Epidemic spreading dynamics on complex networks with adaptive social-support

2019 ◽  
Vol 525 ◽  
pp. 778-787 ◽  
Author(s):  
Rong Zhou ◽  
Qingchu Wu
Keyword(s):  
Social Support ◽  
Complex Networks ◽  
Epidemic Spreading ◽  
Spreading Dynamics

scholarly journals Phase diagrams of interacting spreading dynamics in complex networks

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Liming Pan ◽  
Dan Yang ◽  
Wei Wang ◽  
Shimin Cai ◽  
Tao Zhou ◽  
...  
Keyword(s):  
Complex Networks ◽  
Phase Diagrams ◽  
Spreading Dynamics

scholarly journals Synergistic Effects in Networked Epidemic Spreading Dynamics

2020 ◽  
Vol 67 (3) ◽  
pp. 496-500 ◽  
Author(s):  
Masaki Ogura ◽  
Wenjie Mei ◽  
Kenji Sugimoto
Keyword(s):  
Synergistic Effects ◽  
Epidemic Spreading ◽  
Spreading Dynamics

Epidemic spreading dynamics with drug resistance and heterogeneous contacts

2018 ◽  
Vol 441 ◽  
pp. 19-27 ◽  
Author(s):  
Peng-Bi Cui ◽  
Lei Gao ◽  
Wei Wang
Keyword(s):  
Drug Resistance ◽  
Epidemic Spreading ◽  
Spreading Dynamics

scholarly journals Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Mi Feng ◽  
Shi-Min Cai ◽  
Ming Tang ◽  
Ying-Cheng Lai
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics

scholarly journals Control of epidemic spreading on complex networks by local traffic dynamics

2011 ◽  
Vol 84 (4) ◽  
Author(s):  
Han-Xin Yang ◽  
Wen-Xu Wang ◽  
Ying-Cheng Lai ◽  
Yan-Bo Xie ◽  
Bing-Hong Wang
Keyword(s):  
Complex Networks ◽  
Epidemic Spreading ◽  
Traffic Dynamics

Selectivity-based spreading dynamics on complex networks

2008 ◽  
Vol 78 (2) ◽  
Author(s):  
Rui Yang ◽  
Liang Huang ◽  
Ying-Cheng Lai
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics

scholarly journals Detecting hidden layers from spreading dynamics on complex networks

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
Łukasz G. Gajewski ◽  
Jan Chołoniewski ◽  
Mateusz Wilinski
Keyword(s):  
Complex Networks ◽  
Spreading Dynamics

scholarly journals Importance of Interaction Structure and Stochasticity for Epidemic Spreading: A COVID-19 Case Study

2020 ◽  
Author(s):  
Gerrit Großmann ◽  
Michael Backenköhler ◽  
Verena Wolf
Keyword(s):  
Contact Network ◽  
Epidemic Spreading ◽  
Virus Propagation ◽  
Deterministic Models ◽  
Interaction Structure ◽  
Ode Models ◽  
Spreading Dynamics ◽  
Multi Agent ◽  
Political Decision Making

AbstractIn the recent COVID-19 pandemic, computer simulations are used to predict the evolution of the virus propagation and to evaluate the prospective effectiveness of non-pharmaceutical interventions. As such, the corresponding mathematical models and their simulations are central tools to guide political decision-making. Typically, ODE-based models are considered, in which fractions of infected and healthy individuals change deterministically and continuously over time.In this work, we translate an ODE-based COVID-19 spreading model from literature to a stochastic multi-agent system and use a contact network to mimic complex interaction structures. We observe a large dependency of the epidemic’s dynamics on the structure of the underlying contact graph, which is not adequately captured by existing ODE-models. For instance, existence of super-spreaders leads to a higher infection peak but a lower death toll compared to interaction structures without super-spreaders. Overall, we observe that the interaction structure has a crucial impact on the spreading dynamics, which exceeds the effects of other parameters such as the basic reproduction number R0. We conclude that deterministic models fitted to COVID-19 outbreak data have limited predictive power or may even lead to wrong conclusions while stochastic models taking interaction structure into account offer different and probably more realistic epidemiological insights.

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