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Effect of Vaccination Strategies on the Herd Immunity of Growing Networks

机译:疫苗接种策略对生长网络牧群免疫的影响

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It is well known that non-vaccinated individuals may be protected from contacting a disease by vaccinated individuals in a social network through community protection (herd immunity). Such protection greatly depends on the underlying topology of the social network, and the strategy used in selecting individuals for vaccination. Social networks however undergo constant growth, and it may be argued that network growth may change the level of herd immunity present in social networks. In this paper, we analyse the effect of growth and immunization strategies on herd immunity of social networks. Considering three classical topologies - Random, scale-free and small-world, we compare the influence of immunization strategies on each of them and then discuss how network growth can nullify or amplify these differences. We show that betweenness based vaccination is best strategy of immunization, regardless of topology, in static networks, but its prominence over other strategies diminishes in dynamically growing topologies. We demonstrate that herd immunity of random networks actually increases with growth, if the proportion of survivors to a secondary infection is considered, while the community protection in scale-free and small world networks decreases with growth. We compare the relative influence of growth on each class of networks vaccinated under different strategies.
机译:众所周知,可以通过社区保护(群体免疫)在社交网络中保护未接种疫苗的个体免于接触疾病。这种保护在很大程度上取决于社交网络的基础拓扑以及在选择个人进行疫苗接种时所使用的策略。然而,社交网络不断增长,可能会争辩说,网络的增长可能会改变社交网络中存在的群体免疫的水平。在本文中,我们分析了生长和免疫策略对社交网络群免疫的影响。考虑三种经典拓扑-随机,无标度和小世界,我们比较免疫策略对每种拓扑的影响,然后讨论网络增长如何消除或扩大这些差异。我们显示,基于中间性的疫苗接种是最佳的免疫策略,无论拓扑结构如何,在静态网络中,但其相对于其他策略的重要性在动态增长的拓扑结构中逐渐消失。我们证明,如果考虑到继发感染的幸存者比例,则随机网络的畜群免疫实际上会随着增长而增加,而无规模和小型世界网络中的社区保护会随着增长而降低。我们比较了增长对在不同策略下接种的每类网络的相对影响。

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