理论构建人群关系网,采用随机差分方程组等价描述SIS模型与SIR模型的疾病传播过程,通过方程组,理论上得到了某些一般性结论。然后讨论了由于新个体加入使网络变复杂的传播情况,通过仿真发现,新加个体的重要度及人群关系网的拓扑结构对疾病传播过程有影响。此外,研究了SIR模型中不同初始感染个体对疾病传播的影响,当初始个体是随机选取或来自不同k-center时,传播过程有明显的不同。而且对个体数目及平均度相同的网络,在相同条件下,网络的深度越小,感染的人数越多。