一类具有常数迁入且总入口在变化的SIRI传染病模型的稳定性

讨论一类具有常数迁入率,染病类有病死且有效接触率依赖于总人数的SIRI传染病模型.给出了基本再生数σ的表达式.如果σ≤1,则疾病消除平衡点是全局稳定的;如果σ＞1,则存在唯一的传染病平衡点且是局部渐近稳定的.对具有双线性传染率和标准传染率的相应模型,进一步证明了当σ＞1时传染病平衡点的全局稳定性.     

一类具常数接触率传染病模型的稳定性分析

本文研究了一类具常数接触率的传染病模型,用上下解方法和Liapnuov泛函讨论了地方病平衡点及无病平衡点的渐近行为,得到了各自全局稳定的充分条件.     

野生鸟类传染性疾病研究进展

由于具有独特的飞行能力和极强的地理扩散能力,鸟类活动为某些传染性疾病的快速传播和扩散带来了潜在风险.自20世纪以来,以禽霍乱、禽波特淋菌病、西尼罗河热、禽流感等为代表的鸟类疾病频繁暴发,导致为数众多的野生鸟类、家禽甚至人类死亡,给社会造成巨大的经济损失.因此,有关鸟类传染性疾病的研究已引起了国内外学者的广泛关注.从鸟类传染性疾病的生态学特征、疾病对鸟类与人类社会的影响、鸟类对疾病的传播、鸟类疾病的监测、预警和防控等方面对野生鸟类的传染性疾病研究进展进行了综述.不同疾病导致的鸟类死亡量、易感物种数量、暴发频率和地理扩散等特征差异显著.20世纪以来,疾病已成为全球生物多样性的七大威胁因子之一.疾病可能造成鸟类大量死亡,从而对鸟类种群,特别是濒危鸟类种群造成严重影响.其中,人畜共患病还会导致家禽家畜甚至人类的死亡,从而对社会产生严重的影响.野生鸟类作为多种疾病传播的媒介,其移动和迁徙可能会导致疾病的传播与扩散.开展全面的监测活动和建立疾病预警体系,对于疾病的防控具有重要意义.     

一类食饵染病且垂直传染的生态流行病扩散模型的整体性态

本文研究一类具有空间扩散和食饵染病且垂直传染的的生态一流行病模型的在整体性态.首先讨论解的存在唯一性和一致有界性;其次由线性化方法得到了该模型非负平衡点的局部渐近稳定的充分条件,并构造恰当的Lyapunov泛函证明非负平衡点的全局渐近稳定性,得到了捕食者灭绝、疾病消失和疾病成为地方病的充分条件.     

具常投放率的反应扩散系统的渐近性质

本文研究一类具常投放率的人口动力学中反应扩散系统的Neumann初边值问题,应用比较函数讨论其解的渐近性态,给出稳态解的存在条件．     

一个带有ATL过程的CD4~+ T-淋巴细胞感染HTL V-I病毒的年龄结构模型分析

本文中,我们提出并分析了一个HTLⅤ-Ⅰ感染的年龄结构模型,得到了决定该模型未感染平衡点和感染平衡点的存在性和局部渐近稳定性的条件,即当一个活跃染病淋巴细胞在其整个染病期间在一个均为易感T-淋巴细胞的细胞群体中所能传染的细胞的平均数量不超过某一个阈值时,系统仅存在局部渐近稳定的未感染平衡点;当一个活跃染病淋巴细胞在其整个染病期间在一个均为易感T-淋巴细胞的细胞群体中所能传染的细胞的平均数量超过这一阈值时,未感染平衡点不稳定,此时存在局部渐近稳定的感染平衡点.     

一类具有阶段结构和隔离的种群-传染病模型

该文讨论了一类具有阶段结构和隔离的种群-传染病模型.在该模型中,假设染病者没有生育能力.通过分析讨论,得到了地方病平衡点存在的阈值条件,以及无病平衡点和地方病平衡点局部渐近稳定和全局渐近稳定的充分条件.     

鸟类的扩散行为研究进展

扩散是生物个体之间相互远离的单线性运动,是生物的基本特征之一,对种群的分布、动态及遗传结构等方面均有重要影响.扩散有出生扩散和繁殖扩散等主要形式.动物发生扩散的主要原因包括:避免近亲繁殖、减少竞争、改变繁殖地点等.近年来,扩散已经成为鸟类学研究的前沿领域.评述了鸟类扩散行为的性别差异、体质对于扩散的影响,阐述了扩散的基本过程及栖息地选择、长距离扩散等内容,同时介绍了环志标记、无线电遥测、分子生物学等研究鸟类扩散的主要方法.展望了鸟类扩散研究的发展趋势,认为新技术和新方法的应用将成为扩散生态学家关注的重要问题,未来研究将更加重视对鸟类扩散理论问题的探讨,而对鸟类扩散行为的研究成果也会更广泛地应用于濒危物种及其栖息地的保护工作中.     

一类潜伏期和染病期均传染且具非线性传染率的流行病模型

研究了一类潜伏期和染病期都传染的具非线性传染率的SEIS流行病模型,确定了各类平衡点存在的条件阈值,讨论了各平衡点的稳定性,揭示了潜伏期传染和染病期传染对流行病发展趋势的共同影响．     

生态学中一类具时滞反应扩散方程组解的性质

本文讨论一类具时滞反应扩散方程组初边值问题,得到该问题解的存在唯一性并讨论解的渐近性态．     

Determination of optimal vaccination strategies using an orbital stability threshold from periodically driven systems

We analyse a periodically driven SIR epidemic model for childhood related diseases, where the contact rate and vaccination rate parameters are considered periodic. The aim is to define optimal vaccination strategies for control of childhood related infections. Stability analysis of the uninfected solution is the tool for setting up the control function. The optimal solutions are sought within a set of susceptible population profiles. Our analysis reveals that periodic vaccination strategy hardly contributes to the stability of the uninfected solution if the human residence time (life span) is much larger than the contact rate period. However, if the human residence time and the contact rate periods match, we observe some positive effect of periodic vaccination. Such a vaccination strategy would be useful in the developing world, where human life spans are shorter, or basically in the case of vaccination of livestock or small animals whose life-spans are relatively shorter.     

Disease evolution on networks: the role of contact structure

Owing to their rapid reproductive rate and the severe penalties for reduced fitness, diseases are under immense evolutionary pressure. Understanding the evolutionary response of diseases in new situations has clear public-health consequences, given the changes in social and movement patterns over recent decades and the increased use of antibiotics. This paper investigates how a disease may adapt in response to the routes of transmission available between infected and susceptible individuals. The potential transmission routes are defined by a computer-generated contact network, which we describe as either local (highly clustered networks where connected individuals are likely to share common contacts) or global (unclustered networks with a high proportion of long-range connections). Evolution towards stable strategies operates through the gradual random mutation of disease traits (transmission rate and infectious period) whenever new infections occur. In contrast to mean-field models, the use of contact networks greatly constrains the evolutionary dynamics. In the local networks, high transmission rates are selected for, as there is intense competition for susceptible hosts between disease progeny. By contrast, global networks select for moderate transmission rates because direct competition between progeny is minimal and a premium is placed upon persistence. All networks show a very slow but steady rise in the infectious period.     

Rejection of Eimeria by foreign hosts.

Oocysts of Eimeria maxima inoculated into guinea fowl do not develop. Infection occurs when intestinal mucosa or liver tissue from guinea fowl given E. maxima is transferred to susceptible chickens. By transferring material at different times after inoculation, it was shown that most of the stages are lost between 6 and 12 h and few, if any, survive to 48 h. Sporozoites of E. tenella had low infectivity after 48 h contact in vitro with peritoneal macrophages from guinea fowls and turkeys. Sporozoites of E. grenieri from the guinea fowl appeared to be destroyed within macrophages taken from chickens.     

Estimating and interpreting secondary attack risk: Binomial considered biased

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be neglected safely when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.     

The evolution of parasite virulence and transmission rate in a spatially structured population

If the transmission occurs through local contact of the individuals in a spatially structured population, the evolutionarily stable (ESS) traits of parasite might be quite different from what the classical theory with complete mixing predicts. In this paper, we theoretically study the ESS virulence and transmission rate of a parasite in a lattice-structured host population, in which the host can send progeny only to its neighboring vacant site, and the transmission occurs only in between the infected and the susceptible in the nearest-neighbor sites. Infected host is assumed to be infertile. The analysis based on the pair approximation and the Monte Carlo simulation reveal that the ESS transmission rate and virulence in a lattice-structured population are greatly reduced from those in completely mixing population. Unlike completely mixing populations, the spread of parasite can drive the host to extinction, because the local density of the susceptible next to the infected can remain high even when the global density of host becomes very low. This demographic viscosity and group selection between self-organized spatial clusters of host individuals then leads to an intermediate ESS transmission rate even if there is no tradeoff between transmission rate and virulence. The ESS transmission rate is below the region of parasite-driven extinction by a finite amount for moderately large reproductive rate of host; whereas, the evolution of transmission rate leads to the fade out of parasite for small reproductive rate, and the extinction of host for very large reproductive rate.     

An epidemic model in a patchy environment

An epidemic model is proposed to describe the dynamics of disease spread among patches due to population dispersal. We establish a threshold above which the disease is uniformly persistent and below which disease-free equilibrium is locally attractive, and globally attractive when both susceptible and infective individuals in each patch have the same dispersal rate. Two examples are given to illustrate that the population dispersal plays an important role for the disease spread. The first one shows that the population dispersal can intensify the disease spread if the reproduction number for one patch is large, and can reduce the disease spread if the reproduction numbers for all patches are suitable and the population dispersal rate is strong. The second example indicates that a population dispersal results in the spread of the disease in all patches, even though the disease can not spread in each isolated patch.     

Discussion: The Kermack-McKendrick epidemic threshold theorem

The paper reviews the work of Kermack and McKendrick on the development of simple mathematical models of the transmission dynamics of viral and bacterial infectious agents within population of hosts. The focus of attention is centred on the notion of a threshold density of susceptible hosts to trigger an epidemic and recent extensions of this idea as expressed in the definition of a basic or case reproductive rate of infection. The main body of the paper examines recent developments of the basic Kermack-McKendrick model with an emphasis on deterministic models that describe various types of heterogeneity in the processes that determine transmission between infected and susceptible persons. Particular attention is given to the role of behavioural heterogeneity within the framework of a contact or mixing matrix which defines “who acquires infection from whom”.     

Asymptotic behavior in a deterministic epidemic model

The effects of a periodic contact rate and of carriers are considered for a generalization of Bailey's simple epidemic model. In this model it is assumed that individuals become susceptible again as soon as they recover from the infection so that a fixed population can be divided into a class of infectives and a class of susceptibles which vary with time. If the contact rate is periodic, then the number of infectives as time approaches infinity either tends to zero or is asymptotically periodic depending on whether the total population size is less than or greater than a threshold value. The behavior for large time of the number of infectives is determined for three modifications of the model which involve carriers.     

Network frailty and the geometry of herd immunity

The spread of infectious disease through communities depends fundamentally on the underlying patterns of contacts between individuals. Generally, the more contacts one individual has, the more vulnerable they are to infection during an epidemic. Thus, outbreaks disproportionately impact the most highly connected demographics. Epidemics can then lead, through immunization or removal of individuals, to sparser networks that are more resistant to future transmission of a given disease. Using several classes of contact networks-Poisson, scale-free and small-world-we characterize the structural evolution of a network due to an epidemic in terms of frailty (the degree to which highly connected individuals are more vulnerable to infection) and interference (the extent to which the epidemic cuts off connectivity among the susceptible population that remains following an epidemic). The evolution of the susceptible network over the course of an epidemic differs among the classes of networks; frailty, relative to interference, accounts for an increasing component of network evolution on networks with greater variance in contacts. The result is that immunization due to prior epidemics can provide greater community protection than random vaccination on networks with heterogeneous contact patterns, while the reverse is true for highly structured populations.     

How does the resistance threshold in spatially explicit epidemic dynamics depend on the basic reproductive ratio and spatial correlation of crop genotypes?

We examined the fraction of resistant cultivars necessary to prevent a global pathogen outbreak (the resistance threshold) using a spatially explicit epidemiological model (SIR model) in a finite, two-dimensional, lattice-structured host population. Infectious diseases in our model could be transmitted to susceptible nearest-neighbour sites, and the infected site either recovered or died after an exponentially distributed infectious period. Threshold behaviour of this spatially explicit SIR model cannot be reduced to that of bond percolation, as was previously noted in the literature, unless extreme assumptions (synchronized infection events with a fixed lag) are imposed on infection process. The resistance threshold is significantly lower than that of conventional mean-field epidemic models, and is even lower if the spatial configuration of resistant and susceptible crops are negatively correlated. Finite size scaling applied to the resistance threshold for a finite basic reproductive ratio ρ of pathogen reveals that its difference from static percolation threshold (0.41) is inversely proportional to ρ. Our formula for the basic reproductive ratio dependency of the resistance threshold produced an estimate for the critical basic reproductive ratio (4.7) in a universally susceptible population, which is much larger than the corresponding critical value (1) in the mean-field model and nearly three times larger than the critical growth rate of a basic contact process (SIS model). Pair approximation reveals that the resistance threshold for preventing a global epidemic is factor 1/(1−η) greater with spatially correlated planting than with random planting, where η is initial correlation in host genotypes between nearest-neighbour sites. Thus the eradication is harder with a positive spatial correlation (η>0) in mixed susceptible/resistant plantings, and is easier with a negative correlation (η<0). The effect of finite field size (L), which corresponded to the mean distance between sources of infections, is given by the increased resistance threshold (by the amount L^−0.75) from its infinite size limit. Implications of these results on effective planting strategies in multi-line control plans are discussed.