Vaccination has become one of the most prominent measures for preventing the spread of infectious diseases in modern times. However, mass vaccination of the population may not always be possible due to high costs, severe side effects, or shortage. Therefore, identifying individuals with a high potential of spreading the disease and targeted vaccination of these individuals is of high importance. While various strategies for identifying such individuals have been proposed in the network epidemiology literature, the vast majority of them rely solely on the network topology. In contrast, in this paper, we propose a novel targeted vaccination strategy that considers both the static network topology and the dynamic states of the network nodes over time. This allows our strategy to find the individuals with the highest potential to spread the disease at any given point in time. Extensive evaluation that we conducted over various real-world network topologies, network sizes, vaccination budgets, and parameters of the contagion model, demonstrates that the proposed strategy considerably outperforms existing state-of-the-art targeted vaccination strategies in reducing the spread of the disease. In particular, the proposed vaccination strategy further reduces the number of infected nodes by 23–99%, compared to a vaccination strategy based on Betweenness Centrality.

接种疫苗已成为防止现代传染病传播的最重要措施之一。但是，由于成本高昂，副作用严重或短缺，不一定总能对人群进行疫苗接种。因此，鉴定具有高传播疾病潜力的个体和针对这些个体的定向疫苗接种非常重要。尽管在网络流行病学文献中已经提出了多种识别此类个体的策略，但其中绝大多数仅依赖于网络拓扑。相反，在本文中，我们提出了一种新颖的针对性疫苗接种策略，该策略同时考虑了静态网络拓扑和网络节点随时间的动态状态。这使我们的策略能够找到在任何给定时间点传播疾病可能性最高的个体。我们对各种现实世界的网络拓扑结构，网络规模，疫苗接种预算以及传染模型的参数进行了广泛的评估，结果表明，在减少疫苗接种的传播方面，所提出的策略在性能上要远远超过现有的最新目标疫苗接种策略。疾病。尤其是，与基于介乎中心性的疫苗接种策略相比，拟议的疫苗接种策略进一步将感染节点的数量减少了23–99％。证明了拟议的策略在减少疾病传播方面明显优于现有的最新定向疫苗接种策略。尤其是，与基于“中间性中心”的疫苗接种策略相比，拟议的疫苗接种策略进一步将感染节点的数量减少了23–99％。证明了拟议的策略在减少疾病传播方面明显优于现有的最新定向疫苗接种策略。尤其是，与基于“中间性中心”的疫苗接种策略相比，拟议的疫苗接种策略进一步将感染节点的数量减少了23–99％。