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Modeling the social obesity epidemic with stochastic networks

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  • González-Parra, Gilberto
  • Acedo, L.
  • Villanueva Micó, Rafael-J.
  • Arenas, Abraham J.

Abstract

In this paper we extend a compartmental model to the case of a homogenous network epidemic model for a study of the dynamics of obese populations. The social epidemic network-based approach developed here uses different algorithms and points of views regarding the simulation of the dynamics of the network. First, Monte Carlo simulations for homogeneous networks using a traditional constant probability transition rates and a mean-field-like approach are presented. We show that these networks evolve towards an approximately stationary state, which coincides with the one obtained by the underlying classical compartmental continuous model. A mean-field-like approach is applied in order to reduce the large computation time required when dealing with large contact networks. We also investigate, using homogenous contact networks, the effect of the realistic assumption that the waiting times between subpopulations follow a gamma distribution instead of the traditional exponential distribution. It is concluded that careful attention must be paid to the distributions assumed for the state periods.

Suggested Citation

  • González-Parra, Gilberto & Acedo, L. & Villanueva Micó, Rafael-J. & Arenas, Abraham J., 2010. "Modeling the social obesity epidemic with stochastic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3692-3701.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:17:p:3692-3701
    DOI: 10.1016/j.physa.2010.04.024
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    References listed on IDEAS

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    1. Wu, Xiaoyan & Liu, Zonghua, 2008. "How community structure influences epidemic spread in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 623-630.
    2. Noymer, Andrew, 2001. "The Transmission and Persistence of`'Urban Legends': Sociological Application of Age-Structured Epidemic Models," Center for Culture, Organizations and Politics, Working Paper Series qt0rv3c82q, Center for Culture, Organizations and Politics of theInstitute for Research on Labor and Employment, UC Berkeley.
    3. Peng, Chengbin & Jin, Xiaogang & Shi, Meixia, 2010. "Epidemic threshold and immunization on generalized networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 549-560.
    4. Acedo, L., 2006. "A second-order phase transition in the complete graph stochastic epidemic model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 613-624.
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    Cited by:

    1. Sun, Ruoyan, 2016. "Optimal weight based on energy imbalance and utility maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 429-435.
    2. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Enrique Lozano-Ochoa & Jorge Fernando Camacho & Cruz Vargas-De-León, 2017. "Qualitative Stability Analysis of an Obesity Epidemic Model with Social Contagion," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-12, January.
    4. Abidemi, Afeez & Owolabi, Kolade M. & Pindza, Edson, 2022. "Modelling the transmission dynamics of Lassa fever with nonlinear incidence rate and vertical transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    5. Luo, Xiaojuan & Hu, Yuhen & Zhu, Yu, 2014. "Topology evolution model for wireless multi-hop network based on socially inspired mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 639-650.

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