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.     

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%。     

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.     

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.     

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.     

一种新的振荡型DGPM(1,N|sin)模型及其应用

针对传统多变量灰色模型未能有效预测振荡序列的问题,提出一种新的振荡型DGPM(1,N|sin)模型.首先,将非线性时间周期项和时变参数引入离散灰色预测模型;然后,建立非线性规划模型,利用遗传算法确定最优参数;最后,将该模型应用于中国消费价格指数的预测中,验证了本文模型的有效性和适用性.结果显示,振荡型DGPM(1,N|sin)模型有较高的预测精度,为振荡序列的预测提供了有效方法.     

隐马尔科夫模型在乙肝发病预测中的应用

基于隐马尔科夫模型(HMM)为中国疾病预防与控制中心发布的乙肝发病数量时间序列进行建模,通过似然函数的计算而建立起一个具有2状态的单变量正态分布隐马尔科夫模型.根据模型估计结果,发现两个状态对应的乙肝发病数量的分布规律有较大差异,分别对应着乙肝疫情的低发状态和高发状态.状态之间有可能发生转换,但是转换的概率比较低.基于所估计得到的隐马尔科夫模型,可以识别出特定时刻乙肝疫情所处的状态,也可以预测未来时刻乙肝疫情所处的状态.     

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.     

基于一类时滞动力学系统对新型冠状病毒肺炎疫情的建模和预测

2019年12月,新型冠状病毒肺炎(novel coronavirus pneumonia, NCP)疫情从武汉开始暴发,几天内迅速传播到全国乃至海外.科学有效地掌控疫情发展对疫情防控至关重要.本文基于全国各级卫生健康委员会每日公布的累计确诊数和治愈数,提出一类基于时滞动力学系统的传染病动力学模型.在模型中引入时滞过程,用来描述病毒潜伏期和治疗周期.通过公布的疫情数据,首先准确反演模型的参数;其次有效地模拟目前疫情的发展,并预测疫情未来的趋势;最后分析各级政府防控措施手段的有效程度,并发现在现有的高效防控措施下,疫情将在近期好转.     

GI(1)+GI(2)+…+GI(N)/M/1排队模型

文献[1]引入了一类具有广泛应用前景的随机过程--Markov骨架过程.本文借助这类随机过程的方法研究了GI(1)+GI(2)+…+GI(N)/M/1排队模型,求出了此模型到达过程、等待时间及队长的概率分布.     

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.     

Complex dynamics of epidemic models on adaptive networks

There has been a substantial amount of well mixing epidemic models devoted to characterizing the observed complex phenomena (such as bistability, hysteresis, oscillations, etc.) during the transmission of many infectious diseases. A comprehensive explanation of these phenomena by epidemic models on complex networks is still lacking. In this paper we study epidemic dynamics in an adaptive network proposed by Gross et al., where the susceptibles are able to avoid contact with the infectious by rewiring their network connections. Such rewiring of the local connections changes the topology of the network, and inevitably has a profound effect on the transmission of the disease, which in turn influences the rewiring process. We rigorously prove that the adaptive epidemic model investigated in this paper exhibits degenerate Hopf bifurcation, homoclinic bifurcation and Bogdanov–Takens bifurcation. Our study shows that adaptive behaviors during an epidemic may induce complex dynamics of disease transmission, including bistability, transient and sustained oscillations, which contrast sharply to the dynamics of classical network models. Our results yield deeper insights into the interplay between topology of networks and the dynamics of disease transmission on networks.     

Global analysis of a delayed epidemic dynamical system with pulse vaccination and nonlinear incidence rate

This study explores the influence of epidemics by numerical simulations and analytical techniques. Pulse vaccination is an effective strategy for the treatment of epidemics. Usually, an infectious disease is discovered after the latent period, H1N1 for instance. The vaccinees (susceptible individuals who have started the vaccination process) are different from both susceptible and recovered individuals. So we put forward a SVEIRS epidemic model with two time delays and nonlinear incidence rate, and analyze the dynamical behavior of the model under pulse vaccination. The global attractivity of ‘infection-free’ periodic solution and the existence, uniqueness, permanence of the endemic periodic solution are investigated. We obtain sufficient condition for the permanence of the epidemic model with pulse vaccination. The main feature of this study is to introduce two discrete time delays and impulse into SVEIRS epidemic model and to give pulse vaccination strategies.     

Dynamics of SIS Epidemic Model with Varying Total Population and Multivaccination Control Strategies

In this paper, two susceptible‐infected‐susceptible epidemic models with varying total population size, continuous vaccination, and state‐dependent pulse vaccination are formulated to describe the transmission of infectious diseases, such as diphtheria, measles, rubella, pertussis, and so on. The first model incorporates the proportion of infected individuals in population as monitoring threshold value; we analytically show the existence and orbital asymptotical stability of positive order‐1 periodic solution for this control model. The other model determines control strategy by monitoring the proportion of susceptible individuals in population; we also investigate the existence and global orbital asymptotical stability of the disease‐free periodic solution. Theoretical results imply that the disease dies out in the second case. Finally, using realistic parameter values, we carry out some numerical simulations to illustrate the main theoretical results and the feasibility of state‐dependent pulse control strategy.     

Effects of population density on the spread of disease

The effect of population density on the epidemic outbreak of measles or measles-like infectious diseases was evaluated. Using average-number contacts with susceptible individuals per infectious individual as a measure of population density, an analytical model for the distribution of the nonstationary stochastic process of susceptible contact is presented. A 5-dimensional lattice simulation model of disease spread was used to evaluate the effects of four different population densities. A zero-inflated Poisson probability model was used to quantify the nonstationarity of the contact rate in the stochastic epidemic process. Analysis of the simulation results identified a decrease in a susceptible contact rate from four to three, resulted in a dramatic effect on the distribution of contacts over time, the magnitude of the outbreak, and, ultimately, the spread of disease. © 2001 John Wiley & Sons, Inc.     

From Individuals to Populations: A Symbolic Process Algebra Approach to Epidemiology

Is it possible to symbolically express and analyse an individual-based model of disease spread, including realistic population dynamics? This problem is addressed through the use of process algebra and a novel method for transforming process algebra into Mean Field Equations. A number of stochastic models of population growth are presented, exploring different representations based on alternative views of individual behaviour. The overall population dynamics in terms of mean field equations are derived using a formal and rigorous rewriting based method. These equations are easily compared with the traditionally used deterministic Ordinary Differential Equation models and allow evaluation of those ODE models, challenging their assumptions about system dynamics. The utility of our approach for epidemiology is confirmed by constructing a model combining population growth with disease spread and fitting it to data on HIV in the UK population. This work was supported by EPSRC through a Doctoral Training Grant (CM, from 2004–2007), and through System Dynamics from Individual Interactions: A process algebra approach to epidemiology (EP/E006280/1, all authors, 2007–2010).     

Stochastically perturbed vector-borne disease models with direct transmission

In this paper we study a stochastic epidemic model of vector-borne diseases with direct mode of transmission and its delay modification. More precisely, we extend the deterministic epidemic models by introducing random perturbations around the endemic equilibrium state. By using suitable Lyapunov functions and functionals, we obtain stability conditions for the considered models and study the effect of the delay on the stability of the endemic equilibrium. Finally, numerical simulations for the stochastic model of malaria disease transmission are presented to illustrate our mathematical findings.     

Equation-Free multiscale computational analysis of individual-based epidemic dynamics on networks

The surveillance, analysis and ultimately the efficient long-term prediction and control of epidemic dynamics appear to be some of the major challenges nowadays. Detailed individual-based mathematical models on complex networks play an important role towards this aim. In this work, it is shown how one can exploit the Equation-Free approach and optimization methods such as Simulated Annealing to bridge detailed individual-based epidemic models with coarse-grained, system-level analysis within a pair-wise representation perspective. The proposed computational methodology provides a systematic approach for analyzing the parametric behavior of complex/multiscale epidemic simulators much more efficiently than simply simulating forward in time. It is shown how steady state and (if required) time-dependent computations, stability computations, as well as continuation and numerical bifurcation analysis can be performed in a straightforward manner. The approach is illustrated through a simple individual-based SIRS epidemic model deploying on a random regular connected graph. Using the individual-based simulator as a black box coarse-grained timestepper and with the aid of Simulated Annealing I compute the coarse-grained equilibrium bifurcation diagram and analyze the stability of the stationary states sidestepping the necessity of obtaining explicit closures at the macroscopic level.     

SIR epidemics with stochastic infectious periods

We consider an SIR model for the spread of an epidemic in a closed and homogeneously mixing population, where the infectious periods are represented by an arbitrary absorbing Markov process. A version of this process starts whenever an infection occurs, and the contamination rate of the newly infected individual is a function of its state. When his process is absorbed, the individual becomes a removed case. We use a martingale approach to derive the distribution of the final epidemic size and severity for this class of models. Next, we examine some special cases. In particular, we focus on situations where the infection processes are Brownian motions and where they are Markov-modulated fluid flows. In the latter case, we use matrix-analytic methods to provide more explicit results. We conclude with some numerical illustrations.