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Theory and Modern Applications

Figure 1 | Advances in Difference Equations

Figure 1

From: Dynamical analysis of a stochastic SIS epidemic model with nonlinear incidence rate and double epidemic hypothesis

Figure 1

Time evolutions of the deterministic \(\pmb{SIS}\) system with parameters \(\pmb{A=1}\) , \(\pmb{d=0.1}\) , \(\pmb{\beta_{1}=1.2}\) , \(\pmb{\beta_{2}=1.5}\) , \(\pmb{a_{1}=1}\) , \(\pmb{a_{2}=1.5}\) , \(\pmb{b_{1}=2}\) , \(\pmb{b_{2}=1}\) , \(\pmb{\alpha_{1}=0.2}\) , \(\pmb{\alpha_{2}=0.4}\) . (a) Time series for \(S(t)\), \(I_{1}(t)\), \(I_{2}(t)\) with parameters \(r_{1}=0.9\), \(r_{2}=0.9\), \(R_{1}=0.9091\), \(R_{2}=0.6696\). (b) Time series for \(S(t)\), \(I_{1}(t)\), \(I_{2}(t)\) with parameters \(r_{1}=0.3\), \(r_{2}=0.9\), \(R_{1}=1.8182\), \(R_{2}=0.6696\). (c) Time series for \(S(t)\), \(I_{1}(t)\), \(I_{2}(t)\) with parameters \(r_{1}=0.9\), \(r_{2}=0.3\), \(R_{1}=0.9091\), \(R_{2}=1.1719\). (d) Time series for \(S(t)\), \(I_{1}(t)\), \(I_{2}(t)\) with parameters \(r_{1}=0.3\), \(r_{2}=0.3\), \(R_{1}=1.8182\), \(R_{2}=1.1719\).

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