Articles producció científica> Enginyeria Informàtica i Matemàtiques

Nonperturbative heterogeneous mean-field approach to epidemic spreading in complex networks

  • Identification data

    Identifier: imarina:9285113
    Authors:
    Gomez, SergioGomez-Gardenes, JesusMoreno, YamirArenas, Alex
    Abstract:
    Since roughly a decade ago, network science has focused among others on the problem of how the spreading of diseases depends on structural patterns. Here, we contribute to further advance our understanding of epidemic spreading processes by proposing a nonperturbative formulation of the heterogeneous mean-field approach that has been commonly used in the physics literature to deal with this kind of spreading phenomena. The nonperturbative equations we propose have no assumption about the proximity of the system to the epidemic threshold, nor any linear approximation of the dynamics. In particular, we first develop a probabilistic description at the node level of the epidemic propagation for the so-called susceptible-infected-susceptible family of models, and after we derive the corresponding heterogeneous mean-field approach. We propose to use the full extension of the approach instead of pruning the expansion to first order, which leads to a nonperturbative formulation that can be solved by fixed-point iteration, and used with reliability far away from the epidemic threshold to assess the prevalence of the epidemics. Our results are in close agreement with Monte Carlo simulations, thus enhancing the predictive power of the classical heterogeneous mean-field approach, while providing a more effective framework in terms of computational time. © 2011 American Physical Society.
  • Others:

    Author, as appears in the article.: Gomez, Sergio; Gomez-Gardenes, Jesus; Moreno, Yamir; Arenas, Alex
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio
    Keywords: Susceptible-infected-susceptible Structural pattern Probabilistic descriptions Predictive power Nonperturbative Network science Monte carlo simulation Monte carlo methods Mean field approach Linear approximations Intelligent systems Fixed-point iterations First order Epidemiology Epidemic threshold Epidemic spreading Epidemic propagation Computer simulation Computational time Complex networks
    Abstract: Since roughly a decade ago, network science has focused among others on the problem of how the spreading of diseases depends on structural patterns. Here, we contribute to further advance our understanding of epidemic spreading processes by proposing a nonperturbative formulation of the heterogeneous mean-field approach that has been commonly used in the physics literature to deal with this kind of spreading phenomena. The nonperturbative equations we propose have no assumption about the proximity of the system to the epidemic threshold, nor any linear approximation of the dynamics. In particular, we first develop a probabilistic description at the node level of the epidemic propagation for the so-called susceptible-infected-susceptible family of models, and after we derive the corresponding heterogeneous mean-field approach. We propose to use the full extension of the approach instead of pruning the expansion to first order, which leads to a nonperturbative formulation that can be solved by fixed-point iteration, and used with reliability far away from the epidemic threshold to assess the prevalence of the epidemics. Our results are in close agreement with Monte Carlo simulations, thus enhancing the predictive power of the classical heterogeneous mean-field approach, while providing a more effective framework in terms of computational time. © 2011 American Physical Society.
    Thematic Areas: Zootecnia / recursos pesqueiros Statistics and probability Statistical and nonlinear physics Saúde coletiva Química Physics, mathematical Physics, fluids & plasmas Odontología Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General medicine Farmacia Engenharias iv Engenharias iii Engenharias ii Educação física Educação Economia Condensed matter physics Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Author identifier: 0000-0003-1820-0062 0000-0003-0937-0334
    Record's date: 2024-09-28
    Papper version: info:eu-repo/semantics/submittedVersion
    Link to the original source: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.84.036105
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Physical Review e. 84 (3): 036105-
    APA: Gomez, Sergio; Gomez-Gardenes, Jesus; Moreno, Yamir; Arenas, Alex (2011). Nonperturbative heterogeneous mean-field approach to epidemic spreading in complex networks. Physical Review e, 84(3), 036105-. DOI: 10.1103/PhysRevE.84.036105
    Article's DOI: 10.1103/PhysRevE.84.036105
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2011
    Publication Type: Journal Publications
  • Keywords:

    Condensed Matter Physics,Physics, Fluids & Plasmas,Physics, Mathematical,Statistical and Nonlinear Physics,Statistics and Probability
    Susceptible-infected-susceptible
    Structural pattern
    Probabilistic descriptions
    Predictive power
    Nonperturbative
    Network science
    Monte carlo simulation
    Monte carlo methods
    Mean field approach
    Linear approximations
    Intelligent systems
    Fixed-point iterations
    First order
    Epidemiology
    Epidemic threshold
    Epidemic spreading
    Epidemic propagation
    Computer simulation
    Computational time
    Complex networks
    Zootecnia / recursos pesqueiros
    Statistics and probability
    Statistical and nonlinear physics
    Saúde coletiva
    Química
    Physics, mathematical
    Physics, fluids & plasmas
    Odontología
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    General medicine
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Educação física
    Educação
    Economia
    Condensed matter physics
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
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