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Building epidemiological models from R0: an implicit treatment of transmission in networks

机译:从R0建立流行病学模型:网络传播的隐性处理

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摘要

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, R0, 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.
机译:尽管越来越多的证据表明在个人层面上传播所依赖的联系网络的重要性,但简单的确定性模型仍是理论流行病学的核心。这些基于均质混合的均值场或“区隔”模型对传染病的流行病学和生态学做出了并将继续做出重要贡献,但未能再现观察到的在接触网络中传播疾病的许多特征。在这项工作中,我们表明有可能将网络结构对疾病传播的重要影响与从个人层面考虑得出的均值场模型相结合。我们建议将被称为疾病基本生殖数的基本数R0(通常作为阈值量导出)用作构建模型的中心参数。我们表明,对各个级别参数的可靠估计可以代替对网络结构的详细了解,而这通常可能很难获得。我们用小世界网络说明了该模型,并举例说明了易感,感染,恢复(SIR)流行病。

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