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Modelling of Vaccination Strategies for Epidemics using Evolutionary Computation

机译:基于进化计算的流行病疫苗接种策略建模

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Personal contact networks that represent social interactions can be used to identify who can infect whom during the spread of an epidemic. The structure of a personal contact network has great impact upon both epidemic duration and the total number of infected individuals. A vaccine, with varying degrees of success, can reduce both the length and spread of an epidemic, but in the case of a limited supply of vaccine a vaccination strategy must be chosen, and this has a significant effect on epidemic behaviour.In this study we consider four different vaccination strategies and compare their effects upon epidemic duration and spread. These are random vaccination, high degree vaccination, ring vaccination, and the base case of no vaccination. All vaccinations are applied as the epidemic progresses, as opposed to in advance. The strategies are initially applied to static personal contact networks that are known ahead of time. They are then applied to personal contact networks that are evolved as the vaccination strategy is applied. When any form of vaccination is applied, all strategies reduce both duration and spread of the epidemic. When applied to a static network, random vaccination performs poorly in terms of reducing epidemic duration in comparison to strategies that take into account connectivity of the network. However, it performs surprisingly well when applied on the evolved networks, possibly because the evolutionary algorithm is unable to take advantage of a fixed strategy.
机译:代表社交互动的个人联系网络可用于识别谁可以在流行病的传播中感染谁。个人联系网络的结构对疫情持续时间和感染者的总数产生了很大的影响。一种疫苗,具有不同程度的成功,可以减少流行病的长度和扩散,但在有限的疫苗供应有限的情况下,必须选择疫苗接种策略,这对疫情的行为具有显着影响。本研究我们考虑四种不同的疫苗接种策略,并比较它们对流行持续时间和传播的影响。这些是随机疫苗接种,高度接种疫苗接种,环疫苗接种和无疫苗接种的基础情况。所有接种疫苗都适用于流行病进展,而不是提前。策略最初应用于提前已知的静态个人联系网络。然后将它们应用于在应用疫苗接种策略时进化的个人联系网络。当应用任何形式的疫苗接种时,所有策略都会减少流行病的持续时间和传播。当应用于静态网络时,随机疫苗接种在降低潮流期的情况下,与考虑网络连接的策略相比,减少了疫情持续时间。然而,当应用于进化的网络时,它可能会令人惊讶地表现得很好,可能是因为进化算法无法利用固定策略。

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