Abstract
Dengue disease is a major public health problem in the world with a fast spreading rate. Human migration has contibute to spread of the different serotypes of dengue virus, incrementing the risk of dengue hemorrhagic fever and dengue shock syndrome. The disease immunological response is complex, with severe disease linked to the antibody-dependent enhancement. In this work we propose a system of differential equations to model the impact of human migration on the spread of two serotypes of dengue virus between two regions where only one of the serotypes was initially present. When the individuals to the other region they can contract a different serotype. Using the next generation matrix method, the basic reproductive number \(R_0\) is calculated and disease-free equilibrium stability is determined. In addition, conditions are obtained that guarantee the existence of an endemic equilibrium. These results provide conditions to predict an epidemic outbreak of dengue hemorrhagic due to a secondary infection of dengue.
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Appendices
Appendix A: Positive Invariant Set \(\Gamma\)
We will see that the vector field on the boundary of \(\Gamma\) does not point to the exterior of the region.
It can be seen that in the hyperplanes \(S_i=0\), \(I_i=0\), \(R_i=0\), \(I_{ij}=0\), \(Z{ij}=0\), \(U_i=0\), \(V_i=0, \; i,j=1,2\) the components \(S_i\), \(I_i\), \(R_i\), \(I_{ij}\), Z ij, \(U_i\), \(V_i\) of vector field are positive and therefore the vector field points to the interior of \(\Gamma\).
Now, adding components of the human and vector population in system (1) we get
Using Gronwall inequality we have
Also
This shows that the solutions of system (1) remain in the set \(\Gamma .\)
Appendix B: Existence of an Endemic Equilibrium Point
An endemic equilibrium point \(P_3\) exists, if the conditions of the following propositions are satisfied.
Proposition 7
The values \(I_{12}^*\), \(I_{21}^*\), \(I_2^*\) and \(S_2^*\) are positive if
- i):
-
\(0<I_2^*<\dfrac{(m_{21}+\mu )(\gamma _1+\mu )\left( \mu \nu (\alpha (I_1^*+I_{21}^*)+\nu )+\alpha \beta _{21}\rho _1\Omega _1\right) I_{21}^*}{\alpha \beta _{21}\gamma _2m_{21} \rho _1 \Omega _1 (I_1^*+I_{21}^*)},\) and
- ii):
-
\(\dfrac{R_{01} S_1^*}{S_{01}}-1<\frac{\alpha I_1^*}{\nu }<\frac{R_{01} S_1^*}{S_{01}}, \text{ and } 0<I_1^*<r_{01}.\)
where \(r_{01}\) is the positive root of quadratic polinomial \(N_1\) (it is described in the proof).
Proof
From (2)
where
Then \(I_{12}^*>0,\) if \(h_{02}I_2^*>-h_{01}\), which is hypothesis i).
On the other hand, since
if \(\dfrac{\beta _1\Omega _1S_1^*}{(\gamma _1+\mu )\nu }-\dfrac{\nu }{\alpha }<I_1^* <\dfrac{\beta _1\Omega _1S_1^*}{(\gamma _1+\mu )\nu }.\) Substituting \(R_{01}=\frac{\alpha \beta _1 S_{01}^* U_{01}^*}{(\gamma _1+\mu )\nu }\) in \(\dfrac{\beta _1\Omega _1S_1^*}{(\gamma _1+\mu )\nu }\), we have \(\dfrac{R_{01} S_1^*}{S_{01}}-1<\frac{\alpha I_1^*}{\nu }<\frac{R_{01} S_1^*}{S_{01}},\) which is hypothesis ii).
By (2) we have that the sign of \(I_2^*\) and \(S_2^*\) are given by the sign of \(N_1\) and \(N_2\). Since \(N_2=\alpha \beta _{21}\gamma _2 m_{21}\rho _1\Omega _1(I_1^*+I_{21}^*)(\mu +m_{12}) \Big (I_1^*\left( \alpha I_{21}^*(\gamma _1+\mu ) (\mu +m_{21}) (\beta _{12} \rho _2\Omega _2+\mu \nu ) (\beta _{21}\rho _1\Omega _1+\mu \nu )+\beta _{21} \gamma _2\mu \nu ^2 m_{21}\rho _1 \Omega _1 \right) +I_{21}^*\left( \alpha I_{21}^* (\gamma _1+\mu ) (\mu +m_{21}) (\beta _{12} \rho _2 \Omega _2+\mu \nu ) (\beta _{21} \rho _1 \Omega _1+\mu \nu )+\mu \nu ^2 (\mu (\gamma _1+\mu ) (\beta _{12} \rho _2 \Omega _2+\mu \nu )+m_{21} ((\gamma _1+\mu ) (\beta _{12} \rho _2 \Omega _2+\mu \nu ) +\beta _{21} \gamma _2 \rho _1 \Omega _1)) \right) \Big )>0,\)
then \(I_2^*>0\) and \(S_2^*>0\) if \(N_1>0.\)
Note that \(N_1=h_{12}I_1^*{^2}+h_{11}I_1^*+h_{10}\) where
Furthermore, \(N_1=h_{12}(I_1^*-r_{00})(I_1^*-r_{01}),\) where
\(r_{00}=\frac{-h_{11}+\sqrt{h_{11}^2-4h_{12}h_{10}}}{2h_{12}}\) and \(r_{01}=\frac{-h_{11}-\sqrt{h_{11}^2-4h_{12}h_{10}}}{2h_{12}}.\)
Since
then \(N_1>0\) if \(0<I_1^*<r_{01}\), which is hypothesis ii). Therefore \(I_2^*>0\) and \(S_2^*>0.\) \(\square\)
Now, we will show that the polynomial equations of system (3) can have a positive solution. The quartic and constant coefficients of system (3) are
where
And the expression \(c_0\) is given below in the proof of Proposition 9.
Since \(e_4>0\) and \(f_4<0\), in order to have a positive solution, it is enough to find conditions to have \(e_0<0\) and \(f_0>0\).
Proposition 8
\(e_0<0\) if any of the following conditions are satisfied:
-
\(b_0\ge 0\) and \(S_1<\dfrac{a_0}{a_1},\)
-
\(b_0< 0\) and \(\Big (-\dfrac{b_0}{b_1}<S_1 \text{ and } S_1<\dfrac{a_0}{a_1}\Big ) \text{ or } \Big (-\dfrac{b_0}{b_1}>S_1 \text{ and } S_1>\dfrac{a_0}{a_1}\Big ).\)
Proof
Remember that
Since \(a_0>0\) , \(a_1>0\) and \(b_1>0\) then,
and
On the other hand, \(a_0-a_1 S_1<0, \text{ if } S_1>\dfrac{a_0}{a_1},\)
and
\(\square\)
Proposition 9
\(f_0>0,\) if the following conditions are satisfied:
-
\(\gamma _1+\mu \ge \gamma _2\) and \(\alpha >-\dfrac{c_{00}}{c_{01}},\)
-
\(0<I_1<r_{02}.\)
Proof
Since
we are going to show that the expression
is positive. Since
where
If \(\gamma _1+\mu \ge \gamma _2\) then
Thus
Since \(c_2<0,\) the parabola corresponding to the expression (B1) opens downwards. Therefore
where \(r_{02}\) is the positive root of (B1) \(\square\)
Appendix C: Global Stability of the Disease-Free Equilibrium
We define the following function to demonstrate the global stability of the equilibrium point \(P_0\):
From the above, we note that \(f(x,y)\ge 0\), for (x, y) in \(\Gamma .\)
We will built a Lyapunov function following Shuai and van den Driessche technique [26, 27]. For this, we consider the following matrix.
The characteristic polynomial of \(T^{-1}F\) is,
Therefore the spectral radius of \(T^{-1}F\) is equal to
where
If \(w=(w_1,w_2,w_3,w_4)\) is a left eigenvector associated with the eigenvalue \(R_0\) of \(T^{-1}F\) then it satisfies the following equations.
We have three cases \(R_0=\sqrt{R_{01}}\) or \(R_0=\displaystyle \sqrt{R_{02}}\) with \(\displaystyle \sqrt{R_{01}} \ne \sqrt{R_{02}}\). And \(R_0=\displaystyle \sqrt{R_{01}}=\sqrt{R_{02}}\)
Case \(R_0=\sqrt{R_{01}}>\sqrt{R_{02}}.\)
From (C4), \(\Big (R_0-\dfrac{R_{01}}{R_0}\Big )=\Big (\sqrt{R_{01}}-\sqrt{R_{01}}\Big )=0.\)
From (C5), \(\Big (R_0-\dfrac{R_{02}}{R_0}\Big )=\dfrac{1}{\sqrt{R_{01}}}\Big (R_{01}-R_{02}\Big )\ne 0\) if \(R_{01} \ne R_{02}.\) Therefore \(w_4=0.\)
Taking \(w_2=1\) and substituting in (C2) and (C3) we have
Since \(x^T=\left( I_1,V_1,I_2,V_2\right) ,\) we define the Lyapunov function as
It is clear that \(Q_1(x)\ge 0\) and \(Q_1(x)=0\) if and only if \(x=0.\)
Moreover,
where \(A_1=\left( S_{01}^*-S_1\right) \frac{\alpha U_{01}^*\beta _1V_1}{R_0\nu (\gamma _1+\mu )}+\left( U_{01}^*-U_1\right) \dfrac{\alpha I_1}{\nu }\ge 0\), since \((x,y) \in \Gamma .\)
Therefore \(\dot{Q_1}<0\) if \(R_0<1.\)
Case \(R_0=\sqrt{R_{02}}>\sqrt{R_{01}}.\)
In the same way as above, we have that the Lyapunov function is
where \(A_2=\left( S_{02}^*-S_2\right) \frac{\alpha U_{02}^*\beta _2V_2}{R_0\nu (\gamma _2+\mu )}+\left( U_{02}^*-U_2\right) \dfrac{\alpha I_2}{\nu }\ge 0\), since \((x,y) \in \Gamma .\)
Case \(R_0=\sqrt{R_{01}}=\sqrt{R_{02}}.\)
The left eigenvector associated with the eigenvalue \(R_0\) of \(T^{-1}F\) is
From above we have that
is a Lyapunov function. Moreover \(\dot{Q}<0\) since \((x,y) \in \Gamma .\)
Therefore \(P_0\) is globally asymptotically stable if \(R_0<1.\)
Appendix D: Stability at \(P_1\) or \(P_2\)
The characteristic polynomial of Jacobian matrix evaluated at the equilibrium point \(P_{1}\) is
Where
Note that \(q_1, q_2, q_3, q_4 \in \mathbb {R}^{+}\) and
Using Mathematica we obtain
which is always positive.
\(D_4=q_4D_3>0.\)
By the Routh-Hurwitz criterion [30], we obtain that all roots of \(L^4+q_1 L^3 +q_2 L^2 +q_3 L +q_4\) have negative real part. On the other hand
if
By the Routh-Hurwitz criterion, we obtain that all roots of quadratic polynomial have negative real part.
Therefore, if \(R_1\left( \frac{S_{11}^*}{S_{01}^*}+\frac{\rho _1\beta _{21}Z_{121}^*}{\beta _1S_{01}^*}\right) <1\) then \(P_1\) is locally asymptotically stable.
In the same way, we have that if \(R_2\left( \frac{S_{22}^*}{S_{02}^*}+\frac{\rho _2\beta _{12}Z_{212}^*}{\beta _2 S_{02}^*} \right) <1\) then \(P_2\) is locally asymptotically stable at \(\Gamma .\)
Appendix E: Expressions for \(F_1\) and \(F_2\)
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Vinagre, M.R., Blé, G. & Esteva, L. Dynamical Analysis of a Model for Secondary Infection of the Dengue. Differ Equ Dyn Syst (2023). https://doi.org/10.1007/s12591-022-00628-5
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DOI: https://doi.org/10.1007/s12591-022-00628-5