Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1073/pnas.1910181117 |
Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering | |
David J. Haw; Rachael Pung; Jonathan M. Read; Steven Riley | |
2020-09-08 | |
发表期刊 | Proceedings of the National Academy of Sciences
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出版年 | 2020 |
英文摘要 | Some directly transmitted human pathogens, such as influenza and measles, generate sustained exponential growth in incidence and have a high peak incidence consistent with the rapid depletion of susceptible individuals. Many do not. While a prolonged exponential phase typically arises in traditional disease-dynamic models, current quantitative descriptions of nonstandard epidemic profiles are either abstract, phenomenological, or rely on highly skewed offspring distributions in network models. Here, we create large socio-spatial networks to represent contact behavior using human population-density data, a previously developed fitting algorithm, and gravity-like mobility kernels. We define a basic reproductive number |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/294050 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | David J. Haw,Rachael Pung,Jonathan M. Read,et al. Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering[J]. Proceedings of the National Academy of Sciences,2020. |
APA | David J. Haw,Rachael Pung,Jonathan M. Read,&Steven Riley.(2020).Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering.Proceedings of the National Academy of Sciences. |
MLA | David J. Haw,et al."Strong spatial embedding of social networks generates nonstandard epidemic dynamics independent of degree distribution and clustering".Proceedings of the National Academy of Sciences (2020). |
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