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Simulation of Covid-19 contamination in a student network using the concept of centrality in graphs

Author

Listed:
  • Pâmela Carvalho Marques Silva

    (Universidade Federal Fluminense)

  • Renata Raposo Del-Vecchio

    (Universidade Federal Fluminense)

  • Atila Arueira Jones

    (Department, Instituto Federal de Educaçã, Ciência e Tecnologia do Sudeste de Minas Gerais)

Abstract

With the advance of the COVID-19 pandemic, the emergence of new variants of the virus and peaks of the disease that occur seasonally until today, the study of disease proliferation becomes important. Thus, this study simulates the transmission of the virus in a network of undergraduate students from a Brazilian public university, which implemented the return to face-to-face classes at the beginning of 2022, using the concept of centrality in graphs. Several scenarios were considered, taking different groups as the first infected and analyzing the propagation effect of the disease in the network. The individuals who would represent the highest possible risk of inducing the disease, if infected, were detected through measures of centrality in networks. In addition, we also observed the peak of the disease, noting the highest number of infected people and the time to reach this peak, depending on the definition of the first infected. The identification of those first infected considering their importance in the network, via centrality measures, determines the disease cycle.

Suggested Citation

  • Pâmela Carvalho Marques Silva & Renata Raposo Del-Vecchio & Atila Arueira Jones, 2024. "Simulation of Covid-19 contamination in a student network using the concept of centrality in graphs," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3063-3085, August.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-023-01789-3
    DOI: 10.1007/s11135-023-01789-3
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