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.     

带有脉冲免疫和传染年龄的传染病模型的全局吸引性

讨论了带有脉冲免疫和传染年龄的传染病模型.传染类的恢复率是传染年龄的函数,当染病再生数小于1时,文章得到无病周期解是全局吸引的.如果总人口规模变化,也可得到类似的结论.最后,提出了带有脉冲免疫和传染年龄传染病模型待解决的问题.     

Analytic solutions to compartmental models of the HIV/AIDS epidemic

For many years compartmental models have provided useful insightsinto the spread of epidemics. Such models are usually fairlyeasy to set up, but even the simpler models have the disadvantagethat they are intractable to analytic solution. In this paperwe examine models of the HIV/AIDS epidemic, and show that theequations may be linearized in a piecewise manner over time,thus allowing analytic solutions to be obtained. Indicationsof the usefulness of this approach are provided. In particular,an analytic solution gives insight into the mechanism of theepidemic, together with a clearer picture of the sensitivityof results to changes in parameter values. Further, the processesof parameter estimation and the methodology of back-calculationalso benefit from the provision of functional forms for thestate variables.     

A state‐dependent Markov‐modulated mechanism for generating events and stochastic models

In this paper, we introduce a versatile block‐structured state‐dependent event (BSDE) approach that provides a methodological tool to construct non‐homogeneous Markov‐modulated stochastic models. Alternatively, the BSDE approach can be used to construct even a part (e.g. the arrival process) of the model. To illustrate the usefulness of the BSDE approach, several arrival patterns as well as queueing and epidemic models are considered. In particular, we deal with a state‐dependent quasi‐birth‐and‐death process that gives a constructive generalization of the scalar birth‐and‐death process and the homogeneous quasi‐birth‐and‐death process. Copyright © 2009 John Wiley & Sons, Ltd.     

Multistrain edge‐based compartmental model on networks

Multistrain diseases, which are infected through individual contacts, pose severe public health threat nowadays. In this paper, we build competitive and mutative two‐strain edge‐based compartmental models using probability generation function (PGF) and pair approximation (PA). Both of them are ordinary differential equations. Their basic reproduction numbers and final size formulas are explicitly derived. We show that the formula gives a unique positive final epidemic size when the reproduction number is larger than unity. We further consider competitive and mutative multistrain diseases spreading models and compute their basic reproduction numbers. We perform numerical simulations that show some dynamical properties of the competitive and mutative two‐strain models.     

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.     

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.     

Globally stable vaccine-induced eradication of horizontally and vertically transmitted infectious diseases with periodic contact rates and disease-dependent demographic factors in the population

Within the framework of SEIR-like epidemic models, we studied the conditions for the stable eradication of some families of vertically and horizontally transmitted infectious diseases in the case of periodically varying contact rate. By means of Floquet’s theory, we found a condition for the eradication solution to be locally asymptotically stable. We then demonstrated that the same condition guarantees also that this vaccine-induced disease-free solution is globally asymptotically stable. A model with interacting populations is also considered. In the final part of this work, we extended the model by taking into account the variation of population size, the impact of disease-related deaths and reduction of fertility.     

QUALITATIVE ANALYSIS OF AN EPIDEMIC MODEL WITH VACCINATION

An SIRS epidemic modei with vaccination, temporary immunity and vary-ing total population size is considered. The threshold of existence of endemic equilibrium is found. The disease-free equilibrium is globally asymptotically stable below the threshold, the endemic equilibrium is globally asymptotically stable above the threshold.     

组合证券投资优化模型的比较研究

本文给出基于历史收益率数据的均值一极差和均值一离差型组合证券投资优化模型,并用实例对两模型的结果进行比较。     

具有急慢性阶段的SIS流行病模型的稳定性

本文系统研究了具有急性和慢性两个阶段的SIS流行病模型.由两节构成,第一节建立和研究了具有急性和慢性两个阶段的SIS流行病模型,该模型是由三个常微分方程构成的方程组;第二节在第一节的基础上建立和研究了具有慢性病病程的SIS流行病模型;该模型既含有常微分方程,又含有偏微分方程.假设所研究的国家或地区的总人口N(t)服从增长规律: N'(t)=A—μN(t),运用微分方程和积分方程中的理论和方法,得到了这两个模型再生数R0的表达式．证明了无病平衡态的全局渐近稳定性,给出了两模型地方病平衡态的存在性和稳定性条件．     

A fractional order epidemic model and simulation for avian influenza dynamics

We present a nonlinear fractional order epidemic model to investigate the spreading dynamical behavior of the avian influenza. The population of the model contains susceptible individuals, asymptomatic but infective latent individuals, and infective individuals. We first establish the existence, uniqueness, nonnegativity, and positive invariance of the solution, then we study the reproduction number of the model and the stability of the disease‐free equilibrium. We observe that the reproduction number varies with the order of the fractional derivative ν. In terms of epidemics, this suggests that varying ν induces a change in the avian's epidemic status. Furthermore, we derive the sufficient conditions for the existence and the stability of the endemic equilibrium. Finally, we carry out some numerical simulations to validate the analytical results. We find from simulations that the solution of the fractional order model tends to a stationary state over a longer period of time with decreasing the value of the fractional derivative, and the size of epidemic decreases with decreasing ν.     

混合整数线性模型方差分量的Bayes估计

本文研究了混合整数线性模型方差分量在无信息先验分布和有信息先验分布下Bayes估计，给出了混合整数线性模型方差分量无信息和：有信息先验分布下的极大后验估计和最佳Bayes估计。     

On the use of the geometric approach to global stability for three dimensional ODE systems: A bilinear case

In this paper, we consider a general bilinear three dimensional ODE system, whose structure generalizes many mathematical models of biological interest, including many from epidemics. Our main goal is to find sufficient conditions, expressed in terms of the parameters of the system, ensuring that the geometric approach to global stability analysis, due to [M.Y. Li, J.S. Muldowney, A geometric approach to global-stability problems, SIAM J. Math. Anal. 27 (4) (1996) 1070-1083], may be successfully applied. We completely determine the dynamics of the general system, including thresholds and global stability of the nontrivial equilibrium. The obtained result is applied to several epidemic models. We further show how the role of new parameters on stability of well-established models may be emphasized.     

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.     

具有急慢性阶段的MSIS流行病模型阈值和稳定性结果

系统研究了具有急性和慢性两个阶段的MSIS流行病模型.由两节构成,第1节建立和研究了具有急慢性阶段的MSIS流行病模型;第2节在第1节的基础上建立和研究了具有慢性病病程的MSIS流行病模型.第1节的模型是四个常微分方程构成的方程组.第2节的模型既含有常微分方程,又含有偏微分方程.运用微分方程和积分方程中的理论和方法,得到了这两个模型再生数R0的表达式.证明了当R0<1时,无病平衡态是全局渐近稳定性,给出了各模型地方病平衡态的存在性和稳定性条件.     

Stability of disease free sets in epidemic models

In this paper the attractivity properties of disease free subsets are considered in the context of disease transmission models. Sufficient conditions are derived for the existence of stable disease free subsets in a general compartmental disease transmission model. The conditions are stated in terms of the system linearized along the trajectories limited to a subset of disease free states. The proof is in the framework of the classical direct method of Lyapunov. As illustrations of the result a multigroup SIRS vaccination model and a Lotka–Volterra system with prey epidemic interaction are presented.     

Twisted Extensions of Spin Models

A spin model is one of the statistical mechanical models which were introduced by V.F.R. Jones to construct invariants of links. In this paper, we give a new construction of spin models of size 4n from a given spin model of size n. The process is similar to taking tensor product with a spin model of size four, but we add some sign exchange. This construction also gives symmetric four-weight spin models of the type introduced by E. Bannai and E. Bannai.     

Branching Approximation for the Collective Epidemic Model

A new approach is developed that allows us to establish and analyze a branching-type approximation for the collective epidemic model. Firstly, a necessary and sufficient condition is obtained for the vague convergence of the final size of the epidemic to the total progeny in an appropriate branching model, as the initial number of susceptibles tends to infinity. Then, an upper bound for the L ₁ distance between the statistics under study is derived, showing inter alia that the approximation may hold even when the initial number of infectives is arbitrarily large. The results are illustrated with several particular models of special interest.