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用于研究流行病的综合旅行网络模型:行程与流行病之间的相互作用

Integrated travel network model for studying epidemics: Interplay between journeys and epidemic.

作者信息

Ruan Zhongyuan, Wang Chaoqing, Hui Pak Ming, Liu Zonghua

机构信息

1] Department of Physics, East China Normal University, Shanghai, 200062, China [2] Center for Network Science, Central European University.

Department of Physics, East China Normal University, Shanghai, 200062, China.

出版信息

Sci Rep. 2015 Jun 15;5:11401. doi: 10.1038/srep11401.

Abstract

The ease of travelling between cities has contributed much to globalization. Yet, it poses a threat on epidemic outbreaks. It is of great importance for network science and health control to understand the impact of frequent journeys on epidemics. We stress that a new framework of modelling that takes a traveller's viewpoint is needed. Such integrated travel network (ITN) model should incorporate the diversity among links as dictated by the distances between cities and different speeds of different modes of transportation, diversity among nodes as dictated by the population and the ease of travelling due to infrastructures and economic development of a city, and round-trip journeys to targeted destinations via the paths of shortest travel times typical of human journeys. An example is constructed for 116 cities in China with populations over one million that are connected by high-speed train services and highways. Epidemic spread on the constructed network is studied. It is revealed both numerically and theoretically that the traveling speed and frequency are important factors of epidemic spreading. Depending on the infection rate, increasing the traveling speed would result in either an enhanced or suppressed epidemic, while increasing the traveling frequency enhances the epidemic spreading.

摘要

城市间出行的便捷极大地推动了全球化进程。然而,这也对疫情爆发构成了威胁。对于网络科学和健康防控而言,了解频繁出行对疫情的影响至关重要。我们强调,需要一个从旅行者视角出发的新型建模框架。这种综合旅行网络(ITN)模型应纳入以下因素:城市间距离和不同交通方式的不同速度所决定的链路多样性;城市人口以及基础设施和经济发展带来的出行便利性所决定的节点多样性;以及通过人类旅程中典型的最短旅行时间路径往返目标目的地的情况。以中国116个人口超过百万且由高铁服务和高速公路相连的城市为例进行构建。研究了在构建的网络上疫情的传播情况。从数值和理论两方面揭示出行速度和频率是疫情传播的重要因素。根据感染率,提高出行速度可能导致疫情增强或受到抑制,而增加出行频率则会增强疫情传播。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/514e/4466778/f31ea19a157d/srep11401-f1.jpg

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