2012/03/31 by Chuansheng Shen, Hanshuang Chen, Zhonghuai Hou
Computer Science · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Mental Health Research Topics #Opinion Dynamics and Social Influence #cond-mat.stat-mech #cs.SI #physics.soc-ph
paper · pdf · doi:10.1103/physreve.86.036114
published as Physical Review E 86, 036114 (2012) · 14 pages, 6 figures
arxiv created 2012/08/02 · openalex publication_date 2012/09/24 · arxiv updated 2013/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose an efficient strategy to suppress epidemic explosion in heterogeneous metapopulation networks, wherein each node represents a subpopulation with any number of individuals and is assigned a curing rate that is proportional to kα with the node degree k and an adjustable parameter α. We perform stochastic simulations of the dynamical reaction-diffusion processes associated with the susceptible-infected-susceptible model in scale-free networks. We find that the epidemic threshold reaches a maximum when α is tuned at αopt≃1.3. This nontrivial phenomenon is robust to the change of the network size and the average degree. In addition, we carry out a mean field analysis to further validate our scheme, which also demonstrates that epidemic explosion follows different routes for α larger or less than αopt. Our work suggests that in order to efficiently suppress epidemic spreading on heterogeneous complex networks, subpopulations with higher degrees should be allocated more resources than just being linearly dependent on k.