Physics and Electronics

Second order mean field approach of non-Markovian susceptible-infected model for complex networks

  • Ting QI ,
  • Zhaohua LIN ,
  • Mi FENG ,
  • Ming TANG
Expand
  • 1. School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
    2. School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China
    3. Department of Physics, Hong Kong Baptist University, Hong Kong 999077, China

Received date: 2020-03-03

  Online published: 2021-01-28

Abstract

The objective of this paper is to propose a mathematical theory that can describe the non-Markovian characteristics of the network spreading process, thereby establishing theoretical support for controlling the propagation of diseases or rumors in the real world. According to the second-order mean-field approximation method and the concept of idle edges, a series of partial differential equations are presented that can be used to solve the non-Markovian spreading dynamics of a susceptible-infected (SI) model in complex networks. By comparing the simulation outputs with the theoretical results, this mathematical method can accurately predict the spreading process of the SI model on complex networks. The theory, moreover, can be used to predict the average time for a single node to be infected. The correctness and accuracy of the theory is verified by experimental simulation results.

Cite this article

Ting QI , Zhaohua LIN , Mi FENG , Ming TANG . Second order mean field approach of non-Markovian susceptible-infected model for complex networks[J]. Journal of East China Normal University(Natural Science), 2021 , 2021(1) : 144 -151 . DOI: 10.3969/j.issn.1000-5641.20202s2001

References

1 PASTOR-SATORRAS R, VESPIGNANI A. Physical Review Letters, Epidemic Spreading in Scale-Free Networks. 2001, 86 (14): 3200- 3203.
2 PASTOR-SATORRAS R, CASTELLANO C, VAN MIEGHEM P, et al. Reviews of Modern Physics, Epidemic processes in complex networks. 2015, 87 (3): 925.
3 WANG W, TANG M, STANLEY H E, et al. Reports on Progress in Physics, Unification of theoretical approaches for epidemic spreading on complex networks. 2017, 80 (3): 036603.
4 BARABáSI, A L. Nature, The origin of bursts and heavy tails in human dynamics. 2005, 435 (7039): 207.
5 STOUFFER D B, MALMGREN R D, AMARAL L A N. Nature, Comment on Barabasi. 2005, 435, 207- 211.
6 VáZQUEZ A, OLIVEIRA J G, DEZS? Z, et al. Physical Review E, Modeling bursts and heavy tails in human dynamics. 2006, 73 (3): 036127.
7 KENAH E, ROBINS J M. Physical Review E, Second look at the spread of epidemics on networks. 2007, 76 (3): 036113.
8 VAZQUEZ A, RACZ B, LUKACS A, et al. Physical Review Letters, Impact of non-Poissonian activity patterns on spreading processes. 2007, 98 (15): 158702.
9 KARRER B, NEWMAN M E J. Physical Review E, Message passing approach for general epidemic models. 2010, 82 (1): 016101.
10 MIN B, GOH K I, VAZQUEZ A. Physical Review E, Spreading dynamics following bursty human activity patterns. 2015, 83 (3): 036102.
11 STARNINI M, GLEESON J P, BOGU?á M. Physical Review Letters, Equivalence between non-Markovian and Markovian dynamics in epidemic spreading processes. 2017, 118 (12): 128301.
12 CATOR E, BOVENKAMP R V D, VAN MIEGHEM P. Physical Review E, Susceptible-infected-susceptible epidemics on networks with general infection and cure times. 2013, 87 (6): 1- 7.
13 FENG M, CAI S M, TANG M, et al. Nature Communications, Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks. 2019, 10 (1): 1- 10.
14 MIN B, GOH K I, KIM I M. Europhysics Letters, Suppression of epidemic outbreaks with heavy-tailed contact dynamics. 2013, 103 (5): 50002.
15 VANMIEGHEM P, VANDEBOVENKAMP R. Physical Review Letters, Non-Markovian infection spread dramatically alters the susceptible-infected-susceptible epidemic threshold in networks. 2013, 110 (10): 108701.
16 GEORGIOU N, KISS I Z, SCALAS E. Physical Review E, Solvable non-Markovian dynamic network. 2015, 92 (4): 042801.
17 KISS I Z, R?ST G, VIZI Z. Physical Review Letters, Generalization of pairwise models to non-Markovian epidemics on networks. 2015, 115 (7): 078701.
18 SHERBORNE N, MILLER J C, BLYUSS K B, et al. Journal of Mathematical Biology, Mean-field models for non-Markovian epidemics on networks. 2018, 76 (3): 755- 778.
19 ANDERSON R M, MAY R M. Infectious Diseases of Humans: Dynamics and Control[M]. Oxford University Press, 1992.
20 VANMIEGHEM P, OMIC J, KOOIJ R. IEEE/ACM Transactions On Networking, Virus spread in networks. 2009, 17 (1): 1- 14.
21 VANMIEGHEM P. Computing, The n-intertwined SIS epidemic network model. 2011, 93 (2-4): 147- 169.
22 MCGLADE J M. Advanced Ecological Theory: Principles and Applications[M]. Hoboken: John Wiley & Sons, Ltd, 1999.
23 KEELING M J. Proceedings of the Royal Society B: Biological Sciences, The effects of local spatial structure on epidemiological invasions. 1999, 266 (1421): 859- 867.
24 ROBINSON J C, GLENDINNING P A. From Finite to Infinite Dimensional Dynamical Systems[M]. Berlin: Springer Science & Business Media, 2001.
25 EAMES K T D, KEELING M J. Proceedings of the National Academy of Sciences, Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases. 2002, 99 (20): 13330- 13335.
26 GLEESON J P. Physical Review Letters, High-accuracy approximation of Binary-State dynamics on networks. 2011, 107 (6): 068701.
27 ZACHARY W. Journal of Anthropological Research, An information flow model for conflict and fission in small groups. 1977, 33 (4): 452- 473.
Outlines

/