scholarly journals Modelling approaches for simple dynamic networks and applications to disease transmission models

2012 ◽  
Vol 468 (2141) ◽  
pp. 1332-1355 ◽  
Author(s):  
Istvan Z. Kiss ◽  
Luc Berthouze ◽  
Timothy J. Taylor ◽  
Péter L. Simon
Keyword(s):  
Steady State ◽  
Network Model ◽  
Disease Transmission ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Link Type ◽  
Dynamic Network Model ◽  
Pairwise Model ◽  
Closure Relations ◽  
Transmission Models

In this paper a random link activation–deletion (RLAD) model is proposed that gives rise to a stochastically evolving network. This dynamic network is then coupled to a simple susceptible-infectious-suceptible ( SIS ) dynamics on the network, and the resulting spectrum of model behaviour is explored via simulation and a novel pairwise model for dynamic networks. First, the dynamic network model is systematically analysed by considering link-type independent and dependent network dynamics coupled with globally constrained link creation. This is done rigorously with some analytical results and we highlight where such analysis can be performed and how these simpler models provide a benchmark to test and validate full simulations. The pairwise model is used to study the interplay between SIS -type dynamics on the network and link-type-dependent activation–deletion. Assumptions of the pairwise model are identified and their implications interpreted in a way that complements our current understanding. Furthermore, we also discuss how the strong assumptions of the closure relations can lead to disagreement between the simulation and pairwise model. Unlike on a static network, the resulting spectrum of behaviour is more complex with the prevalence of infections exhibiting not only a single steady state, but also bistability and oscillations.

scholarly journals Infection Percolation: A Dynamic Network Model of Disease Spreading

2021 ◽  
Vol 9 ◽  
Author(s):  
Christopher A. Browne ◽  
Daniel B. Amchin ◽  
Joanna Schneider ◽  
Sujit S. Datta
Keyword(s):  
Network Model ◽  
Traveling Wave ◽  
Disease Transmission ◽  
Dynamic Network ◽  
Susceptibility To Infection ◽  
Dynamic Network Model ◽  
Disease Spreading ◽  
Models Of Disease ◽  
Individual Scale ◽  
The Individual

Models of disease spreading are critical for predicting infection growth in a population and evaluating public health policies. However, standard models typically represent the dynamics of disease transmission between individuals using macroscopic parameters that do not accurately represent person-to-person variability. To address this issue, we present a dynamic network model that provides a straightforward way to incorporate both disease transmission dynamics at the individual scale as well as the full spatiotemporal history of infection at the population scale. We find that disease spreads through a social network as a traveling wave of infection, followed by a traveling wave of recovery, with the onset and dynamics of spreading determined by the interplay between disease transmission and recovery. We use these insights to develop a scaling theory that predicts the dynamics of infection for diverse diseases and populations. Furthermore, we show how spatial heterogeneities in susceptibility to infection can either exacerbate or quell the spread of disease, depending on its infectivity. Ultimately, our dynamic network approach provides a simple way to model disease spreading that unifies previous findings and can be generalized to diverse diseases, containment strategies, seasonal conditions, and community structures.

Dynamic Network Model Analysis Based on Communication Network

2014 ◽  
Vol 989-994 ◽  
pp. 2639-2642
Author(s):  
Nan Qi Yuan ◽  
Tian Jiang ◽  
Shi Bai ◽  
Hao Sun ◽  
Jing Mei Zhao
Keyword(s):  
Communication Network ◽  
Convergence Rate ◽  
Network Model ◽  
Dynamic Network ◽  
Model Analysis ◽  
Vicsek Model ◽  
Dynamic Network Model ◽  
Weighted Model

In order to research dynamic network astringency reaching uniformity, this paper perfects the Vicsek model and puts forward improving dynamic network astringency efficiency by weighted model. We prove that the convergence rate of weighted model is faster than the classic Vicsek model and it can optimize dynamic network.

scholarly journals SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao
Keyword(s):  
Network Model ◽  
Purchase Intention ◽  
Recommendation System ◽  
Optimal Algorithm ◽  
Dynamic Network ◽  
Distribution Model ◽  
Recommendation Algorithm ◽  
Model Based ◽  
Dynamic Network Model ◽  
Recommendation Algorithms

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

scholarly journals Oscillating epidemics in a dynamic network model: stochastic and mean-field analysis

2015 ◽  
Vol 72 (5) ◽  
pp. 1153-1176 ◽  
Author(s):  
András Szabó-Solticzky ◽  
Luc Berthouze ◽  
Istvan Z. Kiss ◽  
Péter L. Simon
Keyword(s):  
Network Model ◽  
Mean Field ◽  
Dynamic Network ◽  
Field Analysis ◽  
Dynamic Network Model

scholarly journals A Dynamic Network Model to Explain the Development of Excellent Human Performance

2016 ◽  
Vol 7 ◽  
Author(s):  
Ruud J. R. Den Hartigh ◽  
Marijn W. G. Van Dijk ◽  
Henderien W. Steenbeek ◽  
Paul L. C. Van Geert
Keyword(s):  
Network Model ◽  
Human Performance ◽  
Dynamic Network ◽  
Dynamic Network Model
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