Journal
CHAOS
Volume 28, Issue 5, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5030908
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Funding
- National Natural Science Foundation of China [61703281, 11547040]
- Science and Technology Innovation Commission of Shenzhen [JCYJ 20160520162743717, SGLH 2013 1010163759789]
- Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province, China [2017 A030310374]
- Young Teachers Start-up Fund of Natural Science Foundation of Shenzhen Science and Technology Foundation [JCYJ20150529164656096]
- Tencent Open Research Fund
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Devising effective strategies for hindering the propagation of viruses and protecting the population against epidemics is critical for public security and health. Despite a number of studies based on the susceptible-infected-susceptible (SIS) model devoted to this topic, we still lack a general framework to compare different immunization strategies in completely random networks. Here, we address this problem by suggesting a novel method based on heterogeneous mean-field theory for the SIS model. Our method builds the relationship between the thresholds and different immunization strategies in completely random networks. Besides, we provide an analytical argument that the targeted large-degree strategy achieves the best performance in random networks with arbitrary degree distribution. Moreover, the experimental results demonstrate the effectiveness of the proposed method in both artificial and real-world networks. Published by AIP Publishing.
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