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%。     

疫苗与隔离措施对流感暴发影响的动力学研究

2018年1月爆发了规模空前的流感,以乙型流感为主,本次流感几乎席卷了中国大部分的地区,导致多人死亡,在社会引起了较大的恐慌.为了更好的应对流感的爆发,减少累计染病量和染病峰值,实现对流感的有效预防控制,在流感病例数据的基础上,建立动力学模型来描述乙型流感爆发的传播过程,计算了基本再生数与有效再生数,并利用遗传算法拟合数据估计出了模型中的参数;通过数值模拟,探究采取疫苗和隔离措施对累计染病数和峰值的影响,得到:隔离与免疫可降低染病峰值,减小染病规模;越早采取免疫措施,越快地减少总染病人数;与采用隔离措施相比,注射疫苗能更快得降低有效再生数.为预防和控制乙型流感爆发提供可以借鉴的理论依据与可行的控制措施.     

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.     

基于回归调整CUSUM控制图的流感监测

流感事件的不可控将对公众健康构成威胁,控制图可以对流感进行监控并对流感爆发进行预警.在实际生活中,每天的流感人数不是同分布的,会受到如温度和湿度等相关因素影响,忽视这些因素可能会使控制图出现误报从而影响疾控部门的决策.考虑到这些因素,文章基于风险调整零膨胀泊松CUSUM控制图提出了一种针对于正常泊松分布的回归调整CUSUM控制图.并且通过蒙特卡洛随机模拟方法算出控制限,分析了文章提出的回归调整CUSUM控制图失控状态下的性能,并与传统CUSUM控制图进行了比较,模拟结果显示回归调整CUSUM控制图明显提高了对漂移的监测效率.最后基于文章提出的方法对香港一家医院的流感人数进行监测,并对流感爆发进行了准确的预警.     

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.     

两个城市人口相互流动的流感模型研究

研究了两个城市之间人口相互流动的具有出生死亡的SIR流感模型,获得了该模型基本再生数,并对基本再生数与移出率之间的关系作了敏感度分析.结果显示,人口的适当迁移可以有效控制流感爆发.     

基于SEIAR模型的流感的最优预防和治疗策略研究

基于流感传播的SEIAR动力学模型研究了流感爆发的最优控制策略.考虑的控制措施包括利用抗病毒药物进行预防和治疗.证明了最优解存在,利用Pontryagin极大值原理给出了动态的最优预防和治疗的设计方法,并利用四阶Runge-kutta迭代算法得到了数值解.     

流感模型的数据模拟和动力学分析

利用常微分方程定性和稳定性理论、计算机工具建立并研究了没有疫苗和带有疫苗的流感模型.根据中国疾控中心的数据,利用MATLAB进行参数模拟,得到了流感基本再生数的取值范围,并对疫苗的年生产量做出了估计;同时,求出了模型的无病平衡点和地方病平衡点,证明了无病平衡点当基本再生数小于1时是全局渐进稳定的、地方病平衡点存在时是局部稳定的.     

甲型H1N1流感早期传播的数学模型

根据甲型H1N1流感早期在我国的传播规律,给出了一种描述甲型H1N1流感早期在我国传播的数学模型,分析了模型的解及其性质,证明了在严格的防控措施下,发病者最终将会完全消失,但处于潜伏期者最终将会达到一个固定的比例,指出了甲型H1N1流感的防控工作是一项长期而艰巨的任务.     

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.     

Uncertainty and sensitivity analysis of the basic reproduction number of a vaccinated epidemic model of influenza

The basic reproduction number and the point of endemic equilibrium are two very important factors in any deterministic compartmental epidemic model as the basic reproduction number and the point of endemic equilibrium represent the nature of disease transmission and disease prevalence respectively. In this article the sensitivity analysis based on mathematical as well as statistical techniques has been performed to determine the importance of the epidemic model parameters. It is observed that 6 out of the 11 input parameters play a prominent role in determining the magnitude of the basic reproduction number. It is shown that the basic reproduction number is the most sensitive to the transmission rate of disease. It is also shown that control of transmission rate and recovery rate of the clinically ill are crucial to stop the spreading of influenza epidemics.     

The spreading frontiers of avian-human influenza described by the free boundary

In this paper,a reaction-diffusion system is proposed to investigate avian-human influenza.Two free boundaries are introduced to describe the spreading frontiers of the avian influenza.The basic reproduction numbers rF0(t)and RF0(t)are defined for the bird with the avian influenza and for the human with the mutant avian influenza of the free boundary problem,respectively.Properties of these two time-dependent basic reproduction numbers are obtained.Sufficient conditions both for spreading and for vanishing of the avian influenza are given.It is shown that if rF0(0)<1 and the initial number of the infected birds is small,the avian influenza vanishes in the bird world.Furthermore,if rF0(0)<1 and RF0(0)<1,the avian influenza vanishes in the bird and human worlds.In the case that rF0(0)<1 and RF0(0)>1,spreading of the mutant avian influenza in the human world is possible.It is also shown that if rF0(t0)>1 for any t0>0,the avian influenza spreads in the bird world.     

具有饱和治疗的禽流感动力学模型的研究

提出了一类带有治疗的禽流感动力学模型,用来分析禽流感从禽类向人类传播的过程.由于治疗禽流感的药物十分有限,提出一个带有饱和治疗的模型.通过讨论得知当禽流感疫情已经发生时,通过控制染病的禽类就可抑制禽流感在人类的传播.     

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.     

Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior

Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak.     

Avian-human influenza epidemic model with diffusion

A diffusive epidemic model is investigated. This model describes the transmission of avian influenza among birds and humans. The behavior of positive solutions to a reaction-diffusion system with homogeneous Neumann boundary conditions are investigated. Sufficient conditions for the local and global asymptotical stability are given by spectral analysis and by using Lyapunov functional. Our result shows that the disease-free equilibrium is globally asymptotically stable, if the contact rate for the susceptible birds and the contact rate for the susceptible humans are small. It suggests that the best policy to prevent the occurrence of a pandemic is not only to exterminate the infected birds with avian influenza but also to reduce the contact rate for susceptible humans with the individuals infected with mutant avian influenza. Numerical simulations are presented to illustrate the main results.     

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.     

Existence of traveling wave solutions for influenza model with treatment

To investigate the spreading speed of influenza and the influence of treatment on the spreading speed, a reaction–diffusion influenza model with treatment is established. The existence of traveling wave solutions is shown by introducing an auxiliary system and applying the Schauder fixed point theorem. The non-existence of traveling wave solutions is proved by a two-sided Laplace transform, which needs a new approach for the prior estimate of exponential decay of traveling wave solutions.