具有年龄结构的传染病模型动力学分析

有史以来,传染病严重危害人类的健康和社会安定,历史证明,传染病的肆虐给人类带来的巨大的痛苦和灾难,虽然人类在治疗传染病方面已经取得了很多有效的成果,但是还面临着传染病所带来的严峻的威胁,我因此们需要对传染病的发病机理、传染规律和治疗方法作进一步研究,本文根据SARS建立了一种新模型,并针对具有年龄结构的SARS传染病模型进行动力学分析。     

解析一类SIS和SIR传染病模型的稳定性

借助微分方程建立传染病SIS模型和SIR模型,进一步研究了一类SIS和SIR传染病模型,得出了决定SIS传染病是否发生的阈值;解析了SIR模型无病平衡点和地方平衡点的稳定性.     

具有阶段结构的SIS传染病模型

讨论了具有阶段结构的SIS传染病模型,给出了传染病最终消除和成为地方病的阈值。     

我国法定管理的传染病

1989年2月21日第七届全国人民代表大会常务委员会第六次会议通过的《中华人民共和国传染病防治法》,根据传染病的危害程度和应采取的监督、监测、管理措施,将全国发病率较高,流行面较大,危害严重的35种急性和慢性传染病,列为法定管理的传染病。在管理方法上,将35种传染病分为甲、乙、丙三类,实行分类管理。对甲类传染病实行强制管理,对乙类传染病实行严格管理,对丙类传染病实行监测管理。这三类传染病分别是:     

传染病临床研究信息系统的建立

目的:建立传染病临床研究信息系统,实现传染病临床研究的信息化管理.方法:按照FDA 21 CFR Part11规则,基于J2EE 技术、Struts三层体系架构以及Hibernate数据库技术,建立传染病临床研究信息系统.结果:开发了传染病临床研究信息系统,能够满足患者基本信息收集、病历管理、病情随访、目标数据挖掘及科研标本管理等多方面需求.结论:该信息系统满足传染病临床病例信息收集与科研的个性化需求,将显著增强传染病病例数据收集的效率和质量,为传染病临床与科研工作提供强有力的信息化保障.     

提高医院传染病管理的实践

目的：总结解放军第302医院在防控传染病方面所践采取的措施,提高医院传染病管理相关工作。方法：完善传染病管理组织,明确责任;认真落实传染病管理相关制度;加强相关知识培训,强化医护人员责任意识,提高能动性、自觉性。总结分析以上相关措施实施后,2005年-2010年期间传染病报告情况。结果：2005年-2010年每年传染病疫情报告卡填写完整率逐渐提高,医院传染病报告漏报率呈逐年下降趋势,近两年呈现填写完整无漏报的情况。结论：提高医院传染病管理的相关措施的实施,使医护人员对的传染病防控意识有所提高,做到早发现、早报告、早隔离、早治疗,降低传染病传播风险,有效减少了医院交叉感染的发生,保障人们的生命健康和社会的发展稳定。     

一类传染病动力学时滞模型的分析

研究了一类传染病动力学模型,用摄动理论讨论了相应的非线性时滞问题,得到了被传染病感染的人群数与健康人群数比例的变化规律的渐近表达式,从而揭示了传染病的潜伏期和传染期对疾病传播的影响和作用.本文的研究为解决这一类非线性时滞模型提供了一种有效的方法.     

具有种群动力的时滞SI模型

对一类具有幼年和成年两个生理阶段结构和时滞的Logistic种群动力的SI传染病模型进行了分析,得到了传染病最终消除和成为地方病的阈值．     

CRISPR基因组编辑技术在传染病致病机理研究和防诊治中的应用

CRISPR基因组编辑技术在基因操作和传染病研究等方面展现出巨大的应用前景,对于有效控制和治愈传染病具有重要价值。通过其构建的细胞、类器官和动物疾病模型,为探索传染病相关分子机制提供了极大便利。CRISPR筛选技术使得高通量鉴定传染病相关风险因子成为可能。基于CRISPR的新型分子诊断工具为病原体的检测提供了更灵敏和快速的方法。利用CRISPR工具敲入抗性基因或破坏风险基因和病毒基因组,有望实现预防或治疗传染病。本综述讨论了CRISPR基因组编辑技术在疾病模型制备、传染病风险因子筛选、病原体诊断和传染病防治中的应用,以期为后续传染病的研究和防诊治提供参考。     

一类传染病模型的扩散性质

讨论了扩散对传染病模型的阈值的影响、扩散对传染病模型中染病者人数的影响以及扩散对传染病模型的平衡点的几何性质的影响．     

Contributions to the mathematical theory of epidemics—I

(1) A mathematical investigation has been made of the progress of an epidemic in a homogeneous population. It has been assumed that complete immunity is conferred by a single attack, and that an individual is not infective at the moment at which he receives infection. With these reservations the problem has been investigated in its most general aspects, and the following conclusions have been arrived at. (2) In general a threshold density of population is found to exist, which depends upon the infectivity, recovery and death rates peculiar to the epidemic. No epidemic can occur if the population density is below this threshold value. (3) Small increases of the infectivity rate may lead to large epidemics; also, if the population density slightly exceeds its threshold value the effect of an epidemic will be to reduce the density as far below the threshold value as initially it was above it. (4) An epidemic, in general, comes to an end, before the susceptible population has been exhausted. (5) Similar results are indicated for the case in which transmission is through an intermediate host.     

The effects of disease dispersal and host clustering on the epidemic threshold in plants

For an epidemic to occur in a closed population, the transmission rate must be above a threshold level. In plant populations, the threshold depends not only on host density, but on the distribution of hosts in space. This paper presents an alternative analysis of a previously presented stochastic model for an epidemic in continuous space (Bolker, 1999, Bull. Math. Biol. 61, 849–874). A variety of moment closures are investigated to determine the dependence of the epidemic threshold on host spatial distribution and pathogen dispersal. Local correlations that arise during the early phase of the outbreak determine whether a true global epidemic will occur.     

Reproduction numbers and thresholds in stochastic epidemic models. I. Homogeneous populations.

We compare threshold results for the deterministic and stochastic versions of the homogeneous SI model with recruitment, death due to the disease, a background death rate, and transmission rate beta cXY/N. If an infective is introduced into a population of susceptibles, the basic reproduction number, R0, plays a fundamental role for both, though the threshold results differ somewhat. For the deterministic model, no epidemic can occur if R0 less than or equal to 1 and an epidemic occurs if R0 greater than 1. For the stochastic model we find that on average, no epidemic will occur if R0 less than or equal to 1. If R0 greater than 1, there is a finite probability, but less than 1, that an epidemic will develop and eventuate in an endemic quasi-equilibrium. However, there is also a finite probability of extinction of the infection, and the probability of extinction decreases as R0 increases above 1.     

Endemic disease in environments with spatially heterogeneous host populations

The main interest in epidemic models stems from their use in uncovering certain qualitative features of epidemic processes. A deterministic model of a general epidemic in a population with an arbitrary number of separate population centers is presented. The mixing within each center is assumed to be homogeneous, and the usual threshold theorem holds for each population. The mixing between centers is nonhomogeneous. This model is used to identify the necessary and sufficient conditions under which a disease will become endemic in the general population when each population center is below the threshold required for establishment of the disease and does not mix with other centers. These conditions depend critically on the concavity of the infection rate function with respect to the length of exposure time. The application of these results to host-vector models is discussed.     

Interval estimates for epidemic thresholds in two-sex network models

Epidemic thresholds in network models of heterogeneous populations characterized by highly right-skewed contact distributions can be very small. When the population is above the threshold, an epidemic is inevitable and conventional control measures to reduce the transmissibility of a pathogen will fail to eradicate it. We consider a two-sex network model for a sexually transmitted disease which assumes random mixing conditional on the degree distribution. We derive expressions for the basic reproductive number (R(0)) for one and heterogeneous two-population in terms of characteristics of the degree distributions and transmissibility. We calculate interval estimates for the epidemic thresholds for stochastic process models in three human populations based on representative surveys of sexual behavior (Uganda, Sweden, USA). For Uganda and Sweden, the epidemic threshold is greater than zero with high confidence. For the USA, the interval includes zero. We discuss the implications of these findings along with the limitations of epidemic models which assume random mixing.     

The Kermack-McKendrick epidemic model revisited

The Kermack-McKendrick epidemic model of 1927 is an age of infection model, that is, a model in which the infectivity of an individual depends on the time since the individual became infective. A special case, which is formulated as a two-dimensional system of ordinary differential ordinary differential equations, has often been called the Kermack-McKendrick model. One of the products of the SARS epidemic of 2002-2003 was a variety of epidemic models including general contact rates, quarantine, and isolation. These models can be viewed as age of infection epidemic models and analyzed using the approach of the full Kermack-McKendrick model. All these models share the basic properties that there is a threshold between disappearance of the disease and an epidemic outbreak, and that an epidemic will die out without infecting the entire population.     

An age-dependent epidemic model with application to measles

A combined epidemic-demographic model is developed which models the spread of an infectious disease throughout a population of constant size. The model allows for births, deaths, temporary or permanent immunity, and immunization. The relationship of this model to previously studied epidemic and demographic models is illustrated. An advantage of this model is that all epidemic and demographic parameters may be estimated. The stability of the equilibrium point corresponding to the elimination of the disease is studied and a threshold value is found which indicates whether the disease will die out or remain endemic in the population. The application of the model to measles indicates that immunization levels needed to reduce the incidence to near zero may not be as high as previously predicted.     

Disease spread in small-size directed networks: Epidemic threshold, correlation between links to and from nodes, and clustering

Network epidemiology has mainly focused on large-scale complex networks. It is unclear whether findings of these investigations also apply to networks of small size. This knowledge gap is of relevance for many biological applications, including meta-communities, plant–pollinator interactions and the spread of the oomycete pathogen Phytophthora ramorum in networks of plant nurseries. Moreover, many small-size biological networks are inherently asymmetrical and thus cannot be realistically modelled with undirected networks. We modelled disease spread and establishment in directed networks of 100 and 500 nodes at four levels of connectance in six network structures (local, small-world, random, one-way, uncorrelated, and two-way scale-free networks). The model was based on the probability of infection persistence in a node and of infection transmission between connected nodes. Regardless of the size of the network, the epidemic threshold did not depend on the starting node of infection but was negatively related to the correlation coefficient between in- and out-degree for all structures, unless networks were sparsely connected. In this case clustering played a significant role. For small-size scale-free directed networks to have a lower epidemic threshold than other network structures, there needs to be a positive correlation between number of links to and from nodes. When this correlation is negative (one-way scale-free networks), the epidemic threshold for small-size networks can be higher than in non-scale-free networks. Clustering does not necessarily have an influence on the epidemic threshold if connectance is kept constant. Analyses of the influence of the clustering on the epidemic threshold in directed networks can also be spurious if they do not consider simultaneously the effect of the correlation coefficient between in- and out-degree.