Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran.
Department of Mathematics, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran.
Sci Rep. 2023 Aug 30;13(1):14217. doi: 10.1038/s41598-023-41460-3.
The COVID-19 pandemic has caused significant disruptions to the daily lives of individuals worldwide, with many losing their lives to the virus. Vaccination has been identified as a crucial strategy to combat the spread of a disease, but with a limited supply of vaccines, targeted blocking is becoming increasingly necessary. One such approach is to block a select group of individuals in the community to control the spread of the disease in its early stages. Therefore, in this paper, a method is proposed for solving this problem, based on the similarity between this issue and the problem of identifying super-spreader nodes. The proposed method attempts to select the minimum set of network nodes that, when removed, no large component remains in the network. To this end, the network is partitioned into various communities, and a method for limiting the spread of the disease to communities is proposed by blocking connecting nodes. Four real networks and four synthetics networks created using the LFR algorithm were used to evaluate the control of the disease by the selected set of nodes using each method, and the results obtained indicate better performance of the proposed method compared to other methods.
新冠疫情对全球个人的日常生活造成了重大干扰，许多人因该病毒失去了生命。疫苗接种被确定为控制疾病传播的关键策略，但由于疫苗供应有限，有针对性的阻断措施变得越来越必要。其中一种方法是在社区中阻断选择的一组人群，以在疾病早期控制其传播。因此，在本文中，提出了一种基于该问题与识别超级传播者节点问题之间的相似性来解决此问题的方法。该方法试图选择网络节点的最小集合，当删除这些节点时，网络中不会存在较大的组件。为此，将网络划分为不同的社区，并通过阻塞连接节点提出了一种限制疾病在社区内传播的方法。使用每个方法评估所选节点集对疾病的控制效果，使用了四个真实网络和四个使用 LFR 算法创建的合成网络，结果表明与其他方法相比，所提出的方法具有更好的性能。
