Abstract
To describe the impact of population mobility between different cities on the spread of infectious disease, a new infectious disease complex dynamical model is proposed. Moreover, we obtain the basic regeneration number of the model based on applied spectral analysis. And the disease-free equilibrium points and local equilibrium points of the model are discussed, and it is found that two kind equilibrium points are globally asymptotically stable. In addition, the final scale of the presented model is analyzed and an expression for the final scale is obtained. Furthermore, we analyze the impact of population mobility on the spread of infectious diseases via numerical simulations. Our results reveal that the increase of population mobility between two cities leads to more intense disease transmission. Finally, the influence of media effects on the spread of infectious diseases is investigated. It is shown that the spread of diseases is suppressed because of the increase of individual's self-isolation rate. Therefore, controlling the population mobility is an effective initiative to curb outbreaks of infectious diseases throughout the network. These results can provide a theoretical basis for preventing and controlling the spreading of infectious diseases.
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Acknowledgements
First of all, I would like to give my heartfelt thanks to all the people who have ever helped me in this paper. My sincere and hearty thanks and appreciations go firstly to my supervisor, Mrs Yang Lixin, whose suggestions and encouragement have given me much insight into these translation studies. It has been a great privilege and joy to study under his guidance and supervision. Furthermore, it is my honor to benefit from his personality and diligence, which I will treasure my whole life. My gratitude to him knows no bounds. I am also extremely grateful to all my friends and classmates who have kindly provided me assistance and companionship in the course of preparing this paper. In addition, I would like to thank the National Natural Science Foundation of China for its support. This research was supported by The National Natural Science Foundation of China under Grant No.11702195.
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This research was supported by The National Natural Science Foundation of China (11702195).
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YQ: Writing-Original Draft, Validation, Formal analysis, Visualization, Methodology. LY: Methodology, Writing-Review&Editing, Funding acquisition, Supervision, Resources. ZG: Resources, Visualization.
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Appendix A: Specific derivation of stability analysis
Appendix A: Specific derivation of stability analysis
Stability plays a crucial role in the dynamic analysis of infectious disease models. The following content is the theoretical derivation of stability analysis. Let's first derive the global stability of the disease-free equilibrium point at \(R_{0} < 1\).
Firstly, the disease-free equilibrium \(E_{0}\) of system (2) is \(\left( {S_{i,k}^{0} ,S_{{q_{i,k} }}^{0} ,0,0,0} \right)\).
Where
System (2) is rewritten in the form.
Where \(X = \left( {S_{i,k} \left( t \right),S_{{q_{i,k} }} \left( t \right)} \right)^{\prime }\)\(Z = \left( {E_{i,k} \left( t \right),I_{i,k} \left( t \right),Q_{i,k} \left( t \right)} \right)^{\prime }\).
\(U_{0} = \left( {X_{0} ,0} \right) = \left( {S_{i,k}^{0} ,S_{{q_{i,k} }}^{0} ,0,0,0} \right)\) denotes the disease-free equilibrium of system.
Then, we have the following two conditions:
\(\left( {H_{1} } \right)\) For \(\frac{dX}{{dt}} = F\left( {X,0} \right) = \left( \begin{gathered} \delta_{i,k} A_{i} + q_{1} S_{{q_{i,k} }} - \left( {q + d_{1} } \right)S_{i,k} \hfill \\ qS_{i,k} - d_{1} S_{{q_{i,k} }} - q_{1} S_{{q_{i,k} }} \hfill \\ \end{gathered} \right)\),\(x_{0} = \left( {S_{i,k}^{0} ,S_{{q_{i,k} }}^{0} ,0,0,0} \right)\) is a globally asymptomatically stable equilibrium of \(\frac{dx}{{dt}} = F\left( {x,0} \right)\). Hence,\(U_{0}\) is globally asymptomatically stable.
And \({\varvec{A}}_{21} \user2{ = - V}_{21}\), \({\mathbf{A}}_{22} {\mathbf{ = }} - {\mathbf{V}}_{22}\), \({\varvec{A}}_{31} \user2{ = } - {\varvec{V}}_{31}\), \({\varvec{A}}_{32} \user2{ = } - {\varvec{V}}_{32}\), \({\varvec{A}}_{33} \user2{ = } - {\varvec{V}}_{33}\).
Where \(\rho_{1l} = \left( {\beta_{2} + \lambda_{2} } \right)k\xi_{2} \omega_{2} \frac{{S_{1,l}^{0} }}{{N_{2} }},\rho_{2l} = \left( {\beta_{1} + \lambda_{1} } \right)k\xi_{1} \omega_{1} \frac{{S_{2,l}^{0} }}{{N_{1} }},l = 1 \cdots m.\)
Note that \({\varvec{A}}\) is an Metzler matrix,\(\user2{ - A}\) is an M-matrix, and all eigenvalues with respect to \({\varvec{A}}\) have negative real parts when \(R_{0} < 1\).We derive that \(Z\left( t \right)\) is globally asymptomatically stable, which means.
Then, the disease-free equilibrium \(E_{0}\) is globally asymptomatically stable when \(R_{0} < 1\).
Next, we discuss the global stability of the local equilibrium point at \(R_{0} { > }1\).
Proof
Consider the following Lyapunov function candidate
where \(h_{1} ,h_{2} ,h_{3} ,h_{4}\) and \(h_{5}\) are positive constants to be determined later. Differentiating the function with respect to time yields.
Substituting system (A5) into the above formula and sorted out:
where \(x_{1} = \frac{{S_{i,k} }}{{S_{i,k}^{*} }},x_{2} = \frac{{S_{{q_{i,k} }} }}{{S_{{q_{i,k} }}^{*} }},x_{3} = \frac{{E_{i,k} }}{{E_{i,k}^{*} }},x_{4} = \frac{{I_{i,k} }}{{I_{i,k}^{*} }},x_{5} = \frac{{Q_{i,k} }}{{Q_{i,k}^{*} }}\).
The above formula can be simplified
Considering \(h_{3} = 1\), by setting the coefficients of \(x_{2} ,x_{3} ,x_{4} ,x_{5}\) to 0 and solving for \(h_{1} ,h_{2} ,h_{4}\) yields.
\(h_{1} = {{\left[ {\left( {q + d_{1} } \right) + a} \right]} \mathord{\left/ {\vphantom {{\left[ {\left( {q + d_{1} } \right) + a} \right]} q}} \right. \kern-0pt} q}.\)\(h_{2} = {{\left[ {\alpha_{i}^{2} \varepsilon_{i} + \alpha_{i}^{1} \left( {d_{1} + d_{2} + \alpha_{i}^{2} + \gamma_{i}^{2} } \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {\alpha_{i}^{2} \varepsilon_{i} + \alpha_{i}^{1} \left( {d_{1} + d_{2} + \alpha_{i}^{2} + \gamma_{i}^{2} } \right)} \right]} {\left[ {\alpha_{i}^{2} \left( {d_{1} + \varepsilon_{i} + \alpha_{i}^{1} } \right)} \right]}}} \right. \kern-0pt} {\left[ {\alpha_{i}^{2} \left( {d_{1} + \varepsilon_{i} + \alpha_{i}^{1} } \right)} \right]}}\).\(h_{4} = {{\left[ {\left( {d_{1} + d_{2} + \alpha_{i}^{2} + \gamma_{i}^{2} } \right)} \right]} \mathord{\left/ {\vphantom {{\left[ {\left( {d_{1} + d_{2} + \alpha_{i}^{2} + \gamma_{i}^{2} } \right)} \right]} {\alpha_{i}^{2} }}} \right. \kern-0pt} {\alpha_{i}^{2} }}.\)\(a = \left( {\beta_{i} + \lambda_{i} } \right)k\left( {1 - \xi_{i} } \right)\theta_{i} + \left( {\beta_{{i^{\prime}}} + \lambda_{{i^{\prime}}} } \right)\left( {\frac{{I_{{i^{\prime}}} + E_{{i^{\prime}}} }}{{N_{{i^{\prime}}} }}} \right)k\xi_{i} \omega_{i}\).
Using the arithmetic–geometric means inequality, it is found that \(L\) is less or equal to zero with equality only if \(x_{1} = 1,x_{2} = 1,x_{3} = 1,x_{4} = 1,x_{5} = 1\). By LaSalle’s invariance principle, we know the largest invariant set in \(\Omega\) is reduced to the endemic equilibrium \(E^{*}\), and the largest invariant set contained in
Then, we conclude that the endemic equilibrium is globally asymptotically stable in \(\Omega\).
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Qin, Y., Yang, L. & Gu, Z. Dynamics behavior of a novel infectious disease model considering population mobility on complex network. Int. J. Dynam. Control (2024). https://doi.org/10.1007/s40435-023-01371-7
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DOI: https://doi.org/10.1007/s40435-023-01371-7