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Influence of Contact Network Topology on the Spread of Tuberculosis

  • Eduardo R. Pinto
  • Erivelton G. Nepomuceno
  • Andriana S. L. O. CampanharoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1068)

Abstract

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.

Keywords

Tuberculosis Topological effect Complex networks Individual-Based Model Complex systems 

Notes

Acknowledgments

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eduardo R. Pinto
    • 1
  • Erivelton G. Nepomuceno
    • 2
  • Andriana S. L. O. Campanharo
    • 3
    Email author
  1. 1.Institute of Biosciences, Postgraduate Program in BiometricsSão Paulo State University (UNESP)BotucatuBrazil
  2. 2.Department of Electrical EngineeringFederal University of São João del-Rei (UFSJ)São João del-ReiBrazil
  3. 3.Institute of Biosciences, Department of BiostatisticsSão Paulo State University (UNESP)BotucatuBrazil

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