按时滞转化的阶段结构SIS传染病模型

对一类按时滞转化的具有两个阶段结构的SIS传染病模型进行了分析,得到了传染病最终消除和成为地方病的阈值.即当传染率小于该阀值时,传染病最终消除;反之,此种传染病将成为地方病.     

基于白噪声的网络传染病模型动力学分析北大核心CSCD

该文基于确定性网络传染病模型,建立了白噪声影响下的随机网络传染病模型,证明了模型全局解的存在唯一性,利用随机微分方程理论得到了传染病随机灭绝和随机持久的充分条件.结果表明,白噪声对网络传染病传播动力学有很大的影响,白噪声能有效抑制传染病的传播,大的白噪声甚至能让原本持久的传染病变得灭绝.最后,通过数值模拟验证了理论结果.     

具有非线性感染力的一类SIR传染病模型的阈值分析

文章针对一类SIR传染病模型进行了改进,考虑了非线性感染力对阈值的影响.主要分四种情形对非线性感染力下的传染病阈值进行了计算与分析.对结果分析可知,传染病的传播阈值与非线性感染力有着密切关系,同时,免疫率μ对传染病阈值λ_c也起着非常关键的决定性作用.     

一类时滞SIS传染病模型的讨论

对一类具有生理阶段结构的SIS传染病模型进行了分析，得到了传染病最终消除和成为地方病的阈值．     

传染病模型的研究及应用

分析了传染病的传播扩散特点,建立了传染病传播扩散的微分方程模型.利用最大似然估计法对模型中的参数进行了估计.并以SARS传染扩散为例,利用网上的公开数据对模型进行了检验,所得结果与实际情况一致.此模型为传染病的预防和控制提供了理论依据.     

基于媒体报道的Filippov传染病模型的全局动力学

在经典传染病模型的基础上,通过考虑阈值策略,研究了一类基于媒体报道的不连续的传染病模型.利用Filippov意义下的右端不连续微分方程理论,对阈值策略下传染病模型的动力学行为进行了定性分析,并利用Poincaré映射研究了无病平衡点、地方病平衡点及伪平衡点的全局渐近稳定性.     

一类具有时滞传染病模型的稳定性

讨论了具有双时滞的SIS传染病模型.研究了一个边界平衡点的全局稳定性和正平衡点的局部稳定性,得到了传染病最终消失和成为地方病的阈值.     

基于灰色微分方程的传染病预测

本文归纳总结了传染病的几类预测方法,并通过一个例子,将灰色模型和微分方程模型结合,用于传染病的预测.     

基于网络媒体信息的传染病传播模型及其仿真研究

当一种突发传染病开始流行时,政府、媒体会以各种形式告知民众,有防范意识的民众将采取一定的防范措施来降低感染率.考虑面对一种突发传染病,将易感群体划分为具有防范意识和不具有防范意识两种群体,利用生命周期理论,分析网络媒体信息报道对传染病传播的影响,以此为依据建立一种改进的传染病传播模型(MSI).利用网络大数据得到对传染病有防范意识群体的观测值信息,利用神经网络技术对模型MSI的参数进行反演.然后对模型MSI数值仿真得到传染病传播过程,提出了相应的控制措施.     

一类传染病持续生存和消亡的阈值

利用齐次向量场与其诱导向量场的关系对一类传染病模型进行了进一步研究,讨论了其平衡点的存在性和稳定性,求出了该类传染病持续生存和最终消亡的阈值.     

MANAGING HARVESTING TO MINIMIZE THE IMPACT OF EPIDEMICS ON WILD FISH STOCKS

ABSTRACT. Epidemic diseases inflict substantial damage to stocks of harvested species. Epidemic waves can be predictable away from their origin. I use a classical epidemiology model to investigate the interaction of harvesting strategy with an epidemic. The effect of reducing populations by harvesting before the epidemic depends upon the nature of the epidemic's survivors. If these have recovered following infection, then pre‐epidemic fishing optimizes the harvest, but reduces long‐term survival. However, if these survivors avoided infection, then increased pre‐epidemic fishing effort can increase post‐epidemic populations; survival is maximized by reducing the pre‐epidemic population to the threshold required to propagate infection. Post‐epidemic harvesting provides poor returns and damages stocks. Optimal stock management strategy in the face of a predicted epidemic depends upon balancing harvesting and conservation of stocks complimentary or antagonistic goals, depending on the nature of the epidemic.     

具有阶段结构的SI传染病模型

本文对一类具有两个阶段结构的SI传染病模型进行了分析，得到了传染病最终消除和成为地方病的阈值。     

猩红热和流脑流行过程的混沌分维研究

以辽宁省本溪市 1 955～ 1 996年的猩红热和流脑逐月发病的数据为根据 ,利用混沌动力学中“相空间技术”,对流行病过程进行能量谱分析及混沌分析 ,发现流脑的流行过程是混沌的 ,猩红热的流行过程是周期的 .本溪流脑数据的混沌迭代模型是 Xt+1=r X2texp{ -0 .0 0 3394 ( Xt-1 4 .4 1 0 96) 2 } ,在模型参数变化范围内 ,经历了周期状态、混沌状态、吸引不动的稳定状态之间的转移 ,这表明流脑流行过程是复杂的 ,给出流行的“阈值”,以控制其流行涨落 ;求出流脑的关联分维是 2 .82 1 .为时间序列的分析研究提供了一种新方法 .     

Dynamics of stochastic heroin epidemic model with L\""evy jumps

People have paid the surge of attention to the prevention and the control of the heroin epidemic for the number of drug addicts is increasing dramatically. In the study of the heroin epidemic, modeling is an important tool. So far many heroin epidemic models are often characterized by ordinary differential equations (ODEs) and many results about them have been obtained. But unfortunately, there is little literature of stochastic heroin epidemic model with jumps. Based on this point, this paper establishes a class of heroin epidemic models---stochastic heroin epidemic model with L\"evy jumps. Under some given conditions, the existence of the global positive solution of such model is first obtained. We then study the asymptotic behavior of this model by applying the Lyapunov technique.     

Approximations for the Long-Term Behavior of an Open-Population Epidemic Model

A simple stochastic epidemic model incorporating births into the susceptible class is considered. An approximation is derived for the mean duration of the epidemic. It is proved that the epidemic ultimately dies out with probability 1. The limiting behavior of the epidemic conditional on non-extinction is studied using approximation methods. Two different diffusion approximations are described and compared.     

社会防控与信息公开：重大传染病疫情的演化机理与预测模型

世界各国对新冠肺炎疫情的抗疫模式产生了严重的分歧。本文根据中国新冠肺炎疫情防控的体制动员成功经验,将社会公共卫生防控措施的博弈因素与传染病模型相结合,构建了重大传染病疫情演化机理与情境预测的演化博弈模型。通过将传染病传播模型中感染系数参数加以内生化,解释了社会动员体制在疫情初期防控中的关键作用。最后使用感染系数内生化的SI模型分别对美国、意大利和中国三种抗疫模式进行Logistic方程拟合和峰值点分析,并将结果进行比对。本文研究表明,在缺少有效疫苗的情况下,采取隔离和政府疫情信息公开的中国疫情防控模式,在新型重大传染病疫情防控过程中发挥着关键性作用。     

人工神经网络在SARS疫情分析与预测中的应用

讨论人工神经网络在 SARS疫情分析与预测中的应用 .采用三层结构的反向传播网络 ( Backpropagation network,简称 BP网络 ) ,对 SARS在中国的传播与流行趋势及控制策略建立了网络模型 .并利用实际数据拟合参数 ,针对北京、山西的疫情进行了计算仿真 .结果表明 ,该网络模型算法收敛速度较快 ,预测精度很高     

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.