The impact of the global and local awareness diffusion on epidemic transmission considering the heterogeneity of individual influences

In this paper, we propose a coupled awareness—epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneities of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals’ receptive preference for information and the impacts of individuals’ perceived local awareness ratio and individuals’ perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established by the microscopic Markov chain approach and the mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals’ responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.

     

Travelling Wave Solutions in Multigroup Age-Structured Epidemic Models

Age-structured epidemic models have been used to describe either the age of individuals or the age of infection of certain diseases and to determine how these characteristics affect the outcomes and consequences of epidemiological processes. Most results on age-structured epidemic models focus on the existence, uniqueness, and convergence to disease equilibria of solutions. In this paper we investigate the existence of travelling wave solutions in a deterministic age-structured model describing the circulation of a disease within a population of multigroups. Individuals of each group are able to move with a random walk which is modelled by the classical Fickian diffusion and are classified into two subclasses, susceptible and infective. A susceptible individual in a given group can be crisscross infected by direct contact with infective individuals of possibly any group. This process of transmission can depend upon the age of the disease of infected individuals. The goal of this paper is to provide sufficient conditions that ensure the existence of travelling wave solutions for the age-structured epidemic model. The case of two population groups is numerically investigated which applies to the crisscross transmission of feline immunodeficiency virus (FIV) and some sexual transmission diseases.     

复杂动态网络传播动力学

病毒、谣言、舆论等在现实社会中各种复杂网络系统上的传播现象与人们现代社会经济生活息息相关,对复杂网络上各种传播现象的动力学行为研究是复杂网络理论的一个重要命题.从流行病学的角度, 针对复杂网络传播行为中存在的动态过程,从自适应躲避、响应时滞、离散采样、开放式系统增长等方面综述了复杂网络传播动力学研究的若干进展.      

Dynamical model and control of a small-world network with memory

This paper proposes a dynamical model of influence volume of small-world-network with memory to investigate the effects of multiple delays on network dynamics. We calculate the influence volume covered by the spreading quantity, discuss the effect of finite size on the network dynamics, and then give the saturate time. The dynamical control is also investigated by introducing the delayed state feedback to simulate the adaptivity of network. With properly chosen delay and gain in feedback path, the controlled model may have stable equilibrium, periodic solution, quasi-periodic solution, or a complex chaotic attractor from a sequence of period-doubling bifurcations. It shows delayed feedback control may find important applications in the management and dynamical control of complex networks.     

Time Periodic Traveling Waves for a Periodic and Diffusive SIR Epidemic Model

In this paper, we study the time periodic traveling wave solutions for a periodic SIR epidemic model with diffusion and standard incidence. We establish the existence of periodic traveling waves by investigating the fixed points of a nonlinear operator defined on an appropriate set of periodic functions. Then we prove the nonexistence of periodic traveling via the comparison arguments combined with the properties of the spreading speed of an associated subsystem.     

An SIR model with infection delay and propagation vector in complex networks

Based on the SIR (Susceptible-Infected-Removed) model, we propose a novel epidemic model to investigate the impact of infection delay and propagation vector on the spreading behaviors in complex networks. Mean-field approximations and extensive numerical simulations indicate that the infection delay and propagation vector can largely reduce the critical threshold and promote the outbreak of epidemics, and even lead to the case that the infectious diseases transform from the disease-free state to endemic one. The current results are greatly instructive for us to further understand the epidemic spreading and design some effective prevention and containment strategies to fight the epidemics.     

Estimating the state of epidemics spreading with graph neural networks

When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of deep neural networks to solve this challenging task. We base our proposed architecture on Graph Convolutional Neural Networks. As such, it can reason on the effect of the underlying social network structure, which is recognized as the main component in spreading an epidemic. The proposed architecture can reconstruct the entire state with accuracy above 70%, as proven by two scenarios modeled on the CoVid-19 pandemic. The first is a generic homogeneous population, and the second is a toy model of the Boston metropolitan area. Note that no retraining of the architecture is necessary when changing the model.

     

复杂网络上集群行为与自旋模型

复杂系统中的集群行为(collective behavior)是复杂系统涌现性的重要表现之一, 它通过微观个体之间的相互作用, 在宏观上表现出一定的时空或功能有序结构．已有研究表明, 个体之间相互作用的网络结构对复杂系统的集群行为有重要影响．基于复杂网络上的自旋模型, 介绍了复杂网络上的集群行为的部分研究进展． 首先简要介绍了自旋模型的相变现象以及网络结构对相变行为的影响; 其次, 介绍了以自旋模型为基础应用到社会经济等领域中的工作, 包括舆论形成、羊群效应以及分隔现象(segregation)等, 尤其关注网络结构对集群行为的影响, 最后介绍了基于自旋模型的网络社团划分方法．对进一步的发展方向进行了初步的讨论．     

Innovation, imitation, and problem-solving in a networked group

We implemented a problem-solving task in which groups of participants simultaneously played a simple innovation game in a complex problem space, with score feedback provided after each of a number of rounds. Each participant in a group was allowed to view and imitate the guesses of others during the game. The results showed the use of social learning strategies previously studied in other species, and demonstrated benefits of social learning and nonlinear effects of group size on strategy and performance. Rather than simply encouraging conformity, groups provided information to each individual about the distribution of useful innovations in the problem space. Imitation facilitated innovation rather than displacing it, because the former allowed good solutions to be propagated and preserved for further cumulative innovations in the group. Participants generally improved their solutions through the use of fairly conservative strategies, such as changing only a small portion of one's solution at a time, and tending to imitate solutions similar to one's own. Changes in these strategies over time had the effect of making solutions increasingly entrenched, both at individual and group levels. These results showed evidence of nonlinear dynamics in the decentralization of innovation, the emergence of group phenomena from complex interactions of individual efforts, stigmergy in the use of social information, and dynamic tradeoffs between exploration and exploitation of solutions. These results also support the idea that innovation and creativity can be recognized at the group level even when group members are generally cautious and imitative.     

海相沉积软土蠕变BP神经网络本构模型

海相沉积软土具有很强的蠕变特性,传统分级构建蠕变本构模型方法不实用,且很难真正反映岩土流变的非线性特性。为此,本文引入具有超强非线性映射和容错能力的BP神经网络模型,通过改进BP算法,根据江门软土的室内直剪蠕变试验结果,建立了海相沉积软土BP神经网络蠕变本构模型,避免了传统方法为满足试验曲线变化规律和蠕变特性而需要建立复杂的本构数学表达式。最后,利用上海地区软土蠕变实验结果对本文提出的方法进行了验证,并对BP神经网络蠕变模型在描述软土流变方面的特点进行了讨论。结果表明,本文建模方法简单,并能很好地描述软土的非线性蠕变问题。     

Nonlinear dynamics in biopsychosocial resilience

Theory and methodology from nonlinear dynamical systems (NDS) may provide considerable advantage to health scientists as well as health care professionals. For instance, NDS methodologies and topics in health care share a focus upon the potentially complex interactions of biological, psychological and social factors over time. Nevertheless, a number of challenges remain in creating the necessary bridges in understanding to allow researchers to apply NDS techniques and to enable practitioners to use the resulting evidence to improve patient care. This article aims to provide such a bridge. First, common concepts pertaining to self-organizing complex adaptive systems are outlined as a general approach to understanding resilience across biological, psychological, and social scales. Next, four data analytic techniques from NDS are compared and contrasted with respect to the information they may provide about some common processes underlying resilience. These techniques are: time-series analysis, state-space grids, catastrophe modeling, and network modeling. Implications for health scientists and practitioners are discussed.     

Nested-spectral analysis reveals a disruption of behavioral-related dynamic functional balance in the aging brain

The relationship between the age-related reorganization of brain networks and individual behavior has attracted much attention. However, how age induces changes in neural activity at different frequencies in the brain to balance the demands of network integration and segregation, and how age-induced changes in network integration and segregation relate to behavior remain enigmatic. Here, a nested-spectral partition method was used to analyze behavioral-related dynamic functional balance in the aging brain with electroencephalogram signals collected from 56 healthy participants (age: 20–80 years) at rest. The nested-spectral partition approach measures hierarchical segregation and integration across multiple levels by detecting hierarchical modules in brain functional networks. Declines in general personality and general cognitive ability in older adults were captured by exploratory factor analysis. We showed that the brain network of elderly individuals contains more hierarchical modules to generate higher segregation, and it is closer to the functional balance state in the theta and alpha bands but away from this state in the gamma band. Meanwhile, the abnormal variability of functional balance in the elderly brain supports more flexible transitions between segregated and integrated states in the alpha band but reduces the transitions in the beta and gamma bands. Crucially, the degeneration of general personality and general cognitive ability is significantly associated with higher segregation and abnormal flexibility of the brain, especially in the theta, beta, and gamma bands. Our results provide deep insights from a spectral partitioning perspective into the brain dynamic mechanisms that are associated with age-related personality and cognitive degeneration.

     

Global Dynamics and Asymptotic Spreading Speeds for a Partially Degenerate Epidemic Model with Time Delay and Free Boundaries

Journal of Dynamics and Differential Equations - This paper concerns the global dynamics and asymptotic spreading speeds for a partially degenerate epidemic model with time delay and free...     

复杂网络及其新近研究进展简介

简单地介绍了近10年来蓬勃发展的复杂网络研究新领域, 特别是其中最具代表性的随机网络、小世界网络和无尺度网络模型及其基本参数和概念, 并简要地回顾了复杂网络理论在生物和大脑神经网络、流行病传播与免疫控制、交通与社会经济、无线通讯、计算机和互联网、传感器网络、语言学与社会科学以及群体活动和编队飞行问题中的分散控制、稳定性特别是一致性问题等方面的应用趋势.     

Wave turbulence and intermittency in directional wave fields

The evolution of surface gravity waves is driven by nonlinear interactions that trigger an energy cascade similarly to the one observed in hydrodynamic turbulence. This process, known as wave turbulence, has been found to display anomalous scaling with deviation from classical turbulent predictions due to the emergence of coherent and intermittent structures on the water surface. In the ocean, waves are spread over a wide range of directions, with a consequent attenuation of the nonlinear properties. A laboratory experiment in a large wave facility is presented to discuss the sensitivity of wave turbulence on the directional properties of model wave spectra. Results show that the occurrence of coherent and intermittent structures become less likely with the broadening of the wave directional spreading. There is no evidence, however, that intermittency completely vanishes.     

Electrodermal arousal between participants in a conversation: nonlinear dynamics and linkage effects

Physiological linkages between two people in conversation were investigated from a nonlinear dynamical systems theory perspective. Thirty-seven pairs of college students engaged in a 20-minute discussion while connected to electrodes that measured their electrodermal response (ED). Greater incidents of linkage were detected through the nonlinear regression analysis compared to linear models. The nonlinear models were exponential structures that determined positive Lyapunov exponents (and thus the presence of chaos) for 73 out of 74 participants. Results support the use of nonlinear models for interpersonal linkage effects of this type. For each participant, Social Sensitivity from the Social Skills Inventory was correlated with the nonlinear model R(2) coefficient, but not with the size of the Lyapunov exponent. The latter results supported the conclusion that empathy acted as a moderator of the nonlinear process that underlies changes in ED levels over time.     

Dynamics of a novel nonlinear SIR model with double epidemic hypothesis and impulsive effects

In this paper, the propagation of a nonlinear delay SIR epidemic using the double epidemic hypothesis is modeled. In the model, a system of impulsive functional differential equations is studied and the sufficient conditions for the global attractivity of the semi-trivial periodic solution are drawn. By use of new computational techniques for impulsive differential equations with delay, we prove that the system is permanent under appropriate conditions. The results show that time delay, pulse vaccination, and nonlinear incidence have significant effects on the dynamics behaviors of the model. The conditions for the control of the infection caused by viruses A and B are given.     

Social-support moderated stress: a nonlinear dynamical model and the stress-buffering hypothesis

Health psychology has studied the cross-sectional, stationary relationships linking stress, social support, and health. Levels of stress-related illness are generally modeled by including a nonlinear multiplicative or 'buffering' effect, corresponding to the interaction of stressor levels with social support from family and friends. The motivation of the present research is to extend an iterative, dynamic model of this well-investigated psychological process using a dynamical systems model expressed as a set of continuous, nonlinear differential equations similar to those of the 'Oregonator,' a model of a nonlinear dynamic chemical system. This model of the behavior of an individual is amenable to numerical investigation of its stationary-state stability properties, temporal evolution, and cause-effect relationships. The continuous variables in this new approach refer to varying states of an individual; they are Perceived stress (X), Symptoms (Y), and Social support (Z). It is expected that poor health in this model, represented by Symptoms (Y), is directly related to Perceived stress, as well as being tied in more complicated ways to Social support. A number of such models may be envisioned, some including a multiplicative, 'buffering' (- X x Z) effect of social support dependent on stress levels. We explore the behavior of this model over ranges of parameter values and initial conditions and relate these results to how an individual reacts to environmental challenges at various levels of stressors and social-support recruitment. Data generated by the model are in turn analyzed with a traditional cross-sectional statistical technique. Similarities and differences between chemical and psychological systems are discussed.     

Dynamics of a multiplex neural network with delayed couplings

Multiplex networks have drawn much attention since they have been observed in many systems, e.g., brain, transport, and social relationships. In this paper, the nonlinear dynamics of a multiplex network with three neural groups and delayed interactions is studied. The stability and bifurcation of the network equilibrium are discussed, and interesting neural activities of the network are explored. Based on the neuron circuit,transfer function circuit, and time delay circuit, a circuit platform of...     

基于混沌神经网络理论的城市地面沉降量预测模型

通过分析城市地面沉降量时间序列的非线性动力学系统,认为该时间序列具有混沌特性。在此基础上,通过相空间重构的方法建立了用于城市地面沉降量预测的混沌神经网络模型;并利用此模型对高桥地面沉降量进行了预测,并和实际监测沉降量进行了比较,最大绝对预测误差为1. 7,预测的平均误差为0. 0833,研究结果表明,应用混沌神经网络模型进行城市沉降预测是可行、精确的。