Controlling Epidemic Outbreaks and Public Sentiment Spreading by Vaccination in Complex Network
LIU Ying1,2, WANG Wei1, SHANG Mingsheng1, TANG Ming1
1. Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, China; 2. School of Computer Science, Southwest Petroleum University, Chengdu 610500, China
Abstract：In this paper, an overview of vaccination methods addressing in suppressing the epidemic spreading is given, focusing on modeling the epidemic and public sentiment spreading from real world scenarios, describing models of dynamic spreading, and presenting vaccination strategies and their efficiency. Simulation results on empirical networks and model networks using different vaccination strategies show that vaccination strategies such as centrality-based vaccination, graph partition-based vaccination and acquaintance vaccination are more effective than random vaccination. This implies that vaccination strategy is important and meaningful in suppressing epidemic spreading. In order to reach a better control result, the topological structure and the completeness of network information should be taken into account when choosing a vaccination strategy.
刘影, 王伟, 尚明生, 唐明. 复杂网络上疫情与舆情的传播及其基于免疫的控制策略[J]. 复杂系统与复杂性科学, 2016, 13(1): 74-83.
LIU Ying, WANG Wei, SHANG Mingsheng, TANG Ming. Controlling Epidemic Outbreaks and Public Sentiment Spreading by Vaccination in Complex Network[J]. Complex Systems and Complexity Science, 2016, 13(1): 74-83.
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