甲型H1N1流感的微分流行病模型与分析

从经典的SIR模型入手,在考虑隔离、治愈后的免疫能力、迁移及防控因子等因素后,建立了适合于甲型H1N1流感的微分方程模型,对其平衡态进行了稳定性分析.另外,考虑到"贫"数据信息的特点,在简化模型后,结合国内H1N1流感数据进行模型的求解和预测,结果表明拟合效果非常好.可以看到,起初确诊人数急剧上升,在11月左右达到最大值,随后有减缓趋势,大约在80天后灭亡.     

基于Kolmogorov前向方程评估甲型H1N1流感疫情的动态变化北大核心CSCD

基于个体水平的传染病模型可以揭示随机性在传染病疫情防控中的重要作用.研究此类模型的普遍方法是通过事件驱动的、大量重复的随机模拟来确定预测变量的范围.而基于Kolmogorov前向方程(KFE)研究个体水平的传染病模型,不仅不需要大量的重复模拟来确定预测变量的范围,而且可以同时考虑每种状态发生的概率.因此,基于2009年西安市第八医院甲型H1N1流感数据,建立了基于社交网络的个体决策心理模型,以确定行为改变率;进一步地,为得到传染病传播过程中各状态的概率分布,基于改进的个体SIR模型,通过Markov过程推导出KFE.结果表明:通过数值求解KFE可以得到整个爆发过程中每种状态发生的概率分布、最严重的时间段及相应的概率,从而能更快、更准确地了解甲型H1N1疫情的传播过程,因此有助于高效地进行甲型H1N1疫情防控.     

甲型H1N1流感传染人数的灰色预测模型研究

就我国甲型H1N1流感传染人数的预测运用灰色系统理论建立了GM(1,1)模型和1阶残差修正模型GMε(1,1),并分别作了精度分析研究了GMε(1,1)的变化趋势,提出了临界值和有效域概念.用MATLAB确定了模型参数及模型预测值.     

具有年龄结构的传染病SIR流行病模型的研究

给出美国流感监测网络统计的2008年10月至2009年9月流感症状患者数在四个年龄段的分布,结合当前H1N1新型流感发病的特点,提出一年龄结构型的流感传播模型,讨论了这个模型的应用和优点.     

一类潜伏期具有常数输人率的SEIR模型在流感防控中的应用

针对流感病毒具有的潜伏性、隐性感染者的流动难于防控性、较高的病死率及治愈后拥有的免疫力等特性建立了潜伏期具有常数输入率的SEIR传染病模型.证明了疾病模型仅存在地方病平衡点,并且是全局渐近稳定的,给出了流感防控过程中总人口输入控制及针对染病者占总人数百分比不同情况下的对隐性染病者输入比例控制值的计算公式,并对甲型H1N1流感病毒相应数据数值模拟.     

甲型H1N1流感的死亡率高吗——Bayes计量分析框架下的建模与估算

在全球甲型H1N1流感大流行背景下,本文在充分考虑各国甲流感死亡率可能存在个体混合效应、独立效应、相关效应及空间相关效应基础上,运用Bayes计量分析框架下的模型选择标准确定描述各国甲流感死亡率的最优模型,并基于该模型对不同国家甲流感死亡率进行估算。结果显示:个体独立、空间相关效应模型能很好拟合各国甲流感疫情统计数据,利用该模型估算的全球甲流感平均死亡率为0.577%。     

隔离！

甲型H1N1流感疫情在全球多国爆发，至今，流感病例仍呈增加态势。由于中国政府高度重视，并采取了有效的防范与隔离措施，疫情在我国的扩散得到了最大限度的控制。     

甲流病毒传播的年龄结构模型的稳定性

研究甲型H1N1流感病毒的传播规律,建立年龄结构具有接种措施的SEIR流行病模型,给出了疾病流行的阈值并证明了地方病平衡点的稳定性问题.最后根据一些实际数据,进行数值模拟进而对模型的合理性加以完善,借助模型预测下一阶段甲流爆发的可能性并提出相关应对措施.     

封闭空间中新型冠状病毒肺炎传播模型:以日本“钻石公主号”邮轮为例

该文以新型冠状病毒(SARS-Cov-2)在日本钻石公主号邮轮上传播为例,通过建立简单的易感者-感染者传染病模型,研究在封闭空间中新冠病毒肺炎(COVID-19)的传播机制.动力学分析和数值拟合预测了疾病传播过程和最终结果,讨论了不同隔离措施对疾病传播进程的影响,并给出防控策略建议.     

带接种和抗病毒治疗的流感传播模型

疫苗注射和抗病毒治疗是两种控制流感传播的重要途径,然而随着耐药菌株即抗药毒株的产生使得抗生素失效.2013年在中国新出现的人感染H7N9病例说明了病毒变异对人类造成的潜在威胁.建立数学模型,研究了抗病毒治疗和疫苗注射对流感传播的动力学行为的影响.模型中包括药物敏感感染群体和抗药性群体.通过分析传染性和抗药性个体的再生数Rsc和RRc得到了决定两者竞争结果的阈值.通过对各平衡点的稳定性分析,由Matlab模拟得到结论:高水平的抗病毒治疗有可能导致患病者的增加,增加程度要受到其他因素包括疫苗接种速率和抗药性的发展,所以抗病毒治疗应该适可而止.     

A fractional order epidemic model for the simulation of outbreaks of influenza A(H1N1)

In this paper, we propose a nonlinear fractional order model in order to explain and understand the outbreaks of influenza A(H1N1). In the fractional model, the next state depends not only upon its current state but also upon all of its historical states. Thus, the fractional model is more general than the classical epidemic models. In order to deal with the fractional derivatives of the model, we rely on the Caputo operator and on the Grünwald–Letnikov method to numerically approximate the fractional derivatives. We conclude that the nonlinear fractional order epidemic model is well suited to provide numerical results that agree very well with real data of influenza A(H1N1) at the level population. In addition, the proposed model can provide useful information for the understanding, prediction, and control of the transmission of different epidemics worldwide. Copyright © 2013 John Wiley & Sons, Ltd.     

Stochastic SIR model with jumps

People have always attached importance to the prevention and the control of the epidemic disease. The study of the epidemic model provides us a powerful tool. Unfortunately the previous model cannot be applied to massive diseases, such as avian influenza. Therefore we need to revise the model. In this paper, we take the lead in using the stochastic differential equation with jumps to study the asymptotic behavior of the stochastic SIR model.     

Modeling influenza progression within a continuous-attribute heterogeneous population

We consider three attributes of an individual that are critical in determining the temporal dynamics of pandemic influenza: social activity, proneness to infection, and proneness to shed virus and spread infection. These attributes differ by individual, resulting in a heterogeneous population. We develop discrete-time models that depict the evolution of the disease in the presence of such population heterogeneity. For every individual, the value for each of the three describing attributes is viewed as an experimental value of a continuous random variable. The methodology is simple yet general, extending more traditional discrete compartmental models that depict population heterogeneity. Illustrative numerical examples show how individuals who have much larger-than-average values for one or more of the attributes drive the influenza wave, especially in the early generations of the pandemic. This heterogeneity-driven pandemic physics carries important policy implications. We conclude by using contact data in four European countries to demonstrate empirical uses of our model.     

Living with influenza: Impacts of government imposed and voluntarily selected interventions

Focusing on mitigation strategies for global pandemic influenza, we use elementary mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. A spreadsheet model of infection spread between several linked heterogeneous communities is based on analytical calculations and Monte Carlo simulations. Since human behavior will likely change during the course of a pandemic, thereby altering the dynamics of the disease, we incorporate a feedback parameter into our model to reflect altered behavior. Our results indicate that while a flu pandemic could be devastating; there are coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak.     

Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 H1N1 outbreak in Arizona

This paper aims to improve the accuracy of standard compartment models in modeling the dynamics of an influenza pandemic. Standard compartment models, which are commonly used in influenza simulations, make unrealistic assumptions about human behavioral responses during a pandemic outbreak. Existing simulation models with public avoidance also make a rigid assumption regarding the human behavioral response to influenza. This paper incorporates realistic assumptions regarding individuals’ avoidance behaviors in a standard compartment model. Both the standard and modified models are parameterized, implemented, and compared in the research context of the 2009 H1N1 influenza outbreak in Arizona. The modified model with heterogeneous coping behaviors forecasts influenza spread dynamics better than the standard model when evaluated against the empirical data, especially for the beginning of the 2009–2010 normal influenza season starting in October 2009 (i.e., the beginning of the second wave of 2009 H1N1). We end the paper with a discussion of the use of simulation models in efforts to help communities effectively prepare for and respond to influenza pandemics.