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

  • Identification data

    Identifier: imarina:9379072
    Authors:
    Granell CGómez SGómez-Gardeñes JArenas A
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Granell C; Gómez S; Gómez-Gardeñes J; Arenas A
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio / Granell Martorell, Clara
    Keywords: Transmission Threshol Networks Network Modeling Metapopulation models Epidemiology Epidemics Dynamics Contagions Challenges Behavior
    Abstract: 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.
    Thematic Areas: Physics, multidisciplinary Physics and astronomy (miscellaneous) Physics and astronomy (all) Physics Matemática / probabilidade e estatística General physics and astronomy Filosofía Engenharias iii Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: clara.granell@urv.cat sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Author identifier: 0000-0003-1820-0062 0000-0003-0937-0334
    Record's date: 2024-10-26
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://onlinelibrary.wiley.com/doi/10.1002/andp.202400078
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Annalen Der Physik. 536 (10):
    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
    Article's DOI: 10.1002/andp.202400078
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2024
    Publication Type: Journal Publications
  • Keywords:

    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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