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

Noémi Nagy; Péter L. Simon

doi:10.3934/dcdsb.2019174

Article Contents

2020, Volume 25, Issue 1: 99-115. Doi: 10.3934/dcdsb.2019174

This issue Previous Article Qualitative analysis on an SIS epidemic reaction-diffusion model with mass action infection mechanism and spontaneous infection in a heterogeneous environment Next Article Stochastic partial differential equation models for spatially dependent predator-prey equations

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

Noémi Nagy^, and
Péter L. Simon

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
^* Corresponding author: Noémi Nagy

Received: October 31, 2018

Early access: July 2019

Published: January 2020

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Abstract

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.

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Mathematics Subject Classification: Primary: 34C23, 34D23, 92C42.

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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 $

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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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