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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

[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.