Full Text:   <1109>

Summary:  <493>

CLC number: TP39; R18

On-line Access: 2014-04-10

Received: 2013-08-31

Revision Accepted: 2014-02-28

Crosschecked: 2014-03-17

Cited: 2

Clicked: 2939

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.4 P.265-274

http://doi.org/10.1631/jzus.C1300243


Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes


Author(s):  Xiao-gang Jin, Yong Min

Affiliation(s):  AI Institute in College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   xiaogangj@cise.zju.edu.cn, myong@zju.edu.cn

Key Words:  Worldwide trade networks, Foodborne diseases, Scale-free networks, Mean-field analysis


Xiao-gang Jin, Yong Min. Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes[J]. Journal of Zhejiang University Science C, 2014, 15(4): 265-274.

@article{title="Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes",
author="Xiao-gang Jin, Yong Min",
journal="Journal of Zhejiang University Science C",
volume="15",
number="4",
pages="265-274",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300243"
}

%0 Journal Article
%T Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes
%A Xiao-gang Jin
%A Yong Min
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 4
%P 265-274
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300243

TY - JOUR
T1 - Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes
A1 - Xiao-gang Jin
A1 - Yong Min
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 4
SP - 265
EP - 274
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300243


Abstract: 
The frequent outbreak of severe foodborne diseases (e.g., haemolytic uraemic syndrome and Listeriosis) in 2011 warns of a potential threat that world trade could spread fatal pathogens (e.g., enterohemorrhagic Escherichia coli). The epidemic potential from trade involves both intra-proliferation and inter-diffusion. Here, we present a worldwide vegetable trade network and a stochastic computational model to simulate global trade-mediated epidemics by considering the weighted nodes and edges of the network and the dual-scale dynamics of epidemics. We address two basic issues of network structural impact in global epidemic patterns: (1) in contrast to the prediction of heterogeneous network models, the broad variability of node degree and edge weights of the vegetable trade network do not determine the threshold of global epidemics; (2) a ‘penetration effect’, by which community structures do not restrict propagation at the global scale, quickly facilitates bridging the edges between communities, and leads to synchronized diffusion throughout the entire network. We have also defined an appropriate metric that combines dual-scale behavior and enables quantification of the critical role of bridging edges in disease diffusion from widespread trading. The unusual structure mechanisms of the trade network model may be useful in producing strategies for adaptive immunity and reducing international trade frictions.

基于反应扩散过程的复杂网络双尺度传播动力学建模

研究目的:全球范围内的食物源传染病呈现出许多新的特征,包括病原体载体多样性、人与传播载体之间的复杂交互以及动力学行为的多尺度特性等。如何发展新的模型和方法来应对这些新的特征是一项关乎国计民生的重要课题。本文使用反应扩散过程,结合平均场分析,从计算机模拟和数学分析两个层面对新的传播动力学进行了建模和研究。
创新要点:经典的SIR和SIS传播模型都忽略了实际传播过程中的多尺度动力学过程,仅仅关注单一扩散行为。本文模型为多尺度复杂传播动力学的建模提供一种可行的思路。
方法提亮:利用反应扩散过程(reaction-diffusion processes),本文提出的模型简洁而又清晰地描述了复杂网络上不同尺度的传播行为,包括节点内部的增殖过程以及网络尺度的扩散过程。同时,利用平均场分析方法(mean-field analysis),为相关模型找到了数学求解的途径,从而为探索多尺度传播过程中的突现等非线性行为找到一种方法。
重要结论:(1)在多尺度动力学条件下,网络的非均匀度数分布的作用被削弱了,而节点内部的增殖机制扮演着更为重要的作用;(2)穿透效应降低了网络社团对于传播的阻碍;(3)基于双尺度的评价机制可以更准确地反映节点在传播中的重要程度。计算机模拟和数学分析均支持以上结论。

关键词:国际贸易网络;食物源传染病;无标度网络;平均场分析

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Amaral, L.A.N., Scala, A., Barthélémy, M., et al., 2000. Classes of small-world networks. Proc. Natl. Acad. Sci. USA, 97(21):11149-11152.

[2]Balcan, D., Colizza, V., Goncalves, B., et al., 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. Proc. Natl. Acad. Sci. USA, 106(51):21484-21489.

[3]Barrat, A., Barthélemy, M., Pastor-Satorras, R., et al., 2004. The architecture of complex weighted networks. Proc. Natl. Acad. Sci. USA, 101(11):3747-3752.

[4]Bennet, N., 2011. Infectious disease surveillance update. The Lancet Infect. Dis., 11(11):815.

[5]Cauchemez, S., Bhattarai, A., Marchbanks, T.L., et al., 2011. Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proc. Natl. Acad. Sci. USA, 108(7):2825-2830.

[6]Chase-Dunn, C., Kawano, Y., Brewer, B.D., 2000. Trade globalization since 1795: waves of integration in the world-system. Am. Sociol. Rev., 65(1):77-95.

[7]Clauset, A., Shalizi, C.R., Newman, M.E.J., 2009. Power-law distributions in empirical data. SIAM Rev., 51(4):661-703.

[8]Colizza, V., Barrat, A., Barthélemy, M., et al., 2006. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl. Acad. Sci. USA, 103(7):2015-2020.

[9]Colizza, V., Pastor-Satorras, R., Vespignani, A., 2007. Reaction-diffusion processes and metapopulation models in heterogeneous networks. Nat. Phys., 3(4):276-282.

[10]Dolgin, E., 2011. As E. coli continues to claim lives, new approaches offer hope. Nat. Med., 17(7):755.

[11]Eguíluz, V.M., Klemm, K., 2002. Epidemic threshold in structured scale-free networks. Phys. Rev. Lett., 89(10): 108701.

[12]Frank, C., Werber, D., Cramer, J.P., et al., 2011. Epidemic profile of Shiga-toxin–producing Escherichia coli O104:H4 outbreak in Germany. N. Engl. J. Med., 365(19): 1771-1780.

[13]Gang, Y., Tao, Z., Jie, W., et al., 2005. Epidemic spread in weighted scale-free networks. Chin. Phys. Lett., 22(2): 510-513.

[14]Guimerà, R., Mossam, S., Turtschi, A., et al., 2005. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. Proc. Natl. Acad. Sci. USA, 102(22):7794-7799.

[15]Hu, Y., Zhu, D., 2009. Empirical analysis of the worldwide maritime transportation network. Phys. A, 388(10):2061-2071.

[16]Khan, K., Arino, J., Hu, W., et al., 2009. Spread of a novel influenza A (H1N1) virus via global airline transportation. N. Engl. J. Med., 361(2):212-214.

[17]Kitano, H., 2004. Biological robustness. Nat. Rev. Genet., 5(11):826-837.

[18]Kuperman, M., Abramson, G., 2001. Small world effect in an epidemiological model. Phys. Rev. Lett., 86(13):2909.

[19]Kupferschmidt, K., 2011a. Scientists rush to study genome of lethal E. coli. Science, 332(6035):1249-1250.

[20]Kupferschmidt, K., 2011b. As E. coli outbreak recedes, new questions come to the fore. Science, 333(6038):27.

[21]Newman, M.E.J., 2002. Spread of epidemic disease on networks. Phys. Rev. E, 66(1):016128.

[22]Newman, M.E.J., 2003. The structure and function of complex networks. SIAM Rev., 45(2):167-256.

[23]Newman, M.E.J., 2006. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA, 103(23):8577-8582.

[24]Newman, M.E.J., Girvan, M., 2004. Finding and evaluating community structure in networks. Phys. Rev. E, 69(2): 026113.

[25]Pastor-Satorras, R., Vespignani, A., 2001. Epidemic spreading in scale-free networks. Phys. Rev. Lett., 86(14):3200-3203.

[26]Rocha, L.E.C., Liljeros, F., Holme, P., 2010. Information dynamics shape the sexual networks of Internet-mediated prostitution. Proc. Natl. Acad. Sci. USA, 107(13):5706-5711.

[27]Rosvall, M., Bergstrom, C.T., 2008. Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. USA, 105(4):1118-1123.

[28]Salathé, M., Jones, J.H., 2010. Dynamics and control of diseases in networks with community structure. PLoS Comput. Biol., 6(4):e1000736.

[29]Santos, F.C., Rodrigues, J.F., Pacheco, J.M., 2005. Epidemic spreading and cooperation dynamics on homogeneous small-world networks. Phys. Rev. E, 72(5):056128.

[30]Serrano, M.Á., Boguñá, M., 2003. Topology of the World Trade Web. Phys. Rev. E, 68(1):015101.

[31]Variano, E.A., McCoy, J.H., Lipson, H., 2004. Networks, dynamics, and modularity. Phys. Rev. Lett., 92(18): 188701.

[32]Watts, D.J., Strogatz, S.H., 1998. Collective dynamics of small-world networks. Nature, 393(6684):440-442.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE