Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader node identification

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.