Building epidemiological models from R0:: an implicit treatment of transmission in networks
Building epidemiological models from R0:: an implicit treatment of transmission in networks
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DOI：
10.1098/rspb.2006.0057
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发表时间：
2007-02-22
影响因子：
4.7
通讯作者：
Pascual, Mercedes
中科院分区：
文献类型：
--
作者：
Aparicio, Juan Pablo;Pascual, Mercedes
简单的确定性模型仍然是理论流行病学的核心，尽管越来越多的证据表明接触网络在个人层面传播的重要性。这些平均场或“房室”模型的基础上均匀混合，并继续作出重要贡献，传染病的流行病学和生态学，但未能重现许多功能观察到的疾病传播接触网络。在这项工作中，我们表明，它是可能的，将网络结构对疾病传播的重要影响与来自个人层面的考虑平均场模型。我们建议，被称为疾病的基本再生数的基本数，R-0，这通常是作为一个阈值量，而不是作为一个中心参数来构建模型。我们表明，可靠的估计个人水平的参数可以取代详细的网络结构的知识，这在一般情况下可能很难获得。我们说明了所提出的模型与小世界网络和经典的例子，易感染的恢复（SIR）流行病。
Simple deterministic models are still at the core of theoretical epidemiology despite the increasing evidence for the importance of contact networks underlying transmission at the individual level. These mean-field or 'compartmental' models based on homogeneous mixing have made, and continue to make, important contributions to the epidemiology and the ecology of infectious diseases but fail to reproduce many of the features observed for disease spread in contact networks. In this work, we show that it is possible to incorporate the important effects of network structure on disease spread with a mean-field model derived from individual level considerations. We propose that the fundamental number known as the basic reproductive number of the disease, R-0, which is typically derived as a threshold quantity, be used instead as a central parameter to construct the model from. We show that reliable estimates of individual level parameters can replace a detailed knowledge of network structure, which in general may be difficult to obtain. We illustrate the proposed model with small world networks and the classical example of susceptible-infected-recovered (SIR) epidemics.