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Probabilistic Discrete-Time Models for Spreading Processes in Complex Networks: A Review

  • Datos identificativos

    Identificador: imarina:9379072
    Autores:
    Granell CGómez SGómez-Gardeñes JArenas A
    Resumen:
    Research into network dynamics of spreading processes typically employs both discrete and continuous time methodologies. Although each approach offers distinct insights, integrating them can be challenging, particularly when maintaining coherence across different time scales. This review focuses on the Microscopic Markov Chain Approach (MMCA), a probabilistic f ramework originally designed for epidemic modeling. MMCA uses discrete dynamics to compute the probabilities of individuals transitioning between epidemiological states. By treating each time step-usually a day-as a discrete event, the approach captures multiple concurrent changes within this time frame. The approach allows to estimate the likelihood of individuals or populations being in specific states, which correspond to distinct epidemiological compartments. This review synthesizes key findings from the application of this approach, providing a comprehensive overview of its utility in understanding epidemic spread.
  • Otros:

    Autor según el artículo: Granell C; Gómez S; Gómez-Gardeñes J; Arenas A
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio / Granell Martorell, Clara
    Palabras clave: Transmission Threshol Networks Network Modeling Metapopulation models Epidemiology Epidemics Dynamics Contagions Challenges Behavior
    Resumen: Research into network dynamics of spreading processes typically employs both discrete and continuous time methodologies. Although each approach offers distinct insights, integrating them can be challenging, particularly when maintaining coherence across different time scales. This review focuses on the Microscopic Markov Chain Approach (MMCA), a probabilistic f ramework originally designed for epidemic modeling. MMCA uses discrete dynamics to compute the probabilities of individuals transitioning between epidemiological states. By treating each time step-usually a day-as a discrete event, the approach captures multiple concurrent changes within this time frame. The approach allows to estimate the likelihood of individuals or populations being in specific states, which correspond to distinct epidemiological compartments. This review synthesizes key findings from the application of this approach, providing a comprehensive overview of its utility in understanding epidemic spread.
    Áreas temáticas: Physics, multidisciplinary Physics and astronomy (miscellaneous) Physics and astronomy (all) Physics Matemática / probabilidade e estatística General physics and astronomy Filosofía Engenharias iii Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: clara.granell@urv.cat sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Identificador del autor: 0000-0003-1820-0062 0000-0003-0937-0334
    Fecha de alta del registro: 2024-10-26
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://onlinelibrary.wiley.com/doi/10.1002/andp.202400078
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Annalen Der Physik. 536 (10):
    Referencia de l'ítem segons les normes APA: Granell C; Gómez S; Gómez-Gardeñes J; Arenas A (2024). Probabilistic Discrete-Time Models for Spreading Processes in Complex Networks: A Review. Annalen Der Physik, 536(10), -. DOI: 10.1002/andp.202400078
    DOI del artículo: 10.1002/andp.202400078
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2024
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Physics,Physics and Astronomy (Miscellaneous),Physics, Multidisciplinary
    Transmission
    Threshol
    Networks
    Network
    Modeling
    Metapopulation models
    Epidemiology
    Epidemics
    Dynamics
    Contagions
    Challenges
    Behavior
    Physics, multidisciplinary
    Physics and astronomy (miscellaneous)
    Physics and astronomy (all)
    Physics
    Matemática / probabilidade e estatística
    General physics and astronomy
    Filosofía
    Engenharias iii
    Astronomia / física
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