| 来源类型 | Monograph (IIASA Interim Report) |
| 规范类型 | 报告 |
| Long-range Correlations Improve Understanding the Influence of Network Structure on Contact Dynamics. | |
| Peyrard N; Dieckmann U; Franc A | |
| 发表日期 | 2008 |
| 出版者 | IIASA, Laxenburg, Austria: IR-08-044 |
| 出版年 | 2008 |
| 语种 | 英语 |
| 摘要 | Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a hosts interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable, and significantly improves the approximations performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a networks clustering coefficient, as well as of new geometric measures, such as a networks square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation - extended to incorporate long-range correlations according to our method - is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence. |
| 主题 | Evolution and Ecology (EEP) |
| URL | http://pure.iiasa.ac.at/id/eprint/8745/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/125638 |
| 推荐引用方式 GB/T 7714 | Peyrard N,Dieckmann U,Franc A. Long-range Correlations Improve Understanding the Influence of Network Structure on Contact Dynamics.. 2008. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| IR-08-044.pdf（771KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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