Author: Lin WANG; Xiang Li
Title: Spatial epidemiology of networked metapopulation: An overview Document date: 2014_6_4
ID: i9tbix2v_29
Snippet: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/003889 doi: bioRxiv preprint can be summarized as R ⋆ = E(R 0 , µ)T (k, θ, ω 0 ), which combines the epidemiology factors E(R 0 , µ) with the diffusion properties of mobility networks T (k, θ, ω 0 ). For large networks with a high-level topological heterogeneity, the mobility item T diverges, i.e., T −1 → 0, thus R ⋆ is always large.....
Document: The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/003889 doi: bioRxiv preprint can be summarized as R ⋆ = E(R 0 , µ)T (k, θ, ω 0 ), which combines the epidemiology factors E(R 0 , µ) with the diffusion properties of mobility networks T (k, θ, ω 0 ). For large networks with a high-level topological heterogeneity, the mobility item T diverges, i.e., T −1 → 0, thus R ⋆ is always larger than unity, which leads to a decreased epidemic threshold. Based on the observation that human beings usually do not perform random walks, yet have specific travel destinations, Tang et al. [90] addressed the effect of objective traveling behavior which enlarges the final morbidity.
Search related documents:
Co phrase search for related documents- decreased epidemic threshold and high level: 1, 2
- epidemic threshold and high level: 1, 2, 3, 4
- epidemic threshold and large network: 1
- epidemic threshold and mobility network: 1, 2
- epidemic threshold and random walk: 1, 2, 3, 4
Co phrase search for related documents, hyperlinks ordered by date