Cargando…
Coupling effects on turning points of infectious diseases epidemics in scale-free networks
BACKGROUND: Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ...
Autores principales: | Kim, Kiseong, Lee, Sangyeon, Lee, Doheon, Lee, Kwang Hyung |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5471948/ https://www.ncbi.nlm.nih.gov/pubmed/28617223 http://dx.doi.org/10.1186/s12859-017-1643-7 |
Ejemplares similares
-
Large-scale prediction of adverse drug reactions-related proteins with network embedding
por: Park, Jaesub, et al.
Publicado: (2022) -
Deep learning-based classification with improved time resolution for physical activities of children
por: Jang, Yongwon, et al.
Publicado: (2018) -
The turning point and end of an expanding epidemic cannot be precisely forecast
por: Castro, Mario, et al.
Publicado: (2020) -
Highly Adsorptive
Au-TiO(2) Nanocomposites
for the SERS Face Mask Allow the Machine-Learning-Based Quantitative
Assay of SARS-CoV-2 in Artificial Breath Aerosols
por: Hwang, Charles S. H., et al.
Publicado: (2022) -
Literature mining for context-specific molecular relations using multimodal representations (COMMODAR)
por: Lee, Jaehyun, et al.
Publicado: (2020)