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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Multiscale, resurgent epidemics in a hierarchical metapopulation model
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Multiscale, resurgent epidemics in a hierarchical metapopulation model

机译：分层元人口模型中的多尺度，复发性流行病

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摘要

Although population structure has long been recognized as relevant to the spread of infectious disease, traditional mathematical models have understated the role of nonhomogenous mixing in populations with geographical and social structure. Recently, a wide variety of spatial and network models have been proposed that incorporate various aspects of interaction structure among individuals. However, these more complex models necessarily suffer from limited tractability, rendering general conclusions difficult to draw. In seeking a compromise between parsimony and realism, we introduce a class of metapopulation models in which we assume homogeneous mixing holds within local contexts, and that these contexts are embedded in a nested hierarchy of successively larger domains. We model the movement of individuals between contexts via simple transport parameters and allow diseases to spread stochastically. Our model exhibits some important stylized features of real epidemics, including extreme size variation and temporal heterogeneity, that are difficult to characterize with traditional measures. In particular, our results suggest that when epidemics do occur the basic reproduction number R-0 may bear little relation to their final size. informed by our model's behavior, we suggest measures for characterizing epidemic thresholds and discuss implications for the control of epidemics.
机译：尽管长期以来人们一直认为人口结构与传染病的传播有关，但是传统的数学模型低估了具有地理和社会结构的人口中非均匀混合的作用。近来，已经提出了各种各样的空间和网络模型，其结合了个体之间的交互结构的各个方面。但是，这些更复杂的模型必然具有有限的易处理性，从而难以得出一般性结论。为了在简约性和现实主义之间寻求折衷，我们引入了一类亚种群模型，在该模型中，我们假设均质混合在局部上下文中成立，并且这些上下文嵌入在依次较大的域的嵌套层次结构中。我们通过简单的运输参数对个体在环境之间的运动进行建模，并允许疾病随机传播。我们的模型展现了真实流行病的一些重要的程式化特征，包括极端规模变化和时间异质性，这些特征很难用传统方法来表征。特别是，我们的研究结果表明，当流行病发生时，基本繁殖数R-0与其最终大小几乎没有关系。根据我们模型的行为，我们建议表征流行阈值的措施，并讨论对流行控制的影响。

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