Probabilistic discrete-time models for spreading processes in complex networks: a review

Granell, Clara ; Gómez, Sergio ; Gómez-Gardeñes, Jesús (Universidad de Zaragoza) ; Arenas, Alex

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.

Idioma: Inglés

DOI: 10.1002/andp.202400078

Año: 2024

Publicado en: ANNALEN DER PHYSIK 536, 10 (2024), 2400078 [20 pp.]
ISSN: 0003-3804

Factor impacto JCR: 2.5 (2024)

Categ. JCR: PHYSICS, MULTIDISCIPLINARY rank: 43 / 114 = 0.377 (2024) - Q2 - T2

Factor impacto SCIMAGO: 0.563 - Physics and Astronomy (miscellaneous) (Q2)

Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-113582GB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2020-128005NB-C21
Financiación: info:eu-repo/grantAgreement/ES/UZ-IBERCAJA/224220

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Física Materia Condensada (Dpto. Física Materia Condensa.)