scholarly journals Epidemic spreading in time-varying community networks

2014 ◽  
Vol 24 (2) ◽  
pp. 023116 ◽  
Author(s):  
Guangming Ren ◽  
Xingyuan Wang
Keyword(s):  
Community Networks ◽  
Time Varying ◽  
Epidemic Spreading

scholarly journals Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks

2021 ◽  
Vol 105 (4) ◽  
pp. 3819-3833
Author(s):  
Haili Guo ◽  
Qian Yin ◽  
Chengyi Xia ◽  
Matthias Dehmer
Keyword(s):  
Information Diffusion ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Time Varying Networks

scholarly journals Modeling and analysis of epidemic spreading on community networks with heterogeneity

2018 ◽  
Vol 119 ◽  
pp. 136-145 ◽  
Author(s):  
Chanchan Li ◽  
Guo-ping Jiang ◽  
Yurong Song ◽  
Lingling Xia ◽  
Yinwei Li ◽  
...  
Keyword(s):  
Community Networks ◽  
Epidemic Spreading ◽  
Modeling And Analysis

scholarly journals Suppression of epidemic spreading in time-varying multiplex networks

2019 ◽  
Vol 75 ◽  
pp. 806-818 ◽  
Author(s):  
Hui Yang ◽  
Changgui Gu ◽  
Ming Tang ◽  
Shi-Min Cai ◽  
Ying-Cheng Lai
Keyword(s):  
Time Varying ◽  
Epidemic Spreading ◽  
Multiplex Networks

Epidemic spreading in community networks

2005 ◽  
Vol 72 (2) ◽  
pp. 315-321 ◽  
Author(s):  
Zonghua Liu ◽  
Bambi Hu
Keyword(s):  
Community Networks ◽  
Epidemic Spreading

Epidemic spreading phenomena on a scale-free network with time-varying transmission rate due to social responses

2020 ◽  
Vol 31 (10) ◽  
pp. 2050148
Author(s):  
Samira Maghool ◽  
Nahid Maleki-Jirsaraei
Keyword(s):  
Behavioral Response ◽  
Transfer Rate ◽  
Social Cost ◽  
Transmission Rate ◽  
Social Awareness ◽  
Initial Time ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Social Responses ◽  
Special Case

In this paper, we study and simulate the effect of individual social responses, as a collective factor, on the epidemic spreading processes. We formally define the problem based on the traditional [Formula: see text] and [Formula: see text] compartmental models considering the time-varying infection probability dependent on the social responses. In this study, models of generic and special case scenarios are developed. While in the generic case the effective parameter of behavioral response is demonstrated as one collective factor, in the special case the behavioral response is assumed as the combination of two collective factors: social cost and transfer rate of social awareness. With social cost, we refer to the costs incurred by a certain population to prevent or mitigate an epidemic. With transfer rate of social awareness, we describe the averaged rate of received information and knowledge regarding a disease that individuals hold and make use to avoid negative consequences. We show that, while in both [Formula: see text] and [Formula: see text] models the density of infected agents grows exponentially during the initial time steps, the inclusion of our models of social responses, either generic or special one, leads to mitigation of the spreading. As a result of both generic and special cases, the density of infected agents in the stationary state and the maximum number of infected agents decrease according to power-law functions for different values of collective factors. In the special case results, we also witnessed significant changes in the slope of decreasing trends of stationary density of states happening for a critical value of transfer rate of social awareness, approximately at about the inverse of the time interval of transmission rate update. With this result, we point out that increasing the transfer rate of social awareness to about this critical point outperforms any slight increase in social cost in reducing the number of infected agents.

An analytical framework for the study of epidemic models on activity driven networks

2017 ◽  
Vol 5 (6) ◽  
pp. 924-952 ◽  
Author(s):  
Lorenzo Zino ◽  
Alessandro Rizzo ◽  
Maurizio Porfiri
Keyword(s):  
Jump Process ◽  
Analytical Framework ◽  
Epidemic Models ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Markov Jump ◽  
Real World Data ◽  
Time Model ◽  
Formal Framework ◽  
Over Time

Abstract Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies. However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to capture several key features of a number of infectious diseases. Activity driven networks (ADNs) constitute a promising modelling framework to describe epidemic spreading over time varying networks, but a number of technical and theoretical gaps remain open. Here, we lay the foundations for a novel theory to model general epidemic spreading processes over time-varying, ADNs. Our theory derives a continuous-time model, based on ordinary differential equations (ODEs), which can reproduce the dynamics of any discrete-time epidemic model evolving over an ADN. A rigorous, formal framework is developed, so that a general epidemic process can be systematically mapped, at first, on a Markov jump process, and then, in the thermodynamic limit, on a system of ODEs. The obtained ODEs can be integrated to simulate the system dynamics, instead of using computationally intensive Monte Carlo simulations. An array of mathematical tools for the analysis of the proposed model is offered, together with techniques to approximate and predict the dynamics of the epidemic spreading, from its inception to the endemic equilibrium. The theoretical framework is illustrated step-by-step through the analysis of a susceptible–infected–susceptible process. Once the framework is established, applications to more complex epidemic models are presented, along with numerical results that corroborate the validity of our approach. Our framework is expected to find application in the study of a number of critical phenomena, including behavioural changes due to the infection, unconscious spread of the disease by exposed individuals, or the removal of nodes from the network of contacts.

scholarly journals Social contagions on time-varying community networks

2017 ◽  
Vol 95 (5) ◽  
Author(s):  
Mian-Xin Liu ◽  
Wei Wang ◽  
Ying Liu ◽  
Ming Tang ◽  
Shi-Min Cai ◽  
...  
Keyword(s):  
Community Networks ◽  
Time Varying
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