A two species competition model under the simultaneous effect of toxicant and disease

A mathematical model is proposed to study the simultaneous effects of toxicants and infectious diseases on a competing species system. It is assumed that the competing populations are adversely affected by the toxicant and one of them is vulnerable to an infectious disease. In this paper, two models are studied separately. The first model is developed to study the effect of only infectious diseases on the existence of a two competing species system in the absence of a toxicant, whereas in the second model the presence of a toxicant is also taken into account. In both the models, conditions for the existence of interior equilibria are derived. The models are analyzed using stability theory, and conditions for the nonlinear stability of the interior equilibria are obtained using Lyapunov’s direct method. Further, the models are studied numerically by taking two sets of numerical values for each model and the results are compared.     

Analysis of a stochastic recovery-relapse epidemic model with periodic parameters and media coverage

This paper addresses, motivated by mathematical work on infectious disease models, the impacts of environmental noise and media coverage on the dynamics of recovery-relapse infectious diseases. A susceptible-infectious-recovered-infectious model is formulated with both vertical transmission and horizontal transmission. The existence and uniqueness of the positive global solution is studied by constructing suitable Lyapunov-type function. Then, the existence of positive periodic solutions is verified by applying Khasminskii"s theory. The existence of positive periodic solutions indicates the continued survival of the diseases. Besides, sufficient conditions for the extinction of the diseases are obtained. Numerical simulations then demonstrate the dynamics of the solutions. The paper extends the results of the corresponding deterministic system.     

How vector feeding preference through an infectious host relates to the seasonal transmission rates in a mathematical vector‐host model

Vector‐borne diseases, such as leishmaniasis, dengue, malaria, and yellow fever, transmitted by microparasites show periodic fluctuations in their prevalence. The novelty of this research is to assess the relationship between the vector feeding preference for an infectious host and the annual seasonal transmission through a vector‐host mathematical model. For the first time, numerical simulations illustrate that by increasing the vector feeding preference value in the transmission dynamics, periodic fluctuations accentuate and the endemic equilibrium average increases in vector and host populations. Moreover, increasing the vector feeding preference value, the amplitude strengthens for the infectious host and vector populations. This periodic behavior shows a similar pattern with the Peruvian incidence data from 2000 to 2016 for Andean cutaneous leishmaniasis provided by the Ministry of Health of Peru (MINSA). In addition, using the Floquet theory, the time average method and the linear operator method provides for the first time that the basic reproduction number for a nonautonomous system depends explicitly on the vector feeding preference for the infectious host. The nonautonomous model system shows that is a threshold parameter for the local stability of the disease‐free periodic solution. Therefore, the vector feeding preference is an important factor that should be considered and attended to for future research. Public and veterinary health in Peru and other countries should consider the vector feeding preference for specific host to vector control.     

Dynamics of a stochastic SIR model with both horizontal and vertical transmission

A stochastic mathematical model with both horizontal and vertical transmission is proposed to investigate the dynamical behavior of SIR disease. By employing theories of stochastic differential equation and inequality techniques, the threshold associating on extinction and persistence of infectious diseases is deduced for the case of the small noise. Our results show that the threshold completely depends on the stochastic perturbation and the basic reproductive number of the corresponding deterministic model. Moreover, we find that large noise is conducive to control the spread of diseases and the persistent disease in deterministic model may eliminate ultimately due to the effect of large noise. Finally, numerical simulations are performed to illustrate the theoretical results.     

复杂疾病的分子网络模型研究

复杂疾病是危害人类健康的主要杀手.不同于单基因缺陷性遗传病,复杂疾病的发生发展与多个基因之间、基因与环境之间的相互作用有关,致病机理复杂,其早期诊断及治疗困难是21世纪生物医学研究的重大挑战之一.随着生物知识的不断积累和多层次"组学"数据的井喷式涌现,复杂疾病研究迎来了新的"组学革命",研究模式从以往的只关注某个分子扩展到对分子之间相互形成的生物分子网络的系统分析.作为系统生物学核心概念,生物分子网络系统整合大量生物知识和高通量生物数据,是研究复杂疾病的强有力工具.本文以分子网络为主线,以数学建模为工具来研究复杂疾病,针对复杂疾病关系和复杂疾病的发生发展机制等复杂疾病研究的关键热点问题,分析和集成高通量多层次组学数据,构建并求解生物分子网络的数学模型,在若干复杂疾病相关系统生物学问题中取得有生物学意义的结果.本文提出若干生物网络建模、分析及应用的方法并提供若干应用软件,为从系统层面理解复杂疾病提供重要参考;同时,网络模型在若干实例中的应用得到若干有生物学意义的结论,为揭示复杂疾病机理、推动疾病治疗与预防起到了一定的作用.     

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.     

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 properties of a two-scale network stochastic delayed human epidemic dynamic model

Complex population structure and the large-scale inter-patch connection human transportation underlie the recent rapid spread of infectious diseases of humans. Furthermore, the fluctuations in the endemicity of the diseases within patch dwelling populations are closely related with the hereditary features of the infectious agent. We present an SIR delayed stochastic dynamic epidemic process in a two-scale dynamic structured population. The disease confers temporary natural or infection-acquired immunity to recovered individuals. The time delay accounts for the time-lag during which naturally immune individuals become susceptible. We investigate the stochastic asymptotic stability of the disease free equilibrium of the scale structured mobile population, under environmental fluctuations and the impact on the emergence, propagation and resurgence of the disease. The presented results are demonstrated by numerical simulation results.     

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.     

Mathematical Modeling of Viral Zoonoses in Wildlife

Zoonoses are a worldwide public health concern, accounting for approximately 75% of human infectious diseases. In addition, zoonoses adversely affect agricultural production and wildlife. We review some mathematical models developed for the study of viral zoonoses in wildlife and identify areas where further modeling efforts are needed.     

K-means聚类算法在SIR传染病模型中的应用研究

基于SIR传染病模型,建立了具有K-means聚类算法的SIR元胞自动机模拟模型.通过对分别服从高斯分布和随机均匀分布的两类初始感染源的分析与模拟,给出了疾病感染半径与隔离半径对疾病传播的影响.结果显示:在两种不同类型的初试分布下,感染者的最大值分别与疾病感染传播半径和隔离半径呈正相关与负相关关系,感染者数量随时间的变化率亦呈现相同的变化规律.初始数据的不同分布类型只影响这种正负相关关系的增速.研究结果可为控制和消除传染病提供有效合理的隔离措施,为卫生部门提供防控传染病的理论支持.     

The effect of immigrant communities coming from higher incidence tuberculosis regions to a host country

We introduce a new tuberculosis (TB) mathematical model, with 25 state-space variables where 15 are evolution disease states (EDSs), which generalises previous models and takes into account the (seasonal) flux of populations between a high incidence TB country (A) and a host country (B) with low TB incidence, where (B) is divided into a community (G) with high percentage of people from (A) plus the rest of the population (C). Contrary to some beliefs, related to the fact that agglomerations of individuals increase proportionally to the disease spread, analysis of the model shows that the existence of semi-closed communities are beneficial for the TB control from a global viewpoint. The model and techniques proposed are applied to a case-study with concrete parameters, which model the situation of Angola (A) and Portugal (B), in order to show its relevance and meaningfulness. Simulations show that variations of the transmission coefficient on the origin country has a big influence on the number of infected (and infectious) individuals on the community and the host country. Moreover, there is an optimal ratio for the distribution of individuals in (C) versus (G), which minimizes the reproduction number \(R_0\). Such value does not give the minimal total number of infected individuals in all (B), since such is attained when the community (G) is completely isolated (theoretical scenario). Sensitivity analysis and curve fitting on \(R_0\) and on EDSs are pursuit in order to understand the TB effects in the global statistics, by measuring the variability of the relevant parameters. We also show that the TB transmission rate \(\beta \) does not act linearly on \(R_0\), as it is common in compartment models where system feedback or group interaction do not occur. Further, we find the most important parameters for the increase of each EDS.     

一类马氏环境中连续型传染病模型的灭绝概率

这篇文章主要研究一类马氏环境中的连续型传染病模型,即假设疾病传染率和病人减少(死亡或治愈)的发生频率及数目都受一外在马氏过程的影响.在这些假设下,我们得出初始状态为i时疾病的灭绝概率满足的积分方程,并通过Laplace-变换的方法,给出了积分方程的解.进一步,当外在马氏环境为两个状态,并且每次病人减少的数目都服从指数分布时,给出了灭绝概率Laplace-变换的明确表达式.     

基于存在基础病史易感者的SEIR模型对COVID-19传播的研究

该文基于经典的SEIR传染病模型建立了一类含有基础疾病历史人群的新冠肺炎传播模型,得到了其传播的基本再生数,确定了模型平衡点的存在性,并通过构造Lyapunov函数和利用LaSalle不变性原理论证了平衡点的全局稳定性,用数值模拟对所得理论研究结果进行了有效验证.同时,讨论了由无基础病向有基础病转化的速率系数对疾病传播的影响,发现不考虑基础病的数学模型会低估疾病传播的基本再生数和感染规模,数值模拟也显示了由无基础病向有基础病转化的速率系数对感染者人数峰值的影响.     

DOES WATER DISINFECTANT PLAY A SUPPORTIVE ROLE IN THE SPREAD OF INFECTIOUS DISEASE? A MATHEMATICAL STUDY

The waterborne diseases cause millions of deaths across the globe. It was a preconceived notion since years that ingestion of contaminated water is the only possible way for the spread of waterborne infectious diseases. But some recent studies have shown that waterborne disease can also spread as a result of human to human transmission. The use of disinfectants is a common practice to prevent a waterborne disease. We assume that the inclusion of the disinfectant, although helpful in prevention of disease, caused negative effect on individuals. In this paper, a nonlinear mathematical model has been proposed to analyze the negative effects caused by disinfectant of water on individuals. Our study shows that if the mixing of disinfectant has not been performed in a controlled manner, then it results in an increase in human to human transmission of disease. The equilibrium and stability analysis have been performed to study the nature of the model system. An extensive numerical experiment has been performed to support the analytical findings.     

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.     

A mathematical model for Babesiosis disease in bovine and tick populations

In this paper, we analyze the Babesiosis transmission dynamics on bovine and tick populations. Ticks play a role of infectious agents and vector of the protozoan Babesia hemo‐parasite. In this sense, we set out a mathematical model with constant size population for the evolution of the infected bovines with Babesiosis and analyze its qualitative dynamics. Statistical data are used to estimate some of the parameters of the model. Numerical simulations of the model varying the parameters show different scenarios about the spread of the disease. Copyright © 2011 John Wiley & Sons, Ltd.     

CUDA parallel programming for simulation of epidemiological models based on individuals

Mathematical models are of great value in epidemiology to help understand the dynamics of the various infectious diseases, as well as in the conception of effective control strategies. The classical approach is to use differential equations to describe, in a quantitative manner, the spread of diseases within a particular population. An alternative approach is to represent each individual in the population as a string or vector of characteristic data and simulate the contagion and recovery processes by computational means. This type of model, referred in the literature as MBI (models based on individuals), has the advantage of being flexible as the characteristics of each individual can be quite complex, involving, for instance, age, sex, pre‐existing health conditions, environmental factors, social habits, etc. However, when it comes to simulations involving large populations, MBI may require a large computational effort in terms of memory storage and processing time. In order to cope with the problem of heavy computational effort, this paper proposes a parallel implementation of MBI using a graphics processor unit compatible with CUDA. It was found that, even in the case of a simple susceptible–infected–recovered model, the computational gains in terms of processing time are significant. Copyright © 2015 John Wiley & Sons, Ltd.     

Large deviation limit for discrete-time, totally observed stochastic control problems with multiplicative cost

We show that a large class of discrete-time dynamic games can be obtained as a limit of stochastic control problems with multiplicative cost. Our approach consists in analyzing the large deviation properties of the Markov kernels associated with the stochastic dynamics, and allows us to give a unitary treatment of several nonlinear models.