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多尺度移动性网络与传染病的空间传播。

Multiscale mobility networks and the spatial spreading of infectious diseases.

机构信息

Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.

出版信息

Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21484-9. doi: 10.1073/pnas.0906910106. Epub 2009 Dec 14.

DOI:10.1073/pnas.0906910106
PMID:20018697
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2793313/
Abstract

Among the realistic ingredients to be considered in the computational modeling of infectious diseases, human mobility represents a crucial challenge both on the theoretical side and in view of the limited availability of empirical data. To study the interplay between short-scale commuting flows and long-range airline traffic in shaping the spatiotemporal pattern of a global epidemic we (i) analyze mobility data from 29 countries around the world and find a gravity model able to provide a global description of commuting patterns up to 300 kms and (ii) integrate in a worldwide-structured metapopulation epidemic model a timescale-separation technique for evaluating the force of infection due to multiscale mobility processes in the disease dynamics. Commuting flows are found, on average, to be one order of magnitude larger than airline flows. However, their introduction into the worldwide model shows that the large-scale pattern of the simulated epidemic exhibits only small variations with respect to the baseline case where only airline traffic is considered. The presence of short-range mobility increases, however, the synchronization of subpopulations in close proximity and affects the epidemic behavior at the periphery of the airline transportation infrastructure. The present approach outlines the possibility for the definition of layered computational approaches where different modeling assumptions and granularities can be used consistently in a unifying multiscale framework.

摘要

在传染病的计算建模中,需要考虑许多现实因素,其中人类流动性是一个具有挑战性的问题,无论是在理论方面还是在有限的经验数据可用性方面都是如此。为了研究短期通勤流动和长途航空交通在塑造全球流行病的时空模式方面的相互作用,我们(i)分析了来自全球 29 个国家的流动数据,并找到了一个能够提供全球范围内通勤模式描述的重力模型,其范围可达 300 公里;(ii)在全球结构的元胞自动机传染病模型中,我们整合了一种时间尺度分离技术,用于评估多尺度流动过程在疾病动力学中对感染强度的影响。平均而言,通勤流动的规模比航空流量大一个数量级。然而,将它们引入全球模型表明,模拟流行病的大尺度模式相对于仅考虑航空交通的基线案例只有很小的变化。然而,短距离流动性的存在增加了近距离亚群的同步性,并影响了航空运输基础设施周边的流行病行为。本研究方法概述了定义分层计算方法的可能性,在统一的多尺度框架中,可以使用不同的建模假设和粒度来一致地定义这些方法。

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