Alişan, YiğitIlhan, Nagehan2025-01-062025-01-0620232149-636610.46810/tdfd.1239359https://doi.org/10.46810/tdfd.1239359https://search.trdizin.gov.tr/tr/yayin/detay/1191924https://hdl.handle.net/20.500.14669/805Compartmental mathematical models are frequently used in epidemiology. These models are based on certain assumptions to mathematically model real-life events. However, these assumptions have some limitations. One of these limitations is that they assume that the community is homogeneous, although communities are often heterogeneous. For example, a community may have people or super-spreaders who are not in contact with anyone infected with the virus. In case of limited opportunities, the rate of disease spread can be reduced by vaccinating super-spreaders instead of normal individuals. In the study, centrality values of each individual in the community are determined using a real data set. Vaccinated (immune) and infected individuals are then selected according to certain criteria, and disease spread is simulated. Finally, results are produced using the SIR model, which is the basis of compartmental models. According to the results obtained, the minimum amount of vaccine required to prevent disease spread is calculated. As a result, it was concluded that using the recommended method instead of traditional methods to prevent the spread of disease in the community will result in a 14.39% reduction in vaccine usage.eninfo:eu-repo/semantics/openAccessSIR modelSocial network analysisCommunity detectionCentrality measuresArticle47240119192412