Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

doi:10.1088/1674-1056/20/9/090503

中国物理B ›› 2011, Vol. 20 ›› Issue (9): 90503-090503.doi: 10.1088/1674-1056/20/9/090503

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

张达敏¹, 龚光武¹, 蔡绍洪², 郭长睿²

(1)School of Computer Science and Information, Guizhou University, Guiyang 550025, China; (2)School of Informatics, Guizhou College of Finance and Economics, Guiyang 550004, China

收稿日期:2010-10-11 修回日期:2011-03-30 出版日期:2011-09-15 发布日期:2011-09-15

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

Cai Shao-Hong(蔡绍洪)^a)†, Zhang Da-Min(张达敏)^b), Gong Guang-Wu(龚光武)^b), and Guo Chang-Rui(郭长睿)^a)

^a School of Informatics, Guizhou College of Finance and Economics, Guiyang 550004, China; ^b School of Computer Science and Information, Guizhou University, Guiyang 550025, China

Received:2010-10-11 Revised:2011-03-30 Online:2011-09-15 Published:2011-09-15

摘要/Abstract

摘要： Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.

关键词: scale-free networks, systemic inflammatory response syndrome model, analog simulation

Abstract: Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.

Key words: scale-free networks, systemic inflammatory response syndrome model, analog simulation

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

05.10.-a (Computational methods in statistical physics and nonlinear dynamics) 02.60.Cb (Numerical simulation; solution of equations)

蔡绍洪, 张达敏, 龚光武, 郭长睿. Integrated systemic inflammatory response syndrome epidemic model in scale-free networks[J]. 中国物理B, 2011, 20(9): 90503-090503.

Cai Shao-Hong(蔡绍洪), Zhang Da-Min(张达敏), Gong Guang-Wu(龚光武), and Guo Chang-Rui(郭长睿) . Integrated systemic inflammatory response syndrome epidemic model in scale-free networks[J]. Chin. Phys. B, 2011, 20(9): 90503-090503.

参考文献 33

[1]	Watts D J and Strogatz S H 1998 Nature 393 440
[2]	Barab'asi A L and Albert R 1999 Science 286 509
[3]	Albert R and Barab'asi L 2002 Rev. Mod. Phys. 74 47
[4]	Jeong H, Mason S P, Barab'asi A L and Oltvai Z N 2001 Nature 411 41
[5]	Broder A, CLaffy K C and Maghoul F 2000 Proceedings of the 9th WWW Conference Computer Networks 33 309
[6]	Newman M E J 2001 Phys. Rev. E 64 016131
[7]	Wu J S and Di Z R 2004 Prog. Phys. 24 18
[8]	Fang J Q, Wang X F, Zheng Z G, Bi Q, Di Z R and Li X 2007 Prog. Phys. 27 239
[9]	Fang J Q, Wang X F, Zheng Z G, Bi Q, Di Z R and Li X 2007 Prog. Phys. 27 361
[10]	Wu L and Zhu S 2009 Eur. Phys. J. D 54 87
[11]	Wu D, Zhu S Q and Luo X Q 2009 Eur. Phys. Lett. 86 50002
[12]	Wu L, Zhu S Q and Luo X Q 2010 Chaos 20 033113
[13]	Wu L, Zhu S Q Luo X Q and Wu D 2010 Phys. Rev. E 81 061118
[14]	Wang X H, Jiao L C and Wu J S 2010 Chin. Phys. B 19 020501
[15]	Guan J Y, Wu Z X, Huang Z G and Wang Y H 2010 Chin. Phys. B 19 020203
[16]	Barth'elemy M, Barrat A, Pastor-Satorras R and Vespignani A 2004 Phys. Rev. Lett. 92 178701
[17]	Barth'elemy M, Barrat A, Pastor-Satorras R and Vespignani A 2005 J. Theor. Biol. 235 275
[18]	Kuperman M and Abramson G 2001 Phys. Rev. Lett. 86 2902
[19]	Pastor-Satorras R and Vespignani A 2001 Phys. Rev. Lett. 86 3200
[20]	Pastor-Satorras R and Vespignani A 2001 Phys. Rev. E 63 066117
[21]	Newman M E J 2002 Phys. Rev. E 66 06128
[22]	Lloyd A L and May R M 2001 Science 292 1316
[23]	Moreno Y, Pastor-Satorras R and Vespignani A 2002 Eur. Phys. J. B 26 521
[24]	Gallos L K and Argyrakis P 2003 Physica A 330 117
[25]	Madar N, Kalisky T, Cohen R, Avraham D and Havlin S 2004 Eur. Phys. J. B 38 269
[26]	Wang Y Q and Jiang G P 2010 Acta Phys. Sin. 59 6734 (in Chinese)
[27]	Wang Y Q and Jiang G P 2010 Acta Phys. Sin. 59 6725 (in Chinese)
[28]	Xia C Y, Liu Z X, Chen Z Q and Yuan Z Z 2008 Control and Decision 23 468
[29]	Liu Z R, Yan J R, Zhang J G and Wang L 2006 Chin. Phys. Lett. 23 1343
[30]	Lin G J, Jia X and Ouyang Q 2003 J. Peking Univ. (Health Sciences) 35 66
[31]	Zhang D M and Cai S H 2009 Proceedings of the 5th International Conference on Information Assurance and Security 2 556
[32]	Pastor-Satorras R and Vespignani A 2002 Phys. Rev. E 65 035108
[33]	Li G Z and Shi D H 2006 Prog. Nat. Sci. 65 508

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献 33

相关文章 10

Metrics

本文评价

推荐阅读 0

[1]	苏桂锋, 李晓温, 张小兵, 张一. Finite density scaling laws of condensation phase transition in zero-range processes on scale-free networks[J]. 中国物理B, 2020, 29(8): 88904-088904.
[2]	濮存来, 李杰, 陈荣斌, 许忠奇. Multiple-predators-based capture process on complex networks[J]. 中国物理B, 2017, 26(3): 38901-038901.
[3]	周思源, 王开, 张毅锋, 裴文江, 濮存来, 李微. Generalized minimum information path routing strategy on scale-free networks[J]. 中国物理B, 2011, 20(8): 80501-080501.
[4]	张连明, 邓晓衡, 余建平, 伍祥生. Degree and connectivity of the Internet's scale-free topology[J]. 中国物理B, 2011, 20(4): 48902-048902.
[5]	闫栋, 董明, Abdelaziz Bouras, 于随然. Poor–rich demarcation of Matthew effect on scale-free systems and its application[J]. 中国物理B, 2011, 20(4): 40205-040205.
[6]	郭进利, 郭曌华, 刘雪娇. Mobile user forecast and power-law acceleration invariance of scale-free networks[J]. 中国物理B, 2011, 20(11): 118902-118902.
[7]	刘锋, 赵寒, 李明, 任丰原, 朱衍波. Adaptive local routing strategy on a scale-free network[J]. 中国物理B, 2010, 19(4): 40513-040513.
[8]	孙巍, 窦丽华. Convergence speed of consensus problems over undirected scale-free networks[J]. 中国物理B, 2010, 19(12): 120513-120513.
[9]	祁伟, 许新建, 汪映海. Improving consensual performance of multi-agent systems in weighted scale-free networks[J]. 中国物理B, 2009, 18(10): 4217-4221.
[10]	吴俊, 谭跃进, 邓宏钟, 朱大智. Normalized entropy of rank distribution: a novel measure of heterogeneity of complex networks[J]. 中国物理B, 2007, 16(6): 1576-1580.