Virus Spread in Networks

By Piet Van Mieghem, Jasmina Omic, R. Kooij. IEEE/ACM Transactions on Networking, 2009

1,029 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1db5b3fcdbfc80e8c3ed8bfece5c2a4cb38e0dce

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In Public Health, Environmental and Occupational Health

In Statistical and Nonlinear Physics

Citations in the corpus

The structure and dynamics of multilayer networks (2014, background): “[255] for SIS, and that represents an intermediate step between the accurate microscopic equations [235] and the HMF approximation.”
Controllability Metrics, Limitations and Algorithms for Complex Networks (2013, neutral): “…spreading The N-intertwined SIS model for the dynamics of a viral infection over a network with n nodes and adjacency matrix A = [ a ij ] reads as [44] ˙ p i = − α i p i + (1 − p i ) β i j i a ij p j , i ∈ { 1 , . . . , n , where p i : R ≥ 0 → R [0 , 1] is the map describing the infection…”
Heterogeneous virus propagation in networks: a theoretical study (2017, background): “[10] introduced the first continuous-time node-based epidemic model by modifying a traditional Susceptible-Infected-Susceptible (SIS) model.”
Statistical physics of vaccination (2016, background): “A modified set of generating functions is required to solve percolation problem with bond probability T : G0(u, T ) and G1(u, T ), where Gi(u, T ) = Gi(1 + (u − 1)T ) [228, 229].”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “Much ongoing interest is focusing on dynamic models of structural change [1] [25] [43] [54] [70], and on a broad range of dynamic processes unfolding over static networks; examples include the study of social learning [2] [35], opinion formation [23] [32] and information propagation [25] [57] [60].”
Dynamics of information diffusion and its applications on complex networks (2016, neutral): “The Q3N×3N matrix is decomposed to N infinitesimal matrices, in which each matrix is within three states [226,227].”