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Influence of Contact Network Topology on the Spread of Tuberculosis

Part of the Communications in Computer and Information Science book series (CCIS,volume 1068)


This paper presents the influence of the complex networks topology on the spread of Tuberculosis with the use of the Individual-Based Model (IBM). Five complex network models were used with the IBM, namely, random, small world, scale-free, modular and hierarchical models. For every model, we applied the usual topological properties available in literature for the characterization of complex networks. Afterwards, we verified the topological effect of the contact networks in the evolution of tuberculosis and it was observed that different contact networks result in different epidemic thresholds \((\beta ^*)\) for the spread of tuberculosis. More specifically, we noted that networks that have greater heterogeneity of connections need a lower \(\beta ^*\), however when the value of the infection rate \((\beta )\) is large, the number of individuals infected are similar. It is believed that this observation may contribute to actions to reduce and eradicate the disease.


  • Tuberculosis
  • Topological effect
  • Complex networks
  • Individual-Based Model
  • Complex systems

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  1. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)

    MathSciNet  CrossRef  Google Scholar 

  2. Dodds, P.S., Watts, D.J., Sabel, C.F.: Information exchange and the robustness of organizational networks. Proc. Nat. Acad. Sci. 100(21), 12516–12521 (2003)

    CrossRef  Google Scholar 

  3. Edling, C.R., Åberg, Y., Liljeros, F., Amaral, L.A.N., Stanley, H.E.: The web of human sexual contacts. Nature 411(6840), 907–908 (2002)

    Google Scholar 

  4. Erdös, P., Rényi, A.: On random graphs. Publ. Math. Debrecen 6, 290–297 (1959)

    MathSciNet  MATH  Google Scholar 

  5. Keeling, M.J., Grenfell, B.T.: Individual-based perspectives on R0. J. Theor. Biol. 203(1), 51–61 (2000)

    CrossRef  Google Scholar 

  6. Moreno, V., et al.: The role of mobility and health disparities on the transmission dynamics of tuberculosis. Theor. Biol. Med. Modell. 14(1), 1–17 (2017)

    CrossRef  Google Scholar 

  7. Nepomuceno, E.G., Takahashi, R.H.C., Aguirre, L.A.: Individual based-model (IBM): an alternative framework for epidemiological compartment models. Biometric Braz. J./Revista Brasileira de Biometria 34(1), 133–162 (2016)

    Google Scholar 

  8. Nepomuceno, E.G., Barbosa, A.M., Silva, M.X., Perc, M.: Individual-based modelling and control of bovine brucellosis. Roy. Soc. Open Sci. 5(5), 180200 (2018)

    CrossRef  Google Scholar 

  9. Newman, M.E.J.: Spread of epidemic disease on networks. Phys. Rev. E 66(1), 016128 (2002)

    MathSciNet  CrossRef  Google Scholar 

  10. Newman, M.E.J.: Networks: An Introduction. Oxford University Press, New York (2010)

    CrossRef  Google Scholar 

  11. World Health Organization, et al.: Global tuberculosis report 2016. World Health Organization (2016)

    Google Scholar 

  12. Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Revi. Mod. Phys. 87(3), 925–979 (2015)

    MathSciNet  CrossRef  Google Scholar 

  13. Pinto, E.R., Campanharo, A.S.L.O.: Estudo do efeito topológico das redes contato na propagação de doenças infecciosas. Proc. Ser. Braz. Soc. Comput. Appl. Math. 6(2), 1–7 (2018)

    Google Scholar 

  14. Solé, R.V., Gamarra, J.G., Ginovart, M., López, D.: Controlling chaos in ecology: from deterministic to individual-based models. Bull. Math. Biol. 61(6), 1187–1207 (1999)

    CrossRef  Google Scholar 

  15. Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393(6684), 440 (1998)

    CrossRef  Google Scholar 

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E. R. Pinto acknowledges the support of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), grant 1770124 and supported by resources supplied by the Center for Scientific Computing (NCC/GridUNESP) of the São Paulo State University (UNESP). A. S. L. O. Campanharo acknowledges the support of Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant 2018/25358-9. All codes were written in C language and all figures were generated with XmGrace and Pajek.

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Correspondence to Andriana S. L. O. Campanharo .

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Pinto, E.R., Nepomuceno, E.G., Campanharo, A.S.L.O. (2019). Influence of Contact Network Topology on the Spread of Tuberculosis. In: Cota, V., Barone, D., Dias, D., Damázio, L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068. Springer, Cham.

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