Gursakal, N.Batmaz, B.Aktuna, G.2025-01-112025-01-11202060950-26881469-440910.1017/S09502688200026542-s2.0-85096010466https://doi.org/10.1017/S0950268820002654https://hdl.handle.net/20.500.14627/120Aktuna, Gamze/0000-0003-4575-7763; BATMAZ, Bulent/0000-0003-4706-5808When we consider a probability distribution about how many COVID-19-infected people will transmit the disease, two points become important. First, there could be super-spreaders in these distributions/networks and second, the Pareto principle could be valid in these distributions/networks regarding estimation that 20% of cases were responsible for 80% of local transmission. When we accept that these two points are valid, the distribution of transmission becomes a discrete Pareto distribution, which is a kind of power law. Having such a transmission distribution, then we can simulate COVID-19 networks and find super-spreaders using the centricity measurements in these networks. In this research, in the first we transformed a transmission distribution of statistics and epidemiology into a transmission network of network science and second we try to determine who the super-spreaders are by using this network and eigenvalue centrality measure. We underline that determination of transmission probability distribution is a very important point in the analysis of the epidemic and determining the precautions to be taken.eninfo:eu-repo/semantics/openAccessCovid-19Network ScienceReproduction NumberSuper-SpreaderTransmission GraphsArticleQ2Q2148WOS:00061985670000133143782