Abstract
The spreading of an epidemic depends on the connectivity of the underlying host population. Because of the inherent difficulties in addressing such a problem, research to date on epidemics in networks has focused either on static networks, or networks with relatively few rewirings per timestep. Here we employ a simple, yet highly non-trivial, model of dynamical grouping to investigate the extent to which the underlying dynamics of tightly-knit communities can affect the resulting infection profile. Individual realizations of the spreading tend to be dominated by large peaks corresponding to infection resurgence, and a generally slow decay of the outbreak. In addition to our simulation results, we provide an analytical analysis of the run-averaged behaviour in the regime of fast grouping dynamics. We show that the true run-averaged infection profile can be closely mimicked by employing a suitably weighted static network, thereby dramatically simplifying the level of difficulty.
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References
Keeling, M.J., Rohani, P.: Modeling Infectious Diseases in Humans and Animals. Princeton University Press, New York (2007)
Pastor-Satorras, R., Vespignani, A.: Epidemic dynamics in finite size scale-free networks. Phys. Rev. E 65, 035108–035112 (2002)
Petermann, T., Rios, P.D.L.: The role of clustering and gridlike ordering in epidemic spreading. Phys. Rev. E 69, 066116 (2004)
Watts, D.J., Muhamad, R., Medina, D.C., Dodds, P.S.: Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proc. Natl. Acad. of Sci. 102, 11157–11162 (2005)
Gross, T., Dommar, C., Blasius, B.: Epidemic dynamics on an adaptive network. Phys. Rev. Lett. 96, 20–23 (2006)
Gross, T., Blasius, B.: Adaptive Coevolutionary Networks: A Review. J. R. Soc. Interface 5, 259–271 (2008)
Colizza, V., Vespignani, A.: Invasion threshold in heterogenous metapopulation networks. Phys. Rev. Lett. 99, 148701–148705 (2007)
Shaw, L.B., Schwartz, I.B.: Noise induced dynamics in adaptive networks with applications to epidemiology. E-print arXiv:0807.3455 on xxx.lanl.gov
Gueron, S., Levin, S.A.: The dynamics of group formation. Mathematical Biosciences 128, 243–246 (1995)
Gonzalez, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility pattens. Nature 453, 779–782 (2008)
Eguíluz, V.M., Zimmermann, M.G.: Transmission of information and herd behaviour: an application to financial markets. Phys. Rev. Lett. 85, 5659–5662 (2000)
McDonald, M., Suleman, O., Williams, S., Howison, S., Johnson, N.F.: Impact of unexpected events, shocking news, and rumors on foregin exchange market dynamics. Phys. Rev. E 77, 046110–046122 (2008)
Johnson, N.F., Jefferies, P., Hui, P.M.: Financial Market Complexity. Oxford University Press, Oxford (2003)
Zhao, Z., Calderon, J.P., Xu, C., Hui, P.M., Johnson, N.F.: (in preparation)
Sornette, D., Deschâtres, F., Gilbert, T., Ageon, Y.: Endogenous Versus Exogenous Shocks in Complex Networks: An Empirical Test Using Book Sale Rankings. Phys. Rev. Lett. 93, 228701 (2004)
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Zhao, Z., Zhao, G., Xu, C., Hui, P.M., Johnson, N.F. (2009). Strong Dependence of Infection Profiles on Grouping Dynamics during Epidemiological Spreading. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_96
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DOI: https://doi.org/10.1007/978-3-642-02466-5_96
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