禽流感与防疫

禽流感是一种由A型流感病毒的一种亚型(也称禽流感病毒)引起的禽类(家禽和野禽)和人的传染性疾病,被国际兽疫局定为甲类传染病,了解和认识禽流感的流行与病毒特点、传播途径、主要症状、扑灭措施及疫苗预防,对于控制和防止禽流感的发生,以及为人类健康都有着重大意义。     

The role of evolution in the emergence of infectious diseases

It is unclear when, where and how novel pathogens such as human immunodeficiency virus (HIV), monkeypox and severe acute respiratory syndrome (SARS) will cross the barriers that separate their natural reservoirs from human populations and ignite the epidemic spread of novel infectious diseases. New pathogens are believed to emerge from animal reservoirs when ecological changes increase the pathogen's opportunities to enter the human population and to generate subsequent human-to-human transmission. Effective human-to-human transmission requires that the pathogen's basic reproductive number, R(0), should exceed one, where R(0) is the average number of secondary infections arising from one infected individual in a completely susceptible population. However, an increase in R(0), even when insufficient to generate an epidemic, nonetheless increases the number of subsequently infected individuals. Here we show that, as a consequence of this, the probability of pathogen evolution to R(0) > 1 and subsequent disease emergence can increase markedly.     

Transmissibility of 1918 pandemic influenza

The 1918 influenza pandemic killed 20-40 million people worldwide, and is seen as a worst-case scenario for pandemic planning. Like other pandemic influenza strains, the 1918 A/H1N1 strain spread extremely rapidly. A measure of transmissibility and of the stringency of control measures required to stop an epidemic is the reproductive number, which is the number of secondary cases produced by each primary case. Here we obtained an estimate of the reproductive number for 1918 influenza by fitting a deterministic SEIR (susceptible-exposed-infectious-recovered) model to pneumonia and influenza death epidemic curves from 45 US cities: the median value is less than three. The estimated proportion of the population with A/H1N1 immunity before September 1918 implies a median basic reproductive number of less than four. These results strongly suggest that the reproductive number for 1918 pandemic influenza is not large relative to many other infectious diseases. In theory, a similar novel influenza subtype could be controlled. But because influenza is frequently transmitted before a specific diagnosis is possible and there is a dearth of global antiviral and vaccine stores, aggressive transmission reducing measures will probably be required.     

Influenza A （HIN1） transmission by road traffic between cities and towns

Influenza A (H1N1) was spread widely between cities and towns by road traffic and had a major impact on public health in China in 2009. Understanding regulation of its transmission is of great significance with urbanization ongoing and for mitigation of damage by the epidemic. We analyzed influenza A (H1N1) spatiotemporal transmission and risk factors along roads in Changsha, and combined diffusion velocity and floating population size to construct an epidemic diffusion model to simulate its transmission between cities and towns. The results showed that areas along the highways and road intersections had a higher incidence rate than other areas. Expressways and county roads played an important role in the rapid development stage and the epidemic peak, respectively, and intercity bus stations showed a high risk of disease transmission. The model simulates the intensity and center of disease outbreaks in cities and towns, and provides a more complete simulation of the disease spatiotemporal process than other models.     

The global impact of HIV/AIDS

The scale of the human immunodeficiency virus (HIV)/AIDS epidemic has exceeded all expectations since its identification 20 years ago. Globally, an estimated 36 million people are currently living with HIV, and some 20 million people have already died, with the worst of the epidemic centred on sub-Saharan Africa. But just as the spread of HIV has been greater than predicted, so too has been its impact on social capital, population structure and economic growth. Responding to AIDS on a scale commensurate with the epidemic is a global imperative, and the tools for an effective response are known. Nothing less than a sustained social mobilization is necessary to combat one of the most serious crises facing human development.     

Ecological and immunological determinants of influenza evolution

In pandemic and epidemic forms, influenza causes substantial, sometimes catastrophic, morbidity and mortality. Intense selection from the host immune system drives antigenic change in influenza A and B, resulting in continuous replacement of circulating strains with new variants able to re-infect hosts immune to earlier types. This 'antigenic drift' often requires a new vaccine to be formulated before each annual epidemic. However, given the high transmissibility and mutation rate of influenza, the constancy of genetic diversity within lineages over time is paradoxical. Another enigma is the replacement of existing strains during a global pandemic caused by 'antigenic shift'--the introduction of a new avian influenza A subtype into the human population. Here we explore ecological and immunological factors underlying these patterns using a mathematical model capturing both realistic epidemiological dynamics and viral evolution at the sequence level. By matching model output to phylogenetic patterns seen in sequence data collected through global surveillance, we find that short-lived strain-transcending immunity is essential to restrict viral diversity in the host population and thus to explain key aspects of drift and shift dynamics.     

Influence of extreme weather and meteorological anomalies on outbreaks of influenza A (H1N1)

Biological experiments and epidemiological evidence indicate that variations in environment have important effect on the occurrence and transmission of epidemic influenza.It is therefore important to understand the characteristic patterns of transmission for prevention of disease and reduction of disease burden.Based on case records,we analyzed the environmental characteristics including climate variables in Changsha,and then constructed a meteorological anomaly susceptive-infective-removal (SIR) model on the basis of the results of influenza A (H1N1) transmission.The results showed that the outbreak of influenza A (H1N1) in Changsha showed significant correlation with meteorological conditions;the spread of influenza was sensitive to meteorological anomalies,and that the outbreak of influenza A (H1N1) in Changsha was influenced by a combination of absolute humidity anomalous weather conditions,contact rates of the influenza patients and changes in population movements.These findings will provide helpful information regarding prevention strategies under different conditions,a fresh understanding of the emergence and re-emergence of influenza outbreaks,and a new perspective on the transmission dynamics of influenza.     

基于博弈论的社交网络谣言传播模型研究

考虑到社交网络中谣言传播存在博弈现象,基于博弈论的方法建立适用于社交网络的谣言传播模型,该模型中将节点分为传播节点和健康节点,两类节点根据自身收益选择最优策略使自身利益最大化;并得到纳什均衡。对所提模型中各类节点收益进行分析,并用收益损失参数和额外收益参数来对节点收益进行量化分析。以MATLAB为实验平台,分别选取满足不同条件的参数进行分析。结果证明了所提模型的正确性。基于博弈论对谣言传播展开研究,有助于更加深入了解社交网络上谣言传播的内在机理,也为谣言的防控提供了理论参考。     

小世界网络上流行病扩散控制策略

基于WS小世界网络和SIRS(Susceptible Infected Removed Susceptible)类流行病模型,通过数值模拟研究改变网络拓扑结构策略和改变网络拓扑结构与直接免疫相结合的策略对流行病扩散的控制。模拟结果显示,在第一种策略下,自我隔离的节点达到40%时疾病将在有限的时间内达到零感染态,而且随着自我隔离节点度被降低程度的升高疾病被控制的效果越好;在第二种策略下,疾病达到零感染态的速度比第一种策略下更快。两种策略都能够阻碍疾病的传染,控制疾病的蔓延,而且第二种策略的控制效果更佳。     

中国人感染H7N9的时空传播特性分析

为揭示中国人感染H7N9疫情的时空发展规律和演变规律,基于2013年至2017年人感染H7N9统计数据,分别进行时间序列和标准差椭圆分析。首先以月为时间单位分析疫情时间序列,识别疫情的高峰期和低谷期,在此基础上将整个疫情期进行阶段划分,并对各阶段的疫情变化规律进行分析。然后根据疫情病例的发生位置建立标准差椭圆,通过各阶段椭圆中心、方位角和面积大小的变化刻画疫情的扩散情况。最后,通过对3个典型区域在时间和空间上进行分析,进一步验证疫情的扩散规律。结果表明:每年12月至次年5月为疫情高发期,尤其是1月和2月,这2个月病例数占比高达51.74%;每年的9月为疫情低谷期,是疫情扩散阶段的划分点。在整个疫情期间,病例数呈上升、下降、爆发和归零的趋势;空间上,标准差椭圆的中心从湖州市向抚州市、衢州市转移,椭圆覆盖区域也由较为集中的沿海地区扩展到内陆地区,说明前期的控制措施不够得力,防控效果不明显,使得疫情出现了蔓延的趋势。后期采取了严格措施使疫情得到了有效控制。研究中国人感染H7N9的时空传播机制规律,可为中国传染病的预测与防治提供一定的理论和实践参考。     

Mining potential social relationship with active learning in LBSN

Rapid development of local-based social network ( LBSN ) makes it more convenient for re-searchers to carry out studies related to social network.Mining potential social relationship in LBSN is the most important one.Traditionally, researchers use topological relation of social network or tel-ecommunication network to mine potential social relationship.But the effect is unsatisfactory as the network can not provide complete information of topological relation.In this work, a new model called PSRMAL is proposed for mining potential social relationships with LBSN.With the model, better performance is obtained and guaranteed, and experiments verify the effectiveness.     

Large-scale sequencing of human influenza reveals the dynamic nature of viral genome evolution

Influenza viruses are remarkably adept at surviving in the human population over a long timescale. The human influenza A virus continues to thrive even among populations with widespread access to vaccines, and continues to be a major cause of morbidity and mortality. The virus mutates from year to year, making the existing vaccines ineffective on a regular basis, and requiring that new strains be chosen for a new vaccine. Less-frequent major changes, known as antigenic shift, create new strains against which the human population has little protective immunity, thereby causing worldwide pandemics. The most recent pandemics include the 1918 'Spanish' flu, one of the most deadly outbreaks in recorded history, which killed 30-50 million people worldwide, the 1957 'Asian' flu, and the 1968 'Hong Kong' flu. Motivated by the need for a better understanding of influenza evolution, we have developed flexible protocols that make it possible to apply large-scale sequencing techniques to the highly variable influenza genome. Here we report the results of sequencing 209 complete genomes of the human influenza A virus, encompassing a total of 2,821,103 nucleotides. In addition to increasing markedly the number of publicly available, complete influenza virus genomes, we have discovered several anomalies in these first 209 genomes that demonstrate the dynamic nature of influenza transmission and evolution. This new, large-scale sequencing effort promises to provide a more comprehensive picture of the evolution of influenza viruses and of their pattern of transmission through human and animal populations. All data from this project are being deposited, without delay, in public archives.     

基于偏微分方程的增长网络结构分析

为了研究网络的功能,需要首先研究增长网络的拓扑结构,包括网络的度分布和节点度等。当网络规模足够大时,将网络节点的度看作连续变量,根据网络演化过程中所满足的马尔科夫性,建立网络节点数量的变化方程,从而化简变形得到基于一阶双曲方程的增长网络模型。求解得到了兼具优先和随机2种连接机制的网络度分布P(k)和节点度kt0(t),同时也发现了节点度函数与双曲方程特征线之间的关系。根据网络的演化机制,通过对该增长网络模型进行随机模拟,验证了度分布与节点度理论结果的正确性。将网络的度分布计算转化为偏微分方程求解问题,将节点度的变化视为偏微分方程的特征线,将偏微分方程应用于增长网络的建模中,从而可以解析地对网络结构进行分析。     

基于互联网络拓扑结构层次模型的网络拓扑绘制方法研究

由于互联网络拓扑结构复杂,节点数目庞大。因此直接绘制几乎是不可能的。借助Transit-Stub模型,把互联网分成不同的层和模块。通过对单个层和模块的绘制,就可以展示某一域内的网络拓扑结构。这个域是整个网络的局部网络拓扑结构。最后,针对局部网络拓扑介绍了绘制算法。     

Seasonal dynamics of recurrent epidemics

Seasonality is a driving force that has a major effect on the spatio-temporal dynamics of natural systems and their populations. This is especially true for the transmission of common infectious diseases (such as influenza, measles, chickenpox and pertussis), and is of great relevance for host-parasite relationships in general. Here we gain further insights into the nonlinear dynamics of recurrent diseases through the analysis of the classical seasonally forced SIR (susceptible, infectious or recovered) epidemic model. Our analysis differs from other modelling studies in that the focus is more on post-epidemic dynamics than the outbreak itself. Despite the mathematical intractability of the forced SIR model, we identify a new threshold effect and give clear analytical conditions for predicting the occurrence of either a future epidemic outbreak, or a 'skip'-a year in which an epidemic fails to initiate. The threshold is determined by the population's susceptibility measured after the last outbreak and the rate at which new susceptible individuals are recruited into the population. Moreover, the time of occurrence (that is, the phase) of an outbreak proves to be a useful parameter that carries important epidemiological information. In forced systems, seasonal changes can prevent late-peaking diseases (that is, those having high phase) from spreading widely, thereby increasing population susceptibility, and controlling the triggering and intensity of future epidemics. These principles yield forecasting tools that should have relevance for the study of newly emerging and re-emerging diseases controlled by seasonal vectors.     

境外输入性病例对疫情防控的影响——基于SEIDR传染病模型

 为探究新型冠状病毒肺炎疫情因全球跨境流动带给中国境内疫情防控的影响,根据经典传染病动力学模型,提出考虑了境外输入性病例的SEIDR传染病模型。模型将入境人员分为未经海关入境人员和通过海关入境人员,设计了“境内有疫情,境外无输入”“境内无疫情,境外有输入”“境内有疫情,境外有输入”等3种情形。通过计算各类情形下感染人数峰值和持续的时间范围,比较不同类型的入境方式对境内疫情的影响和对境内医疗资源造成的压力。根据研究结果,建议根据疫情风险程度和资源条件采取检测措施;对未经海关入境的人员严格防控,对通过海关入境人员进行闭环管理;动态调整入境隔离措施和隔离期,在保证境内外疫情防控的提前下逐步恢复国际交流;整合医疗资源,提高配置效率,缓解境内资源占用压力。     

The scaling laws of human travel

The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.     

基于熵结合的活动轮廓分割模型

针对传统活动轮廓对图像分割鲁棒性较差的问题, 将基于区域的轮廓模型和基于梯度的轮廓模型通过图像熵与图像梯度和进行结合。通过图像熵与图像梯度和建立基于梯度与基于区域结合的活动轮廓模型。将水平集函数嵌入到模型中, 对模型结果进行连续分割, 并进行拓扑变化。采用窄带方法进行快速演化。实验证明, 该方法有较好的鲁棒性和较快的分割速度, 对图像分割理论的发展提供了新的研究途径。     

基于免疫进化策略的神经网络优化方法

对神经网络的研究多年来主要集中于网络权值优化或结构优化上,却忽略了神经网络结构与权值之间密不可分的联系.针对上述问题,将免疫系统中的浓度机制和记忆机制引入进化策略,提出了一种基于免疫进化策略的神经进化算法,在优化网络拓扑结构的同时优化网络的连接权值.进一步地,用Cauchy变异算子代替传统的Gauss变异算子,以获得更为理想的全局收敛效果.理论分析和仿真结果表明,免疫进化策略能够很好地保持种群多样性,避免未成熟收敛,采用免疫进化策略设计神经网络具有良好的全局收敛性能和快速学习网络结构和网络权值的能力.