ArticleNature communications2019
Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Temporal heterogeneity shapes diffusion dynamics in complex networks.Nature communications · 2026Article
- Fast and exact stochastic simulations of epidemics on static and temporal networks.PLoS computational biology · 2025Article
- Assessing the impact of interregional mobility on COVID19 spread in Spain using transfer entropy.Scientific reports · 2025Article
- Analytical and Numerical Treatment of Continuous Ageing in the Voter Model.Entropy (Basel, Switzerland) · 2022Article
- Endemic state equivalence between non-Markovian SEIS and Markovian SIS model in complex networks.Physica A · 2022Article
- Non-Markovian SIR epidemic spreading model of COVID-19.Chaos, solitons, and fractals · 2022Article
- Epidemic dynamics on higher-dimensional small world networks.Applied mathematics and computation · 2022Article
- Estimation of the basic reproduction number of COVID-19 from the incubation period distribution.The European physical journal. Special topics · 2022Article
- Transient inconsistency between population density and fisheries yields without bycatch species extinction.Ecology and evolution · 2020Article
- Non-Markovian recovery makes complex networks more resilient against large-scale failures.Nature communications · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epidemic spreading processes in the real world depend on human behaviors and, consequently, are typically non-Markovian in that the key events underlying the spreading dynamics cannot be described as a Poisson random process and the corresponding event time is not exponentially distributed. In contrast to Markovian type of spreading dynamics for which mathematical theories have been well developed, we lack a comprehensive framework to analyze and fully understand non-Markovian spreading processes. Here we develop a mean-field theory to address this challenge, and demonstrate that the theory enables accurate prediction of both the transient phase and the steady states of non-Markovian susceptible-infected-susceptible spreading dynamics on synthetic and empirical networks. We further find that the existence of equivalence between non-Markovian and Markovian spreading depends on a specific edge activation mechanism. In particular, when temporal correlations are absent on active edges, the equivalence can be expected; otherwise, an exact equivalence no longer holds.
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Registered trials
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