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January  2020, 25(1): 99-115. doi: 10.3934/dcdsb.2019174

Detailed analytic study of the compact pairwise model for SIS epidemic propagation on networks

Institute of Mathematics, Eötvös Loránd University Budapest, Numerical Analysis and Large Networks Research Group, Hungarian Academy of Sciences, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary

* Corresponding author: Noémi Nagy

Received  November 2018 Published  July 2019

The global behaviour of the compact pairwise approximation of SIS epidemic propagation on networks is studied. It is shown that the system can be reduced to two equations enabling us to carry out a detailed study of the dynamic properties of the solutions. It is proved that transcritical bifurcation occurs in the system at $ \tau = \tau _c = \frac{\gamma n}{\langle n^{2}\rangle-n} $, where $ \tau $ and $ \gamma $ are infection and recovery rates, respectively, $ n $ is the average degree of the network and $ \langle n^{2}\rangle $ is the second moment of the degree distribution. For subcritical values of $ \tau $ the disease-free steady state is stable, while for supercritical values a unique stable endemic equilibrium appears. We also prove that for subcritical values of $ \tau $ the disease-free steady state is globally stable under certain assumptions on the graph that cover a wide class of networks.

Citation: Noémi Nagy, Péter L. Simon. Detailed analytic study of the compact pairwise model for SIS epidemic propagation on networks. Discrete & Continuous Dynamical Systems - B, 2020, 25 (1) : 99-115. doi: 10.3934/dcdsb.2019174
References:
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I. Z. Kiss, J. C. Miller and P.L. Simon, Mathematics of Epidemics on Networks: From Exact to Approximate Models, Springer, 2017. doi: 10.1007/978-3-319-50806-1.  Google Scholar

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H. MatsudaN. OgitaA. Sasaki and K. Sato, Statistical mechanics of population: The lattice Lotka-Volterra model, Progress of theoretical Physics, 88 (1992), 1035-1049.  doi: 10.1143/ptp/88.6.1035.  Google Scholar

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R. Pastor-Satorras and A. Vespignani, Epidemic dynamics and endemic states in complex networks, Physical Review E, 63 (2001), 066117. doi: 10.1103/PhysRevE.63.066117.  Google Scholar

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M. A. Porter and J. P. Gleeson, Dynamical Systems on Networks: A Tutorial, A tutorial. Frontiers in Applied Dynamical Systems: Reviews and Tutorials, 4. Springer, Cham, 2016, arXiv: 1403.7663. doi: 10.1007/978-3-319-26641-1.  Google Scholar

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P. L. SimonM. Taylor and I. Z. Kiss, Exact epidemic models on graphs using graph automorphism driven lumping, Journal of Mathematical Biology, 62 (2011), 479-508.  doi: 10.1007/s00285-010-0344-x.  Google Scholar

[13]

M. TaylorP. L. SimonD. M. GreenT. House and I. Z. Kiss, From Markovian to pairwise epidemic models and the performance of moment closure approximations, Journal of Mathematical Biology, 64 (2012), 1021-1042.  doi: 10.1007/s00285-011-0443-3.  Google Scholar

show all references

References:
[1]

C. Castillo-Chavez and B. Song, Dynamical models of tuberculosis and their applications, Mathematical biosciences and engineering, 1 (2004), 361-404.  doi: 10.3934/mbe.2004.1.361.  Google Scholar

[2]

K. T. D. Eames and M. J. Keeling, Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases, Proceedings of the National Academy of Sciences, 99 (2002), 13330-13335.  doi: 10.1073/pnas.202244299.  Google Scholar

[3]

X. C. Fu, M. Small and G. R. Chen, Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis, John Wiley & Sons, 2014. doi: 10.1002/9781118762783.  Google Scholar

[4]

J. P. Gleeson, Binary-state dynamics on complex networks: Pair approximation and beyond, Physical Review X, 3 (2013), 021004. doi: 10.1103/PhysRevX.3.021004.  Google Scholar

[5]

J. K. Hale, Ordinary Differential Equations, New York-London-Sydney, 1969.  Google Scholar

[6]

T. House and M. J. Keeling, Insights from unifying modern approximations to infections on networks, Journal of The Royal Society Interface, 8 (2011), 67-73.  doi: 10.1098/rsif.2010.0179.  Google Scholar

[7]

M. J. KeelingD. A. Rand and A. J. Morris, Correlation models for childhood epidemics, Proceedings of the Royal Society of London. Series B: Biological Sciences, 264 (1997), 1149-1156.  doi: 10.1098/rspb.1997.0159.  Google Scholar

[8]

I. Z. Kiss, J. C. Miller and P.L. Simon, Mathematics of Epidemics on Networks: From Exact to Approximate Models, Springer, 2017. doi: 10.1007/978-3-319-50806-1.  Google Scholar

[9]

H. MatsudaN. OgitaA. Sasaki and K. Sato, Statistical mechanics of population: The lattice Lotka-Volterra model, Progress of theoretical Physics, 88 (1992), 1035-1049.  doi: 10.1143/ptp/88.6.1035.  Google Scholar

[10]

R. Pastor-Satorras and A. Vespignani, Epidemic dynamics and endemic states in complex networks, Physical Review E, 63 (2001), 066117. doi: 10.1103/PhysRevE.63.066117.  Google Scholar

[11]

M. A. Porter and J. P. Gleeson, Dynamical Systems on Networks: A Tutorial, A tutorial. Frontiers in Applied Dynamical Systems: Reviews and Tutorials, 4. Springer, Cham, 2016, arXiv: 1403.7663. doi: 10.1007/978-3-319-26641-1.  Google Scholar

[12]

P. L. SimonM. Taylor and I. Z. Kiss, Exact epidemic models on graphs using graph automorphism driven lumping, Journal of Mathematical Biology, 62 (2011), 479-508.  doi: 10.1007/s00285-010-0344-x.  Google Scholar

[13]

M. TaylorP. L. SimonD. M. GreenT. House and I. Z. Kiss, From Markovian to pairwise epidemic models and the performance of moment closure approximations, Journal of Mathematical Biology, 64 (2012), 1021-1042.  doi: 10.1007/s00285-011-0443-3.  Google Scholar

Figure 1.  Case of the globally stable disease-free equilibrium: Time evolution of the expected number of the infected nodes $ [I_1] $, $ [I_2] $, $ [I_3] $ of degree $ n_1 = 2 $, $ n_2 = 3 $, $ n_3 = 4 $ respectively, started with $ 900 $, $ 500 $ randomly chosen infected nodes (i.e. firstly $ 765 $, $ 90 $, $ 45 $ infected nodes of degree 2, 3, 4 respectively (continuous curves), secondly $ 425 $, $ 50 $, $ 25 $ infected nodes of degree $ 2 $, $ 3 $, $ 4 $ respectively (dashed curves)). The parameters are: $ N = 1000 $, $ N_1 = 850 $, $ N_2 = 100 $, $ N_3 = 50 $, $ \gamma = 1 $, $ \tau = 0.5 $, $ \tau_c = 0.7586 $
Figure 2.  Case of the globally stable endemic equilibrium: Time evolution of the expected number of the infected nodes $ [I_1] $, $ [I_2] $, $ [I_3] $ of degree $ n_1 = 2 $, $ n_2 = 3 $, $ n_3 = 4 $ respectively, started with $ 900 $, $ 500 $ randomly chosen infected nodes (i.e. firstly $ 765 $, $ 90 $, $ 45 $ infected nodes of degree 2, 3, 4 respectively (continuous curves), secondly $ 425 $, $ 50 $, $ 25 $ infected nodes of degree $ 2 $, $ 3 $, $ 4 $ respectively (dashed curves)). The parameters are: $ N = 1000 $, $ N_1 = 850 $, $ N_2 = 100 $, $ N_3 = 50 $, $ \gamma = 1 $, $ \tau = 1 $, $ \tau_c = 0.7586 $
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