Epidemic spreading in communities with mobile agents

2009 ◽  
Vol 388 (7) ◽  
pp. 1228-1236 ◽  
Author(s):  
Jie Zhou ◽  
Zonghua Liu
2012 ◽  
Vol 98 (6) ◽  
pp. 68003 ◽  
Author(s):  
Han-Xin Yang ◽  
Wen-Xu Wang ◽  
Ying-Cheng Lai ◽  
Bing-Hong Wang

2020 ◽  
Vol 51 (9) ◽  
pp. 1853
Author(s):  
Md Arquam ◽  
A. Singh

2010 ◽  
Vol 20 (03) ◽  
pp. 765-773 ◽  
Author(s):  
ARTURO BUSCARINO ◽  
AGNESE DI STEFANO ◽  
LUIGI FORTUNA ◽  
MATTIA FRASCA ◽  
VITO LATORA

The study of social networks, and in particular those aspects related to disease spreading, has recently attracted considerable attention in the scientific community. In this paper, we investigate the effect of motion on the spread of diseases in dynamical networks of mobile agents. In order to simulate the long distance displacements empirically observed in real human movements, we consider different motion rules, such as random walks with the addition of jumps or Lévy flights. We compare the epidemic thresholds found in dynamical networks of mobile agents with the analogous expressions for static networks. We discuss the existing relations between dynamical networks of random walkers with jumps and static small-world networks, and those between systems of Lévy walkers and scale-free networks.


2014 ◽  
Vol 19 (5) ◽  
pp. 1301-1312 ◽  
Author(s):  
Xiao-Pu Han ◽  
Zhi-Dan Zhao ◽  
Tarik Hadzibeganovic ◽  
Bing-Hong Wang

2017 ◽  
Vol 86 (11) ◽  
pp. 113001 ◽  
Author(s):  
Takashi Nagatani ◽  
Genki Ichinose ◽  
Kei-ichi Tainaka

2017 ◽  
Vol 2 (1) ◽  
pp. 27-32
Author(s):  
Botchkaryov. A. ◽  

The way of functional coordination of methods of organization adaptive data collection processes and methods of spatial self-organization of mobile agents by parallel execution of the corresponding data collection processes and the process of motion control of a mobile agent using the proposed protocol of their interaction and the algorithm of parallel execution planning is proposed. The method allows to speed up the calculations in the decision block of the mobile agent by an average of 40.6%. Key words: functional coordination, adaptive data collection process, spatial self-organization, mobile agents


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