异质群体中SIR型传染病爆发流行的标度律
A Scaling Law of the SIR Outbreak Prevalence in Heterogeneous Medias
DOI: 10.12677/AAM.2016.54090, PDF, HTML, XML, 下载: 1,792  浏览: 2,914  国家自然科学基金支持
作者: 彭为花, 王家赠*:北京工商大学,理学院数学系,北京
关键词: SIR型传染病键逾渗概率母函数标度律Susceptible-Infectious-Removed Bond Percolation Probability Generating Function Scaling Law
摘要: 本文主要研究异质群体中SIR型传染病的爆发流行规模对于传播速率的标度律。这里的标度律计算是基于键逾渗的方法,它是通过一个自组织方程得到的。在爆发阈值附近,如果底层结构(用度分布来刻画)不是非常异质的话,不同底层结构下的标度律系数是等于1的。但是,对于无标度的网络,在α>4时,此时的标度律系数也是1;但当3<α<4和2<α<3时,标度律系数是大于1的且依赖于参数α的取值,即
Abstract: In this paper, we study the scaling law of the outbreak prevalence of susceptible-infectious-re- moved epidemic on the transmissibility. The scale of prevalence is calculated by bond percolation method, which is got through a self-consistent equation. Near the threshold region, we find that the scaling law coefficients of different media structures, which were represented by degree distributions, are equal to unity if the media is not so heterogeneous. For scale free networks, we find that when α>4, the scaling law coefficient is 1; when 2<α<3, the scaling law coefficient is ; when 2<α<3 , the corresponding scaling law is , which are bigger than one and depending on the parameter α .
文章引用:彭为花, 王家赠. 异质群体中SIR型传染病爆发流行的标度律[J]. 应用数学进展, 2016, 5(4): 783-789. http://dx.doi.org/10.12677/AAM.2016.54090

参考文献

[1] Meshal, R.A. (1991) Infectious Diseases of Humans. Oxford University Press, Location.
[2] Woolhouse, M.E., Dye, C., Etard, J.F., et al. (1997) Heterogeneities in the Transmission of Infectious Agents: Implications for the Design of Control Programs. Proceedings of the National Academy of Sciences of the United States of America, 94, 338-342.
https://doi.org/10.1073/pnas.94.1.338
[3] CDC (2001) HIV Prevalence Trends in Selected Populations in the United States: Results from National Serosurveillance, 1993-1997. 1-51.
[4] Lajmanovich, A. and Yorke, J.A. (1976) A Deterministic Model for Gonorrhea in a Nonhomogeneous Population. Mathematical Biosciences, 28, 221-236.
https://doi.org/10.1016/0025-5564(76)90125-5
[5] Hethcote, H.W. and Yorke, J.A. (1984) Gonorrhea Transmission Dynamics and Control. Springer-Verlag, Location.
https://doi.org/10.1007/978-3-662-07544-9
[6] Pastor-Satorras, R. and Vespignani, A. (2001) Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86, 3200-3203.
https://doi.org/10.1103/PhysRevLett.86.3200
[7] Boccaletti, S., Latora, V., Moreno, Y., Chavez, M.A. and Hwang, D.U. (2006) Complex Networks: Structure and Dynamics. Physics Reports, 424, 175-308.
https://doi.org/10.1016/j.physrep.2005.10.009
[8] Dorogovtsev, S.N., Goltsev, A.V. and Mendes, J.F.F. (2007) Critical Phenomena in Complex Networks. Review of Modern Physics, 80, 1275-1335.
https://doi.org/10.1103/RevModPhys.80.1275
[9] Sander, L.M., Warren, C.P., Sokolov, I.M., Simon, C. and Koopman, J. (2002) Percolation on Heterogeneous Networks as a Model for Epidemics. Mathematical Biosciences, 180, 293-305.
https://doi.org/10.1016/S0025-5564(02)00117-7
[10] Meyers, L. (2007) Contact Network Epidemiology: Bond Percolation Applied to Infectious Disease Prediction and Control. Bulletin of the American Mathematical Society, 44, 63-87.
[11] Newman, M.E. (2002) Spread of Epidemic Disease on Networks. Physical Review E, 66, 016128.
https://doi.org/10.1103/PhysRevE.66.016128
[12] Durrett, R. (2006) Random Graph Dynamics (Cambridge Series in Statistical and Probabilistic Mathematics). Cambridge University Press.
https://doi.org/10.1017/CBO9780511546594
[13] Volz, E. (2008) Sir Dynamics in Random Networks with Heterogeneous Connectivity. Journal of Mathematical Biology, 56, 293-310.
https://doi.org/10.1007/s00285-007-0116-4
[14] Cohen, R., Benavraham, D. and Havlin, S. (2002) Percolation Critical Exponents in Scale-Free Networks. Physical Review E, 66, 1-20.
https://doi.org/10.1103/physreve.66.036113