Abstract
We propose and analyze a mathematical model of a vector-borne disease that includes vector feeding preference for carrier hosts and intrinsic incubation in hosts. Analysis of the model reveals the following novel results. We show theoretically and numerically that vector feeding preference for carrier hosts plays an important role for the existence of both the endemic equilibria and backward bifurcation when the basic reproduction number \({\mathcal {R}}_0\) is less than one. Moreover, by increasing the vector feeding preference value, backward bifurcation is eliminated and endemic equilibria for hosts and vectors are diminished. Therefore, the vector protects itself and this benefits the host. As an example of these phenomena, we present a case of Andean cutaneous leishmaniasis in Peru. We use parameter values from previous studies, primarily from Peru to introduce bifurcation diagrams and compute global sensitivity of \({\mathcal {R}}_0\) in order to quantify and understand the effects of the important parameters of our model. Global sensitivity analysis via partial rank correlation coefficient shows that \({\mathcal {R}}_0\) is highly sensitive to both sandflies feeding preference and mortality rate of sandflies.
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Acknowledgements
Rocío Marilyn Caja Rivera acknowledges fruitful conversations to Dr. Linda Allen (TEXAS TECH UNIVERSITY), Dr. Abraham Cáceres (UNMSM-PERU), Dr. Sergio Ibañez (INECOL-MEXICO) and she expresses gratefulness to anonymous reviewers for careful reading and valuable comments to this research.
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Appendices
Appendix 1
Proof of Theorem 1
1.a)
As \(f(\lambda _h)\) in Eq. (10) is a quadratic function with \(A>0\), it follows that the minimum of f occurs at \(\hat{\lambda }_h= -\frac{B}{2A}>0\) with \(f(\hat{\lambda _h})=-\frac{B^{2}-4AC}{4A}\). Thus if \(B^2-4AC=0\), then system (1) has a unique endemic equilibrium.
1.b)
As \(f(\lambda _h)\) in Eq. (10) is a quadratic function with \(B<0\), \(A>0\), \(C>0\), \(B^{2}-4AC>0\), it follows that the minimum of f occurs at \(\hat{\lambda }_h= -\frac{B}{2A}>0\) with \(f(\hat{\lambda _h})=-\frac{B^{2}-4AC}{4A}\). Thus if \(B^{2}-4AC>0\), then system (1) has two endemic equilibria.
1.c)
For \(\alpha _v<\alpha _v^{*}\) and \({\mathcal {R}}_{0}<\sqrt{H}<1\). By hypothesis, \(A>0\), \(B>0\) and \(C>0\). Then, Eq. (10) does not have any positive root. Thus, conclusion 1.c) holds.
1.d)
For \(\alpha _v<\alpha _v^{*}\) and \({\mathcal {R}}_{0} \ge 1\) we get \(B<0\) and \(C\le 0\). Then conclusion 1.d holds.
2.a)
For \(\alpha _v \ge \alpha _v^{*}\) and \({\mathcal {R}}_{0} >1\) we get \(C<0\). Then system (1) has a unique endemic equilibrium.
2.b)
For \(\alpha _v \ge \alpha _v^{*}\) and \({\mathcal {R}}_{0} \le 1\) we get \(C\ge 0\) and \(H>{\mathcal {R}}_{0}^2\). Then system (1) has no endemic equilibrium. \(\square \)
Appendix 2
Proof of Theorem 2
The Jacobian matrix of system (1), computed at \(E_{0}\) for \(b_1^*\), is given by :
The characteristic polynomial of the Jacobian matrix is:
where
We replace the value of \(b_1^*\) in \(d_3\), and we get
The Jacobian matrix admits a zero eigenvalue and the other eigenvalues are real and negative. Thus, the disease-free equilibrium \(E_0\) is a non-hyperbolic equilibrium and assumption (A1) of Theorem (Castillo-Chavez 2004) is demonstrated. We indicate by \(v=(v_1, v_2, v_3,v_4,v_5)\) and \(w=(w_1,w_2,w_3,w_4,w_5)^T\), a right and a left eigenvector associated with the zero eigenvalue, respectively, such that their dot product is one \(v.w=1\). Multiplying vJ and Jw and setting each of them equal to zero yields:
then,
where
The functions \(f_k\), \(k=1,\ldots ,5\) are the right side of the differential equations in (1a)-(1e). We define two quantities important for verification of the subcritical bifurcation
It can be checked that:
Accordingly to the coefficients a and b described in Theorem 4.1 of (Castillo-Chavez 2004), it follows:
and
Then, a is positive when \(\alpha _v<\frac{\mu _v((\mu _h+\delta _h)(\mu _h+\sigma _h)+\mu _h\omega _h)}{\mu _h\sigma _h(2\mu _v+b_2\beta _2)} =\alpha _v^*\). Consequently, system (1) shows backward bifurcation at \({\mathcal {R}}_0\) when \(\alpha _v< \alpha _v^*\).
On the other hand, a is always negative when
Therefore, system (1) exhibits a forward bifurcation at \({\mathcal {R}}_{0}=1\) when \(\alpha _v>\alpha _v^*\). \(\square \)
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Caja Rivera, R., Barradas, I. Vector Preference Annihilates Backward Bifurcation and Reduces Endemicity. Bull Math Biol 81, 4447–4469 (2019). https://doi.org/10.1007/s11538-018-00561-1
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DOI: https://doi.org/10.1007/s11538-018-00561-1