scholarly journals Epidemic dynamics on complex networks with general infection rate and immune strategies

2018 ◽  
Vol 23 (6) ◽  
pp. 2071-2090
Author(s):  
Shouying Huang ◽  
◽  
Jifa Jiang ◽  
Keyword(s):  
Complex Networks ◽  
Infection Rate ◽  
Epidemic Dynamics

scholarly journals Effects of the Awareness-Driven Individual Resource Allocation on the Epidemic Dynamics

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiaolong Chen ◽  
Ruijie Wang ◽  
Dan Yang ◽  
Jiajun Xian ◽  
Qing Li
Keyword(s):  
Resource Allocation ◽  
Complex Networks ◽  
Infection Rate ◽  
The Self ◽  
Phase Iii ◽  
Self Awareness ◽  
Allocation Model ◽  
Epidemic Dynamics ◽  
Disease Awareness ◽  
The Impact

We investigate the effects of self-protection awareness on the spread of disease from the aspect of resource allocation behavior in populations. To this end, a resource-based epidemiological model and a self-awareness-based resource allocation model in complex networks are proposed, respectively. First of all, we study the coupled disease-awareness dynamics in complex networks with fixed degree heterogeneity. Through extensive Monte Carlo simulations, we find that overall the self-awareness inhibits the spread of disease. More importantly, the influence of the self-awareness on the spreading dynamics can be divided into three phases. In phase I, the self-awareness is relatively small and the outbreak of the epidemic can not be suppressed effectively. While, in phase II, the epidemic size is significantly reduced. Finally, in phase III, there is a sufficiently large value of self-awareness, the disease cannot outbreak anymore. Further, we study the impact of degree heterogeneity on the coupled disease-awareness dynamics and find that the network heterogeneity plays the role of “double-edged sword” in that it can either suppress or promote the epidemic spreading. Specifically, when the basic infection rate is relatively small, it promotes the spread of disease under the condition that there is a relatively small self-awareness. While, when the basic infection rate is relatively large, it inhibits the outbreak of epidemic at a relatively small self-awareness; in turn, it promotes the outbreak of epidemic at a relatively large self-awareness.

scholarly journals Coevolving Dynamics between Epidemic and Information Spreading considering the Dependence between Vigilance and Awareness Prevalence

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yixiao Zhang ◽  
Xing Lu ◽  
Ni Cui ◽  
Jingtai Tang ◽  
Xiyun Zhang
Keyword(s):  
Infection Rate ◽  
Nonlinear Dependence ◽  
Rational Behavior ◽  
Time Varying ◽  
Protective Measures ◽  
Positive Correlation ◽  
Epidemic Dynamics ◽  
Disease Information ◽  
Information Spreading

It has been demonstrated that the propagation of information and awareness regarding a disease can assist in containing the outbreak of epidemics. Previous models for this coevolving usually introduced the dependence between these two processes by setting a lower but time-independent infection rate for individuals with awareness. However, a realistic scenario can be more complicated, as individual vigilance and the adopted protective measures may depend on the extent of the discussion on the disease, whereas individuals may be irrational or lack relevant knowledge, leading to improper measures being taken. These can introduce a time-varying dependence between epidemic dynamics and awareness prevalence and may weaken the effect of spreading awareness in containing a pandemic. To better understand this effect, we introduce a nonlinear dependence of the epidemic infection rate on awareness prevalence, focusing on the effect of different forms of dependence on the coevolving dynamics. We demonstrate that a positive correlation between vigilance and awareness prevalence can enhance the effect of information spreading in suppressing epidemics. However, this enhancement can be weakened if some individuals are irrational. Our results demonstrate the importance of rational behavior in the strategy of containing epidemics by propagation of disease information.

COMPLEX NETWORKS: TOPOLOGY, DYNAMICS AND SYNCHRONIZATION

2002 ◽  
Vol 12 (05) ◽  
pp. 885-916 ◽  
Author(s):  
XIAO FAN WANG
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
Small World ◽  
Dynamical Networks ◽  
Scale Free ◽  
Epidemic Dynamics ◽  
The Past ◽  
The Relationship

Dramatic advances in the field of complex networks have been witnessed in the past few years. This paper reviews some important results in this direction of rapidly evolving research, with emphasis on the relationship between the dynamics and the topology of complex networks. Basic quantities and typical examples of various complex networks are described; and main network models are introduced, including regular, random, small-world and scale-free models. The robustness of connectivity and the epidemic dynamics in complex networks are also evaluated. To that end, synchronization in various dynamical networks are discussed according to their regular, small-world and scale-free connections.

Epidemic dynamics on semi-directed complex networks

2013 ◽  
Vol 246 (2) ◽  
pp. 242-251 ◽  
Author(s):  
Xiaoguang Zhang ◽  
Gui-Quan Sun ◽  
Yu-Xiao Zhu ◽  
Junling Ma ◽  
Zhen Jin
Keyword(s):  
Complex Networks ◽  
Epidemic Dynamics

scholarly journals Epidemic dynamics of influenza-like diseases spreading in complex networks

2020 ◽  
Vol 101 (3) ◽  
pp. 1801-1820
Author(s):  
Yi Wang ◽  
Zhouchao Wei ◽  
Jinde Cao
Keyword(s):  
Complex Networks ◽  
Epidemic Dynamics

scholarly journals Collective synchronization induced by epidemic dynamics on complex networks with communities

2007 ◽  
Vol 75 (1) ◽  
Author(s):  
Gang Yan ◽  
Zhong-Qian Fu ◽  
Jie Ren ◽  
Wen-Xu Wang
Keyword(s):  
Complex Networks ◽  
Epidemic Dynamics

scholarly journals Dynamics-Preserving Graph Embedding for Community Mining and Network Immunization

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 250
Author(s):  
Jianan Zhong ◽  
Hongjun Qiu ◽  
Benyun Shi
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Graph Embedding ◽  
Opinion Formation ◽  
Embedding Method ◽  
Influence Matrix ◽  
Singular Value Decomposition Method ◽  
Epidemic Dynamics ◽  
Community Mining ◽  
Node Embeddings

In recent years, the graph embedding approach has drawn a lot of attention in the field of network representation and analytics, the purpose of which is to automatically encode network elements into a low-dimensional vector space by preserving certain structural properties. On this basis, downstream machine learning methods can be implemented to solve static network analytic tasks, for example, node clustering based on community-preserving embeddings. However, by focusing only on structural properties, it would be difficult to characterize and manipulate various dynamics operating on the network. In the field of complex networks, epidemic spreading is one of the most typical dynamics in networks, while network immunization is one of the effective methods to suppress the epidemics. Accordingly, in this paper, we present a dynamics-preserving graph embedding method (EpiEm) to preserve the property of epidemic dynamics on networks, i.e., the infectiousness and vulnerability of network nodes. Specifically, we first generate a set of propagation sequences through simulating the Susceptible-Infectious process on a network. Then, we learn node embeddings from an influence matrix using a singular value decomposition method. Finally, we show that the node embeddings can be used to solve epidemics-related community mining and network immunization problems. The experimental results in real-world networks show that the proposed embedding method outperforms several benchmark methods with respect to both community mining and network immunization. The proposed method offers new insights into the exploration of other collective dynamics in complex networks using the graph embedding approach, such as opinion formation in social networks.

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