Effect of Vaccination Strategies on the Herd Immunity of Growing Networks

Piraveenan Mahendra, M. S. Uddin, Gnana Thedchanamoorthy
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Abstract

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.
疫苗接种策略对生长网络群体免疫的影响
众所周知,通过社区保护(群体免疫),社会网络中接种疫苗的个人可以保护未接种疫苗的个人不接触某种疾病。这种保护很大程度上取决于社会网络的底层拓扑结构,以及选择接种疫苗的个体所采用的策略。然而,社会网络经历了不断的增长,可能有人认为,网络增长可能会改变社会网络中存在的群体免疫水平。本文分析了生长策略和免疫策略对社会网络群体免疫力的影响。考虑到三种经典拓扑-随机,无标度和小世界,我们比较了免疫策略对它们的影响,然后讨论了网络增长如何抵消或放大这些差异。我们表明,在静态网络中,基于间隔的疫苗接种是最佳的免疫策略,无论拓扑如何,但在动态增长的拓扑中,它比其他策略的突出性减弱。我们证明,如果考虑到二次感染的幸存者比例,随机网络的群体免疫力实际上随着增长而增加,而无标度和小世界网络的社区保护随着增长而降低。我们比较了成长对不同策略下接种疫苗的每一类网络的相对影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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