# Some Formal Results on Positivity, Stability, and Endemic Steady-State Attainability Based on Linear Algebraic Tools for a Class of Epidemic Models with Eventual Incommensurate Delays

Authors: M. De la Sen; R. Nistal; S. Alonso-Quesada; A. Ibeas
Journal: Discrete Dynamics in Nature and Society
 (2019)

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## Abstract

A formal description of typical compartmental epidemic models obtained is presented by splitting the state into an infective substate, or infective compartment, and a noninfective substate, or noninfective compartment. A general formal study to obtain the reproduction number and discuss the positivity and stability properties of equilibrium points is proposed and formally discussed. Such a study unifies previous related research and it is based on linear algebraic tools to investigate the positivity and the stability of the linearized dynamics around the disease-free and endemic equilibrium points. To this end, the complete state vector is split into the dynamically coupled infective and noninfective compartments each one containing the corresponding state components. The study is then extended to the case of commensurate internal delays when all the delays are integer multiples of a base delay. Two auxiliary delay-free systems are defined related to the linearization processes around the equilibrium points which correspond to the zero delay, i.e., delay-free, and infinity delay cases. Those auxiliary systems are used to formulate stability and positivity properties independently of the delay sizes. Some examples are discussed to the light of the developed formal study.


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## Body

## 1. Introduction

Epidemic models have been widely studied in the last decades involving several inter-actuating subpopulations with mutual coupled dynamics. Important properties which have to be required to the epidemic models for their well-posedness are their solution positivity under any given nonnegative initial conditions as well as their stability conditions around one of the equilibrium points. Thus, relevant background literature has dealt with the study of these issues for different types of epidemic models, in both continuous and discrete-time [1–5]. A general result is that the disease-free equilibrium point is locally asymptotically stable when the endemic one is not reachable and if this last one is reachable (i.e., allocated within the closed first orthant of the real space including any trajectory solution) then it is locally stable while the disease-free equilibrium one is unstable. The reachability of the endemic equilibrium point, as well as the stability properties of the equilibrium points, is definitely linked with the value of the so-called reproduction number (sometimes referred to as “basic reproduction number” or “basic reproduction ratio” in the literature [4, 5]). If such a reproduction number is less than unity then the disease-free equilibrium point is locally asymptotically stable and the endemic one is unattainable (or unreachable) since it has some negative component for some infective subpopulation. This fact implies the global asymptotic stability to the disease-free equilibrium point provided that all the infective subpopulations are asymptotically stable. However, if the reproduction number exceeds unity, then the disease-free equilibrium point is unstable and the endemic equilibrium point is reachable (or attainable) so that the disease becomes permanent through time. The frontier between both situations typically occurs when the reproduction number is unity. See, for instance, [6–12] and some references therein. In those studies, the first orthant of the space state is an invariant subspace as a result of the nonnegativity properties of the state trajectory solutions which is also an invoked property of other biological problems related to species evolution dynamics. See, for instance, [13] and some references therein. Since the reproduction number is a very important tool to have a biological insight about if the disease is either permanent (more than one contagion per infectious individual, in average) or it extinguishes (less than one contagion per infectious individual, in average) many efforts have been done to calculate it for different epidemic models, [4, 5]. The calculation is usually performed in a case-by-case fashion providing the value of the reproduction number for each particular considered model. It turns out that there are no general analysis tools available in the background literature to discuss those properties based on general reasonable and generic assumptions independent of the particular epidemic model.Although these kinds of results are known from the background literature in a variety of epidemic models, they are revisited and presented here in a general framework as a fruitful combination of algebraic results based on positivity and stability of the linearized systems around the equilibrium points.Thus, a general technique to obtain the reproduction number and discuss the stability properties of equilibrium points for any type of compartmental epidemic models is proposed and discussed. The formal study is based on linear algebraic tools to investigate the positivity and the stability of the linearized dynamics around the disease-free and endemic equilibrium points. Consequently, our results unify previous research in this respect. To this end, the original state vector is split into the dynamically coupled infective and noninfective compartments each one containing the corresponding state components. The infective compartment includes the dynamics of all the infective subpopulations in the model, typically, the exposed, asymptomatic and symptomatic infectious and dead-infective, if considered in the model. The noninfective compartment is composed of the subpopulations being free of the disease and typically contains the dynamics of the susceptible and the recovered. For the subsequent study development, the characteristics of the state transition matrices of the linearized infective compartments around the equilibrium points of the epidemic models, which are Metzler matrices, [14, 15], as well as their stability properties and the relations of their properties to those of their opposite M – matrices, are seen to be crucial mathematical tools. The main reason is that the linearized epidemic system versions around the equilibrium points have also to possess nonnegative solution trajectories since the whole epidemic model has nonnegative solution trajectories under any given nonnegative initial conditions. In particular, such state transition matrices define the so-called next generation matrix [4] of the infective compartment. The maximum modulus of such a matrix is the relevant parameter to characterize the stability of the infective compartment and it often determines the disease reproduction number. It is also a crucial fact in the analysis the property that the transmissions matrices F of such linearized systems around the equilibrium points are nonnegative. If the Perron root of the auxiliary matrix (next generation matrix) FV-1, with -V being the disease transition matrix, i.e., its maximum real positive eigenvalue, [2, 3, 16], is less than unity then the linearized system around the disease-free equilibrium point is proved to be locally asymptotically stable and conversely. It is also proved that the reproduction number is linked to the Perron root of the above auxiliary matrix, which coincides with its spectral radius in typical examples of epidemic models. The presence of delays is an important modeling tool in epidemiology [11, 17] in cases when there are successive outbreaks and regrowths of the disease intensity caused by increase of the transmission vector numbers or external immigration to the environment under study in the model. Therefore, once the above general algebraic framework is set for delay-free models,the above study is extended to epidemic models under, in general, incommensurate (in the sense that they are not all integer multiple of the smaller, or base, delay) state point delays, [18]. The case of commensurate delays is a particular case of the above one when all the delays are integer multiples of the base delay [19–23]. Two auxiliary delay-free systems are defined related to the linearization processes around the equilibrium points which correspond to the zero delay, i.e., delay-free, and infinity delay cases. Those auxiliary systems are used to formulate stability and positivity properties, independent of the delay sizes, for the whole epidemic model. As a result of the developed formal treatment, the reproduction number in the delayed case is not directly given by the spectral radii of certain matrices related to the delay-auxiliary free and infinity delay linearized systems around the equilibrium points. Those spectral radii are, in general, lower bounds of the reproduction number. In particular, they give a guaranteed worst-case measure of guaranteed stability of the disease-free equilibrium point.The remaining main body is organized in two more sections referred to the model analysis in the delay-free case and under the presence of delays. The positivity and stability of the solutions in both cases are formally discussed in a general context, rather than for specific models, based on the linearization analysis on the infective compartment for the disease-free and endemic equilibrium points. Illustrative examples referred to the formal links of the presented mathematical framework to particular epidemic models are discussed. Two given appendices are given which contain auxiliary technical results on stability of Metzler andM-matrices and on the uniqueness of the equilibrium points.


### 1.1. Notation

p - = 1,2 , … , p, R 0 + = R + ∪ 0 = r ∈ R : r ≥ 0; R+=r∈R:r>0, R 0 - = R - ∪ 0 = r ∈ R : r ≤ 0; R-=r∈R:r<0, C 0 + = C + ∪ i ω : ω ∈ R = z ∈ C : Re ⁡ z ≥ 0; C+=z∈C:Re⁡z>0; i=-1 is the complex unity; C0,1 is the closed circle in C centred at 0 and radius one of boundary ∂C0,1 (that is, the unit circumference), C 0 - = C - ∪ i ω : ω ∈ R = z ∈ C : Re ⁡ z ≤ 0; C-=z∈C:Re⁡z<0;IfA=Aij∈Rp×q then (1) A⪰0 (in words: A is a nonnegative matrix) means that Aij≥0; ∀i∈p-, ∀j∈q-. An equivalent notation is A∈R0+p×q. Similarly, if A,B∈Rp×q then A⪰B means that A-B⪰0 and A⪯0 means that -A⪰0.(2)A≻0 (in words: A is a positive matrix) means that A≠0⪰0, that is, A⪰0 with at least one positive entry. Similarly, if A,B∈Rp×q then A≻B means that A-B≻0 and A≺0 means that -A≻0.(3)A≻≻0 (in words: A is a strictly positive matrix) means that Aij>0; ∀i∈p-, ∀j∈q-. An equivalent notation is A∈R+p×q. Similarly, if A,B∈Rp×q then A≻≻B means that A-B≻≻0 and A≺≺0 means that -A≻≻0,It turns out thatA≻≻0⇒A≻0⇒A⪰0.The logic conjunction (“And”) and logic disjunction (“Or”) of propositions are defined by the symbols “∧” and “∨,” respectively. The logic negation (“No”) is defined by the symbol “¬”. For instance, if A∈Rp×q then ¬A=Aij≻0 means that Aij<0 for some i,j∈p-×q-.For vectorsv∈Rn, similar nonnegativity, positivity, and strictly positivity notations are used, namely, v⪰0, v≻0, v≻≻0 and the corresponding nonpositivity, negativity, and strict negativity counterpart notations.I n is the identity n-matrix, and superscript T denotes a matrix transposition,ρ A = λ m a x A is the spectral radius of A∈Rp×p,A ∈ R p × p is a Metzler matrix, denoted by A∈MEp×p, if Aij≥0; ∀i,j≠i∈p-,A ∈ R p × p is an M-matrix if Aii≥0 and Aij≤0; ∀i,j≠i∈p-,F s ∞ is the H∞ - norm of complex rational matrices F:C→Cp×q which are analytic in Re⁡s>0; s∈C. The argument “s” stands for the Laplace transform argument.


## 1.1. Notation

p - = 1,2 , … , p, R 0 + = R + ∪ 0 = r ∈ R : r ≥ 0; R+=r∈R:r>0, R 0 - = R - ∪ 0 = r ∈ R : r ≤ 0; R-=r∈R:r<0, C 0 + = C + ∪ i ω : ω ∈ R = z ∈ C : Re ⁡ z ≥ 0; C+=z∈C:Re⁡z>0; i=-1 is the complex unity; C0,1 is the closed circle in C centred at 0 and radius one of boundary ∂C0,1 (that is, the unit circumference), C 0 - = C - ∪ i ω : ω ∈ R = z ∈ C : Re ⁡ z ≤ 0; C-=z∈C:Re⁡z<0;IfA=Aij∈Rp×q then (1) A⪰0 (in words: A is a nonnegative matrix) means that Aij≥0; ∀i∈p-, ∀j∈q-. An equivalent notation is A∈R0+p×q. Similarly, if A,B∈Rp×q then A⪰B means that A-B⪰0 and A⪯0 means that -A⪰0.(2)A≻0 (in words: A is a positive matrix) means that A≠0⪰0, that is, A⪰0 with at least one positive entry. Similarly, if A,B∈Rp×q then A≻B means that A-B≻0 and A≺0 means that -A≻0.(3)A≻≻0 (in words: A is a strictly positive matrix) means that Aij>0; ∀i∈p-, ∀j∈q-. An equivalent notation is A∈R+p×q. Similarly, if A,B∈Rp×q then A≻≻B means that A-B≻≻0 and A≺≺0 means that -A≻≻0,It turns out thatA≻≻0⇒A≻0⇒A⪰0.The logic conjunction (“And”) and logic disjunction (“Or”) of propositions are defined by the symbols “∧” and “∨,” respectively. The logic negation (“No”) is defined by the symbol “¬”. For instance, if A∈Rp×q then ¬A=Aij≻0 means that Aij<0 for some i,j∈p-×q-.For vectorsv∈Rn, similar nonnegativity, positivity, and strictly positivity notations are used, namely, v⪰0, v≻0, v≻≻0 and the corresponding nonpositivity, negativity, and strict negativity counterpart notations.I n is the identity n-matrix, and superscript T denotes a matrix transposition,ρ A = λ m a x A is the spectral radius of A∈Rp×p,A ∈ R p × p is a Metzler matrix, denoted by A∈MEp×p, if Aij≥0; ∀i,j≠i∈p-,A ∈ R p × p is an M-matrix if Aii≥0 and Aij≤0; ∀i,j≠i∈p-,F s ∞ is the H∞ - norm of complex rational matrices F:C→Cp×q which are analytic in Re⁡s>0; s∈C. The argument “s” stands for the Laplace transform argument.


## 2. Results for Delay-Free Epidemic Models

A general compartmental disease model of dimensionm+ncan be written as [1](1)x˙t=Fxt,yt-Vxt,yt=F-Vxt-fxt,yt(2)y˙t=gxt,ytfor any given z0=xT0,y0TT, where n and m are the respective dimensions of the disease and nondisease compartments of respective state vectors xt and yt, F∈Rn×n and -V∈Rn×n are, respectively, the disease transmission and state transition matrices of the linearized system around the disease-free equilibrium point, and (3)fxt,yt=F-Vxt-Fxt,yt+Vxt,yt=θztGztdescribes the higher-order contributions to the infective compartment dynamics, where zt=xTt,yTtT is the whole model state vector, θzt is the scalar positive bounded incidence rate, and Gzt=Fztzt, where Gzt∈Rn×n+m is a partitioned block matrix function of the form Gzt=GxxytGxyyt. We can also rename, by obvious reasons, the state vectors xt and yt of the disease and nondisease compartments as the infective and noninfective compartment (or substates) of the epidemic model, respectively. The following assumptions are made.Assumptions 1. Fi0,yt=Vi0,yt=0; ∀i∈n-=1,2,…,n, Fixt,yt≥0, Vixt,yt≤0 when xi=0, and ∑i=1nVixt,yt≥0 for all xt,yt⪰0.The conditions of Assumptions 1 are invoked for the nonnegativity of the state trajectory solution for initial conditions in the first orthant and for the local stability of the disease-free equilibrium point in the absence of infection, i.e., under a zero reproduction number. See [1–3] for more specific details. In [1–3], the matrices F and V are real square n-matrices defined as the following Jacobian matrices around the disease-free equilibrium point zdf=0T,ydfTT: (4)F=∂Fi0,ydf∂xj;V=∂Vi0,ydf∂xjThe following assumptions on the above Jacobian matrices are reasonable in the context of epidemic models [1]. The notations F, V stand through the manuscript for the Jacobian matrices around the disease-free equilibrium point. The notations Fe, Ve stand for their counterparts around the endemic equilibrium point xend while the notations F0, V0 stand for any generic equilibrium point z0. It is well-known in many typical existing epidemic models that zend and zdf are coincident when the reproduction number is unity.Assumptions 2. (a) The disease-free equilibrium point zdf=0T,ydfTT is unique and the noninfective substate dynamics y˙t=g0,yt is locally asymptotically stable.(b) The minus transition matrixV of the infective states of the linearized infective subsystem around the disease-free equilibrium point is a nonsingular M-matrix with V-1≻0 while the corresponding transmission matrix fulfills F⪰0.The above assumptions are very relevant from a physical point of view having in mind that-V and F are, respectively, the transition and transmission matrices of the linearized system around the disease-free equilibrium point while F-V is the matrix of the dynamics of such a system. It turns out, in practice, that F=0 if and only if the coefficient transmission rate β is zero, that is, there is no disease contagion in practice. Some conditions for the uniqueness of the disease-free equilibrium point are given in Theorem B.1 of Appendix B based on the implicit function theorem. Some conditions are also given for the uniqueness of the endemic equilibrium point in Theorem B.2 of Appendix B based on the Rouché-Frobenius test for compatibility of solutions in algebraic systems. This alternative way of proof is taken on the basis that distinct endemic equilibrium points can have distinct disease and nondisease endemic steady-state equilibrium compartments while the disease-free equilibrium has always zero infective substates. Both theorems are also valid for the case of presence of internal delays. Note that Assumption 2(b) can be stated in the following equivalent form (see Theorem A.1 of Appendix A):Assumption 3(b). The transition matrix -V of the infective state of the linearized system around the disease-free equilibrium point is a stability Metzler matrix while the corresponding transmission matrix fulfills F⪰0.Note that Assumption 3(b) is of a clear interpretation for the linearization around the disease-free equilibrium point while its equivalent form of Assumption 2 is easy to test since it is not needed to calculate the allocation of the eigenvalues of the matrix-V. Note also that F=FR0,ydf⪰0 depends on the disease-free equilibrium substate of the noninfective components and on the reproduction number R0 associated with the disease propagation. If R0=0 then F=F0,ydf=0. Also, it usually occurs that if the epidemic model has only a single constant transmission coefficient rate β, then R0=0 and F=0 if β=0. This section gives some elementary results on positivity of the solutions and stability for the linearized system around the disease-free equilibrium point in the case of absence of modelling delays. The results are relevant since if the linearized system around the disease- free equilibrium point fails to be either positive or, respectively, stable then the whole system cannot be either positive or, respectively, stable. Two basic auxiliary results to be invoked concerning certain technical relations between M-matrices and Metzler matrices and their stability properties are given in Appendix A. The following result on stability and positivity of the linearized infective substate is derived under some of the conditions of Assumptions 1-2:Theorem 1.
The following properties hold.
(i) The linearization of the epidemic model (1)-(2) around the disease-free equilibrium point has a nonnegative infective substate trajectory solution for any initial conditions x0⪰0 and y0⪰0 (i.e., the infective substate is nonnegative for all time) if and only if F-V∈MEn×n. A sufficient condition is that -V∈MEn×n and F⪰0.
(ii) A necessary condition for an epidemic model (1)-(2) to have a nonnegative solution trajectory, irrespective of the given particular initial conditions x0⪰0, y0⪰0, is that F-V∈MEn×n.
(iii) The linearized infective substate around the disease-free equilibrium point has a nonnegative solution which is uniformly bounded for all time withxLt⪰0; ∀t∈R0+ and xLt→0 exponentially as t→∞ for any given initial conditions xL0=x0⪰0, yL0=y0⪰0 if and only if any of the two equivalent conditions given below holds:
(1)F-V∈MEn×n is a stability matrix.
(2)F-V∈MEn×n is nonsingular (or, equivalently, V-F is a nonsingular M-matrix) and V-F-1≻0.
A joint sufficiency-type condition for any of the above Conditions (1)-(2) to hold is that-V∈MEn×n is nonsingular (or, equivalently, V is a nonsingular M-matrix), V-1≻0, F⪰0 and ρFV-1<1.
(iv) The linearized infective substate around the disease-free equilibrium point is nonnegative for any given initial conditionsxL0=x0⪰0, yL0=y0⪰0 and unstable if some of the conditions given below hold:
(1)F-V∈MEn×n is not a stability matrix.
(2)F-V∈MEn×n and V-F-1 either does not exist or if it exists is not positive.
(3)-V∈MEn×n and it exists as V-1 which is not positive, F⪰0 and ρFV-1>1.Proof.
The solution of the linearized infective substate around the disease-free equilibrium point is(5)xLt=eF-VtxL0=e-VtxL0+∫0teVτFxLτdτIt turns out that, for any xL0⪰0, xLt⪰0 since eF-Vt-τ≻0 for any τ≤t, t∈R0+ and F-V∈MEn×n [14], then, xLt⪰0; ∀t∈R0+ for any given xL0⪰0 if F-V∈MEn×n (first term of the solution xLt) or if -V∈MEn×n and F⪰0 (second term of the solution xLt). Then F-V∈MEn×n and -V∈MEn×n and F⪰0 are both sufficient conditions for xL0⪰0⇒xLt⪰0; ∀t∈R0+. The necessity of F-V∈MEn×n follows by contradiction. If F-V∉MEn×n then there exists at least one entry i,j∈n- such that eF-Vtij<0 for some t>0. Then, it suffices to take xLj0>0 and xLk0=0; ∀k≠j∈n- to conclude that xLit=-eF-VtijxLj0<0 so that the infective substate is not positive. Thus, F-V∈MEn×n is a necessary and sufficient condition of nonnegativity of the solution of the infective linearized substate around the disease-free equilibrium point for all time and any nonnegative initial condition. Property (i) has been proved. Property (ii) is obvious from the fact that a necessary condition for nonnegativity of any solution under arbitrary nonnegative initial conditions is that the property holds for its linearized counterpart system. Property (iii) follows since, on one hand, the given conditions of Metzler matrices guarantee the nonnegativity of the linearized solution of the infective substates for any nonnegative initial conditions and all time. And, on the other hand, the convergence to the disease-free equilibrium of such a linearized subsystem follows from the stability of the Metzler matrices under the nonsingularity and positivity of their negative M-matrices counterparts according to Theorem A.1 [(i) to (iii)] and Theorem A.2 (ii) of Appendix A. Property (iv) is a dual version for instability of the sufficiency parts of Property (iii).Note that ifF-V∈MEn×n for any given F⪰0 then -V∈MEn×n (otherwise, F-V∉MEn×n if F=0). Therefore, F-V∈MEn×n;∀F⪰0⇔-V∈MEn×n∧F⪰0. This equivalence is reasonable, in practice, since the nonnegativity of the linearized trajectory solution around the disease-free equilibrium point of the infective compartment is also needed for zero reproduction numbers or zero coefficient transmission rates. The joint sufficiency-type condition of Theorem 1 (iii) is also necessary and this condition is equivalent to the other two Conditions (1)-(2). From Theorem 1 on the linearized infective substate and some further conditions on the nonlinear contribution of the dynamics in the model (1)-(3), we can obtain some further results for the nonnegativity of the solution and the stability of the whole nonlinear model as follows.Theorem 2.
The following properties hold.
(i) The state trajectory solution of (1)-(3) is nonnegative for all time, i.e., z(t)=xTt,yTtT⪰0; ∀t∈R0+ for any given initial condition z0=xT0,yT0T⪰0 if and only if the two subsequent constraints hold:(6)∫0tfxτ,yτdτ⪯eF-Vtx0;∀t∈R0+;∫0tgxτ,yτdτ⪰-y0;∀t∈R0+.Furthermore, one has that(7)lim⁡supt→∞⁡∫0tfxτ,yτdτ-eF-Vtx0⪯0;lim⁡inft→∞⁡y0+∫0tgxτ,yτdτ⪰0.(ii) Assume, furthermore, that F-V∈MEn×n is a stability matrix or that any of the two constraints (1) or (2) of Theorem 1 (iii) holds. Then, (8)∫0∞fxτ,yτdτ=0,fxt,yt→0ast→∞provided that f:R0+n×R0+×R0+m×R0+→R0+nis everywhere uniformly continuous in its definition domain and fxt,yt⪰0; ∀t∈R0+; and fxt,yt→0 as t→∞ (but nonnecessarily ∫0∞fxτ,yτdτ=0) if f:R0+n×R0+×R0+m×R0+→R0+n is everywhere uniformly continuous in its definition domain.Proof.
Property (i) follows directly from (1)-(3). On the other hand, if F-V∈MEn×n, and fxt,yt⪰0; ∀t∈R0+ and it is everywhere uniformly continuous for all time for its arguments in the first closed orthant of Rn+m for all time; then it follows that ∫0∞fxτ,yτdτ=0, and eF-Vtx0→0 and fxt,yt→0 as t→∞. Also, if fxt,yt is everywhere continuous for all time in Rn+m and 0=limt→∞⁡eF-Vt⪰limt→∞⁡∫0tfxτ,yτdτ since F-V∈MEn×n and it is a stability matrix. Then, it follows from Barbalat’s lemma [18, 24] that fxt,yt→0 as t→∞. Property (ii) has been proved.Generally speaking, Theorem1 (iv.(3)) is also guaranteed under sufficient conditions if the involved spectral radii [15, 25, 26] are replaced by matrix norms which are their upper-bounds. Also, note that Theorem 2 (ii) has the assumption that F-V∈MEn×n is stable which is needed to guarantee that no eigenvalue crosses the imaginary axis under perturbations of F resulting in F-V to lose the stability of F-V when F=0, i.e., for F-V=-V. In the following discussions -V is the transition matrix in-between state of the linearized subsystem of the infected variables around the disease-free equilibrium point while F is the transmission matrix around such an equilibrium point.On the other hand, it turns out that the nonnegativity condition∫0tfxτ,yτdτ⪯eF-Vtx0; ∀t∈R0+ of Theorem 2 for the whole trajectory solution, subject to nonlinear dynamics, of the infective substate does not require that F-V∈MEn×n is a stability matrix or any conditions on the nonlinear term fxt,yt. Note that the condition holds directly if -fxt,yt⪰0 since then 0⪰-eF-Vtx0; ∀x0∈R0+n so that -∫0tfxτ,yτdτ⪰0⪰-eF-Vtx0. The nonnegativity of the trajectory solution can be also easily tested on the differential system (1)-(3) by ensuring that, for any zero value of a state component, its corresponding time-derivative is nonnegative. So, we have the subsequent result which is alternative to the two first constraints of Theorem 2 (i).Theorem 3.
The following properties hold.
(i) The state trajectory solution of (1)-(3) is nonnegative for all time, i.e., zt=xTt,yTtT⪰0; ∀t∈R0+ for any given initial condition z0=xT0,yT0T⪰0 if and only if, for all i∈n-, j∈m- and all t∈R0+, the following conditions hold:(9)xit=0⇒fixt,yt≤∑j≠i=1nFij-Vijxjt=∑j=1nFij-Vijxjtyjt=0⇒gjxt,yt≥0.(ii) Property (i) also implies that fi0,ydf=0; ∀i∈n-, gj0,ydf=0; ∀j∈m- if a disease-free equilibrium point zdf=0T,ydfTT exists.Proof.
It is obvious that, for any givenz0⪰0, if zit=0 for some t≥0, then z˙it≥0 from the given conditions and it cannot give ε∈R+ such that zit+ε<0 for some i∈n-. As a result, zt⪰0; ∀t∈R0+. The first part of the result has been proved. The second part is proved by contradiction. First note from the nonnegativity condition of Property (i) that -fi0,ydf≥-∑j=1nFij-Vij.0=0. If fi0,ydf=0; ∀i∈n- then Property (ii) has been proved for the infective substate. Now, assume that there exists some i∈n- such that fi0,ydf<0. Since ϑt=∑j=1nFij-Vijxjtxt=0=0, it follows that x˙ixi=0>0 so that zdf=0T,ydfTT is not an equilibrium point; hence a contradiction follows. In the same way, if gj0,ydf>0 for some j∈m- then zdf=0T,ydfTT is not an equilibrium point; hence we have again a contradiction. Property (ii) has been proved.Theorem 4.
Letg:Rn+m→Rm be everywhere continuously differentiable with a Jacobian matrix:
J g , z z 0 = J g , x z 0 ∣ J g , y z 0 at any z0=x0T,y0TT∈Rn+m with x0∈Rn and y0∈Rm, where z=xT,yTT∈Rn+m, x∈Rn, y∈Rm. Assume that the m×m matrix Jg,yz0 is nonsingular at any equilibrium point z0 of (1)-(3). Then, the following properties hold
(i) There exists an open setU of Rn containing x0 such that there exists a unique continuously differentiable function h:U→Rm such that y0=hx0 and fx,hx=0; ∀x∈U. Furthermore, any equilibrium point z0=x0T,y0TT can be expressed being dependent on the infective substate only, i.e., z0=x0T,hTx0T, while satisfying(10)y0=hx0;x0=F0hx0-V0x0-1fx0,hx0provided that F0hx0-V0x0 is nonsingular at any equilibrium point, where F0h0=F and V00=V if z0=zdf=0T,hT0T is the disease-free equilibrium point and F0hxend=Fe and V0xend=Ve if z0=zend=xendT,hTxendT is the endemic equilibrium point.
(ii) Assume, furthermore, that the transition and transmission matrices of the disease-free equilibrium point satisfy that-V∈MEn×n is a stability matrix and F⪰0. Then, (a) the disease-free equilibrium point exists and it is defined by zdf=0T,hT0T and it is unique and locally asymptotically stable if ρFV-1<1. If ρFV-1>1 then it is unstable; and
(b) the endemic equilibrium point exists (while it is unique under the sufficiency-type conditions of TheoremB.2 of Appendix B) and it is defined by zend=xendT,yendTT=xendT,hTxendT subject to(11)yend=hxend;xend=Fe-Ve-1fxend,hxendIf it exists then it is reachable if ρFV-1≥1 and fxend,hxend⪯fxdf,hxdf while it is unreachable, in the sense that zend∉R0+n+m, if ρFV-1<1 supposing that fxend,hxend≻fxdf,hxdf if zend≠zdf.Proof.
Assume thatJg,yx0,y0∈Rm×Rm is nonsingular. Then, there exits an open set U of Rn containing x0 such that there exists a unique continuously differentiable function h:U→Rm such that y0=hx0 and fx,hx=0; ∀x∈U. Let z0 be either the disease-free equilibrium point z0=zdf=0T,y0TT=0T,hT0T or the endemic one z0=zend=xendT,yendTT=xendT,hTxend. Both of them are unique in a neighbourhood centred at the respective equilibrium point, since h· is unique, while they satisfy (10) and, respectively, (11) if Jg,zzdf and, respectively, Jg,zzend are nonsingular since F0x0-V0x0 is a nonsingular matrix. Property (i) has been proved. In order to prove Property (ii), first note that -V∈MEn×n is nonsingular with V-1≻0 (since -V is stable) with F⪰0. Then, the disease-free equilibrium point exists being defined by zdf=0T,hT0T; it is unique since h· is unique and given by xdf=F-V-1f0,h0=0. Furthermore, it is locally asymptotically stable if ρFV-1<1 since then F-V∈MEn×n is a stability matrix, equivalently V-F-1≻0 so that the linearized system around the disease-free equilibrium point is locally exponentially (then asymptotically) stable. On the other hand, it is unstable if ρFV-1>1 [Theorem 1 (iii)-(iv)]. The part (a) of Property (ii) has been proved. Now let z0=zend=xendT,yendTT be the endemic equilibrium point. The following cases can occur
(1) IfρFV-1≥1, then note that(12)xend=Ve-Fe-1-fxend,hxend⪰V-F-1-fxdf,hxdf⪰0since fxend,hxend⪯0 (being a strict inequality if and only if xend≠xdf=0) and F-V∈MEn×n is stable and then nonsingular
(2) IfR0=ρFV-1<1 then V-F-1≻0, since F-V∈MEn×n, and Fe≻F. Therefore, In+V-F-1Fe-F≻0 and ρV-F-1Fe-F>0 then In+In-V-1F-1V-1Fe-F-1 exists, and(13)Ve-Fe-1=In+V-F-1Fe-F-1V-F-1=In+In-V-1F-1V-1Fe-F-1V-F-1≺V-F-1Note that xend is unreachable if xend≠xdf=0 since -fxend,hxend≺-fxdf,hxdf=0, and(14)xend=Ve-Fe-1-fxend,hxend≺In+In-V-1F-1V-1Fe-F-1×V-F-1-fxdf,hxdf=In+In-V-1F-1V-1Fe-F-1xdf=0.Then, xend⪯0. But, for the case, xend=0, the endemic equilibrium point is coincident with the disease-free one, that is, xend=xdf=0 and ρFV-1=1, while, for ρFV-1<1, xend≺0,; hence a contradiction to its reachability follows since the equilibrium point is never allocated in the open first orthant of Rn. Property (ii) has been proved.Remark 5.
It has not been proved that the endemic equilibrium point is necessarily unique, independently of the concrete epidemic model, in the whole state space. A detailed proof of the global uniqueness of the disease-free equilibrium point is given in TheoremB.1 (i) of Appendix B under the same conditions as those given in Theorem 4 (ii) since the infective equilibrium substate is unique and identically zero what guarantees also the uniqueness of the nondisease compartment via the uniqueness of the function h·. On the other hand, the endemic equilibrium point zend is unique in the whole state space if xend, i.e., its infective substate, is unique since from the uniqueness of h·, the uniqueness of the nondisease compartment and then that of the whole endemic equilibrium point are also guaranteed under some algebraic conditions given in Theorem B.2 in Appendix B.
It is well-known from the background literature on epidemic models, subject to a unique disease-free equilibrium point and a unique endemic equilibrium one, that, typically, if the reproduction numberR0<1 then the disease-free equilibrium point is asymptotically stable while the endemic one is not reachable since it has negative infective values what is incompatible with the positivity of the state trajectory solution. If R0>1 then the endemic equilibrium point is stable and the disease-free one is unstable and, if R0=1, then the infective variables of both equilibrium points are coincident. Theorem 4 describes formally via algebraic tools the reachability/unreachability of the endemic equilibrium point depending on the value of the reproduction number. This fact is illustrated by the subsequent example.Example 6.
A particular case of the SEIADR (susceptible-exposed-symptomatic infectious-asymptomatic infectious-infectious corpses-recovered) proposed in [6, 7] follows below for the case when the vaccination and treatment control parameters V0, KV, and Kξ are constant and there is no impulsive control for the infectious corpses removal control action:(15)S˙t=b1-b2+βIt+βAAt+βDDtSt+ηRt-VtE˙t=-b2+γEt+βIt+βAAt+βDDtStI˙t=-b2+α+τ0+KξIt+γpEtA˙t=-b2+τ0At+γ1-pEtD˙t=-μDt+b2It+At+αItR˙t=-b2+ηRt+τ0It+At+KξIt+VtVt=V0+KVStwhere Vt is a vaccination control of constant gain V0 plus a linear feedback term with gain KV with information of the susceptible subpopulation. There is also an eventual treatment control on the infectious subpopulation of gain Kξ. The disease-free and endemic Jacobian matrices for the joint noninfective infective dynamically coupled compartments are, respectively,(16)Adf=-b2+KV0-βSdf-βASdf-βDSdfη0-b2+γβSdfβASdfβDSdf00γp-b2+α+τ0+Kξ0000γ1-p0-b2+τ00000b2+αb2-μ0KV0τ0+Kξτ00-b2+ηwhere the infective compartment has a Jacobian matrix F-V uncoupled to the noninfective one with(17)V=b2+γ000-γpb2+α+τ0+Kξ00-γ1-p0b2+τ000-b2+α-b2μ;F=β0SdfβArSdfβDrSdf000000000000and(18)Aend=-b2+βIend+βAAend+βDDend+KV0-βSend-βASend-βDSendηβIend+βAAend+βDDend-b2+γβSendβASendβDSend00γp-b2+α+τ0+Kξ0000γ1-p0-b2+τ00000b2+αb2-μ0KV0τ0+Kξτ00-b2+ηwhere the infective compartment Jacobian matrix is Fe-Ve with(19)Ve=V=b2+γ000-γpb2+α+τ0+Kξ00-γ1-p0b2+τ000-b2+α-b2μFe=β0SendβArSendβDrSend000000000000=F-β0Sdf-SendβArSdf-SendβDrSdf-Send000000000000=F-βR0-1Send01βArβDr000000000000with R0=ρFV-1=Sdf/Send. Note that Fe⪰F if R0≤1; F⪰Fe if R0≥1; and F=Fe if R0=1 (what implies xend=xdf), and (20)f0,ydf=0;-fxend,yend=βIend+βAAend+βDDendSend000≥0irrespective that the infective variables be positive or negative since Iend, Aend, and Dend have the same sign as Eend, if Eend≠0, that is, if the endemic equilibrium point has not coincident components with the disease-free one, that is, if R0≠1, as it is deduced from the epidemic model. Note from the matrix Adf that if the linearized infective subsystem around the disease-free equilibrium is identically zero, then the characteristic equation of the linearized noninfective subsystem is given by(21)pnis=s2+2b2+KV+ηs+b2b2+η+KV=0whose roots are s1=-b2<0 and s2=-b2+KV+η<0. So, such a subsystem is asymptotically stable satisfying Assumption 2(a). As a result, the whole linearized subsystem is asymptotically stable if R0≤1.
It has been proved in Theorem4 that the disease-free equilibrium point is stable if ρFV-1≤1, the endemic one is unattainable if ρFV-1<1, and the disease-free equilibrium point is unstable if ρFV-1>1 while the endemic one is reachable if ρFV-1≥1. The next result establishes that the endemic equilibrium point is unstable when unattainable and asymptotically stable when attainable.Proposition 7.
Assume the following.
(1) The endemic and the disease-free transition matrices-Ve and -V are identical and independent of the reproduction number, and -V∈MEn×n is a stability matrix.
(2) The disease-free and endemic equilibrium points exist and they are unique for anyR0∈0,+∞.
(3) The disease-free and the endemic transmission matrices are nonnegative, i.e.,F=FR0⪰0, and Fe=FeR0=ςR0F⪰0, where R0=R0F=ρFV-1 is the reproduction number, and ςR0≥0.
(4)F-Fe=λ-1F⪰0 for some λ∈R.
(5)F≻Fe if R0>1, F≺Fe if R0<1, and F=Fe if R0=1.
Then, the endemic equilibrium point is locally asymptotically stable ifR0≥1 and unstable if R0<1.Proof.
Note that(22)F-V=In-FV-1-VSince -V is a stability matrix, the disease-free equilibrium point is locally asymptotically stable if R0=ρFV-1<1 and unstable if R0=ρFV-1>1, the critical stability case being R0=ρFV-1=1. Note also that(23)Fe-V=In-FeV-1-V=In-FV-1+F-FeV-1-V.Since F-Fe=λ-1F⪰0 with λ=λR0≥1 for R0≤1 since F⪰Fe, then ρF-Fe=λ-1ρF⪰0 and both of them are proportional matrices because of their structure, it follows that the endemic equilibrium point is unstable for R0<1 (when the disease-free is locally stable ) and locally stable for R0>1 (when the disease-free one is unstable).Note that for Example6, λR0=2-1/R0 in the proof of Proposition 7. Note that assumption 4 of Proposition 7 is very reasonable if F and Fe are related by a scalar factor which is the usual case in epidemic models. The subsequent examples show that the endemic equilibrium is unstable in the unattainable region.Example 8.
Consider the SEIADR epidemic model of Example6 with the following parameterization:b1=b2=1/25550days-1; 1/τ0=15.8days; η=2μ=0.1days-1;p=0.15; 1/γ=15.8days; 1/α=13.3days;V0=b1; KV=20b1; Kξ=α;β=0.11days-1; βA=0.05days-1; βD=0.15days-1.
Figure1 shows the maximum of the real parts of the eigenvalues of the Jacobian around the disease-free and endemic equilibrium points versus the reproduction number. In the proposed example, they coincide with the maximum eigenvalue since it is real. The reproduction number is displayed in the real axis by keeping constant all the disease and control model parameters except the coefficient transmission rates which are modified from their given basic values proportionally via the same common direct proportionality constant. Note that the reproduction number is proportional to the disease coefficient rate if all the remaining parameters are kept constant. It is seen in Figure 1 that the value R0=1 is the critical reproduction number giving the frontier between the stability (instability) of the disease-free equilibrium point together with the unattainability and instability (attainability and stability) of the endemic one.Figure 1
Maximum Eigenvalues of the Jacobians of the model of Example6.Example 9.
Consider a true mass action (i.e., the nonlinear infective terms of susceptible-infected products are normalized with the total population) variant of the SEIADR epidemic model of Examples6 and 8 with the parameterization of Example 8. Figure 3 shows a zoom of Figure 2 centred in the stability boundary examined on the maximum eigenvalue evolution of the Jacobian of the endemic equilibrium point. It is seen that the endemic equilibrium is unstable in its unattainable region under nonnegativity conditions of the solution while the disease-free one is the unique asymptotically stable attractor if the reproduction number is less than one. However, if such a reproduction number exceeds unity, then the disease-free equilibrium point is unstable while the endemic one is reachable and asymptotically stable.
A global stability theorem now follows by combining the preceding results and the analysis of Poincaré indices and the alternate stability characteristics of limit cycles surrounding singular points in any hyperplanes of the state space. It is proved, in particular, that the local asymptotic stability of the disease-free equilibrium point for a reproduction number less than one (implying also the unattainability of the endemic equilibrium point) leads to the global asymptotic stability of the whole state towards the disease-free equilibrium point. Note that Proposition7 concludes that only one of the equilibrium points is locally stable for each given value of R0. In particular the disease-free one is locally asymptotically stable if R0≤1 and the endemic one is locally asymptotically stable if R0≥1. Note also that Proposition 7 agrees with the conclusions of Examples 6–9.Figure 2
Maximum real part of the eigenvalues versus the reproduction number.Figure 3
Zoom of the maximum real part of the eigenvalues versus the reproduction number close to the stability region boundary.Theorem 10.
Under Assumptions 1-2, assume also that -V∈MEn×n, V-1≻0, F⪰0, and ρFV-1<1. Moreover, assume that (1) Jg,z0ydfT is nonsingular,
(2)gizt≥0 if yit=0 for any i∈m- and t∈R0+,
(3)fizt≤eiTF-Vxt if xit=0 for any i∈n- and t∈R0+ (it suffices that fizt≤0 if xit=0 for any i∈n-).
(i) Then, the total population, i.e., the sum of all the subpopulations, is uniformly bounded for all time. Furthermore, any trajectory solution is nonnegative and uniformly bounded for all time for any given finite initial conditionx0⪰0 and y0⪰0 while it is globally convergent at an exponential rate to the disease-free equilibrium point which is the unique reachable equilibrium point which is a globally asymptotically stable attractor.
(ii) Assume that forR0>1 the total population, i.e., the sum of all the subpopulations, is uniformly bounded for all time. Assume also that fxt,yt and gxt,yt are uniformly continuous in the first closed orthant of Rn+m. Then, all the trajectory solutions are bounded for all time while they converge asymptotically to the endemic equilibrium point at an exponential rate which is also the unique asymptotically stable attractor.Proof.
The nonnegativity of the solutions and the local asymptotic stability of the disease-free equilibrium point, which is unique, follow directly from Theorem1 (iii) and Theorem 4 (ii. (a)), since from Theorem 4 (ii. (b)), the endemic equilibrium is not reachable if ρFV-1<1. Since the unique disease-free equilibrium point is locally asymptotically stable, its Poincaré index [17] is +1 in any plane of the phase space containing the evolution of any two components of the state trajectory solution. It is well-known from the existence theorem of oscillations that any such a solution, if it exists, should be bounded. This is a consequence of the fact that the total population is bounded for all time and that all the subpopulations are nonnegative for all time what leads to the conclusion that any of them is uniformly bounded for all time for any finite initial conditions in the first closed orthant of Rn+m. Also, any such an oscillation, if it exists, should surround the equilibrium point, since the net global Poincaré index of all the reachable equilibrium points is still +1, while it should be unstable and then asymptotically vanishing to the locally asymptotically stable disease-free equilibrium point, since such a unique equilibrium point within the region defined by such a curve, is locally asymptotically stable. As a result, the disease-free equilibrium point is globally asymptotically stable for any initial condition in the first closed orthant of Rn+m. Property (i) has been proved. Now, assume that the total population is bounded for R0>1 so that the disease-free equilibrium point is unstable and the endemic one is attainable. Two cases can occur.
Case a. The endemic equilibrium point is globally asymptotically stable. Property (ii) follows directly.
Case b. The endemic equilibrium point is not globally asymptotically stable. Since the total population is bounded by hypothesis and all the subpopulations are nonnegative for all time, all the subpopulations are bounded for all time and a bounded limit cycle, if it exists, should surround the endemic point to attract any trajectories in the first orthant of the state space. This consideration follows by examining the admissible Poincaré-type combinations of allowed stable/instable combinations of configurations of singular points and limit cycles. Rewrite compactly (1) to (3) in the form z˙t=f^xt,yt, where f^xt,yt=F-V000xtyt+-fxt,ytgxt,yt. Assume that ∫0∞f^xτ,yτdτ=±∞. Then, zt→±∞ as t→∞ which is not compatible with the nonnegativity of the solution trajectories within the first orthant and their boundenness. Therefore, -∞<∫0∞f^xτ,yτdτ<+∞. Since f^x·.y· is uniformly continuous, it follows from Barbalat’s lemma, [24], that z˙t→0 as t→∞ so that zt cannot have a limit oscillation in any of its components. So, a stable limit cycle cannot surround the endemic equilibrium point if unstable or critically stable. Therefore, the endemic equilibrium point is globally asymptotically stable so that zt→zend as t→∞. Property (ii) has been proved.A very important remark is that the mathematical hypothesis of boundedness of the total population for the cases of reproduction numbers exceeding unity of Theorem10 (ii) is not strong, then fully feasibly, in practice. This consideration is based on the proved boundedness of the total population in the case that the reproduction number is less than unity implying the global asymptotic stability. In fact, it can be conjectured from simple inspection that, for any given initial conditions in the first orthant and any given time instant, the total population cannot exceed its value for the disease-free case at any time because thereof the disease mortality rates. Even if there is no infection mortality both values would be coincident.


## 3. Models with Multiple Incommensurate Point Time-Delays

A compartmental disease model of dimensionm+n with r constant, in general incommensurate, internal point delays hii∈r- satisfying 0=h0≤h1≤h2≤⋯≤hr=h≤h-≤∞, what is assumed in the sequel, can be generalized from (1)-(2) as follows: (24)x˙t=∑i=0rFixt-hi,yt-hi-Vixt-hi,yt-hi=∑i=0rFi-Vixt-hi-fxt,yt,xt-h1,yt-h1,…,xt-hr,yt-hr(25)y˙t≡gx^t,y^t=gxt,yt,xt-h1,yt-h1,…,xt-hr,yt-hrsubject to any absolutely continuous function of initial conditions φ:-h,0→Rn+m with eventual finite jumps on a subset of -h,0 of zero measure with φ0=xT0,yT0T. Any member of such a class of functions is referred to in the sequel as an admissible function of initial conditions, where n and m are the respective dimensions of the disease and nondisease compartments of respective state vectors xt and yt, and the nonlinear effects of (3) are replaced by the subsequent delayed counterpart dynamics:(26)fx^t,y^t≡fxt,yt,xt-h1,yt-h1,…,xt-hr,yt-hr=∑i=0rFi-Vixt-hi-∑i=0rFixt-hi,yt-hi-Vixt-hi,yt-hi=θz^tGz^twhere fx^t,y^t describes the dynamics of higher-order contributions to the dynamics, z^t=x^Tt,y^Tt=zTt,zTt-h1,…,zTt-hr, where zt=yTt,xTtT and θz^t is the scalar positive bounded incidence rate, and Gz^t=∑i=0rFizt-hizt-hi where Gz^t is a partitioned block matrix function of the form Gz^t=∑i=0rGxxxt-hiGxyyt-hi. For commensurate point delays, (24)-(26) remain valid under the additional constraint hi=ih1 for i∈r-. It turns out that the solution is unique on -h,∞ for any such initial conditions from the Cauchy-Peano theorem. Note that, (24)- (26) can be rewritten as follows:(27)x˙t=∑i=0rFi-Vixt-hi-fx^t,y^t=F0-V0xt+∑i=1rFi-Vixt-hi-fx^t,y^t=F-Vxt+∑i=1rFi-Vixt-hi-xt-fx^t,y^t(28)y˙t≡gx^t,y^twhere(29)fx^t,y^t=∑i=0rFi-Vixt-hi-∑i=0rFixt-hi,yt-hi-Vixt-hi,yt-hi=F0-V0xt+∑i=1rFi-Vixt-hi-∑i=0rFixt-hi,yt-hi-Vixt-hi,yt-hi=F0-V0xt+∑i=1rFi-Vixt-hi-xt-∑i=0rFixt-hi,yt-hi-Vixt-hi,yt-hiwith (30)Fj=∂Fji0,ydf∂xjt-hj;Vj=∂Vji0,ydf∂xjt-hj;j∈r-∪0Fej=∂Fejixend,yend∂xjt-hj;Vej=∂Vejixend,yend∂xjt-hj;j∈r-∪0,respectively, at the disease-free equilibrium point zdf=0T,ydfTT and at the endemic one zend=xendT,yendTT and the redefinitions:(31)F=∑i=0rFi,V=∑i=0rViThe extended versions of Assumptions 1 and 2 are kept for the delayed epidemic models as follows.Assumptions 4. ∑j=0rFij0,yt-hj=∑j=0rVij0,yt-hj=0; ∀i∈n-=1,2,…,n, Fijxt-hj,yt-hj≥0, Vijxt-hj,yt-hj≤0 when xit-hj=0, and ∑i=1n∑j=0rVijxt,yt≥0 for all xt,yt⪰0.Assumptions 5. (a) -V0∈MEn×n, V0-1≻0, Fi⪰Vi; ∀i∈r-.(b) The disease-free equilibrium pointzdf=0T,ydfTT is unique and the noninfective subsystem y˙t=g0,yt is locally asymptotically stable.Example 11.
A simple motivation of the model (24)-(26) subject to delays covers and generalizes classical models in the literature which have a physical interpretation. A well-known classical Kermack-McKendrick-based epidemic model in integral form, revisited in [27], is the subsequent one:(32)S˙t=St∫0∞A~τS˙t-τdτwhere St denotes the spatial density of susceptibles, i.e., the number of individuals per area at time t where A~τ is the expected infectivity of an individual which become infected τ unity of times ago. That model can be interpreted in differential form as the SI- epidemic model:(33)S˙t=-βStIt;I˙t=βStIt-γItby defining It=-β-1∫-∞tA~t-τS˙τdτ, subject to the special infectivity A~τ=βe-γτ, where β and γ are the coefficient transmission rate and the infection rate. A more general differential epidemic model subject to time-delays becomes:(34)S˙t=-β∑i=0rSt-hiIt-hi;I˙t=β∑i=0rSt-hiIt-hi-γ∑i=0rIt-hiwhich corresponds to that in the integral form:(35)S˙t=∑i=0rSt-hi∫0∞A~τS˙t-hi-τdτThe physical interpretation relies on the arrival of several infectious groups at different time instants or to the combined contributions of several infective strains at different time instants both with the same coefficient rates.
Sufficient conditions for the stability of the linearized system independent of the delay sizes around the disease-free equilibrium point can be obtained for the cases when eitherV0 or V is a stability matrix. For this purpose, we first write the equations of the linearized system in two ways depending on the nonsingularity of V0 or that of Vas follows.Lemma 12.
The following properties hold.
(i) The infective linearized subsystem obtained from (24)-(25) around the disease-free equilibrium point is given by the subsequent dynamics: (36)x˙Lt=∑i=0rFi-VixLt-hifor given initial conditions xLt=xt=φt; ∀t∈-h,0, where φ:-h,0→Rn+m is an absolutely continuous function with eventual finite jumps on a subset of -h,0 of zero measure with φ0=xT0,yT0T.
(ii) IfV0 is nonsingular then (36) can be equivalently expressed as(37)x˙Lt=-V0In-V0-1F0xLt+∑i=1rFi-VixLt-hi=∑i=0rFi-VixLt+∑i=1rFi-VixLt-hi-xLt(iii) If V is nonsingular then (36) can be equivalently expressed as(38)x˙Lt=-∑i=0rViIn-∑i=0rVi-1∑i=0rFixLt+∑i=1rFi-VixLt-hi-xLt=∑i=0rFi-VixLt+∑i=1rFi-VixLt-hi-xLt(iv) If V0 and V are nonsingular then the linearized system around the disease-free equilibrium point is described indistinctly by both (37) and (38) and also by(39)x˙Lt=-V0In+V0-1∑i=1rViIn-V0In+V0-1∑i=1rVi-1∑i=0rFixLt+∑i=1rFi-VixLt-hi-xLtProof.
Eq. (36) is a linearized version of (24) around the disease-free equilibrium point. Eqs. (37) and (38) are direct from (36) if V0 and V are, respectively, nonsingular. Furthermore, if both matrices are nonsingular then V-1=In+V0-1∑i=1rVi-1V0-1 and In+V0-1∑i=1rVi-1 exists what leads to (39).Some basic results, given in Theorem1 on positivity and stability for the delay-free linearized infective subsystem around the disease-free equilibrium point, for the case of multiple point delays follows.Theorem 13.
The following properties hold under Assumptions 4-5.
(i) The linearized epidemic model (24)-(25) around the disease-free equilibrium point has a nonnegative solution of the infective substate independent of the delays for any bounded admissible function of initial conditions φt⪰0; ∀t∈-h,0 if and only if F0-V0∈MEn×n (equivalently, -V0∈MEn×n and F0⪰0) and Fi⪰Vi; ∀i∈r-. These conditions are also jointly necessary for the epidemic model (24)-(25) to have a nonnegative solution trajectory independent of the delays for any admissible function of initial conditions φt⪰0; ∀t∈-h,0.
(ii) The linearized infective substate around the disease-free equilibrium point has a nonnegative solution trajectory and it is globally exponentially stable independent of the delays for any admissible function of initial conditionsφt⪰0; ∀t∈-h,0 if the following conditions jointly hold: F0-V0∈MEn×n is a stability matrix (equivalently, it exists V0-F0-1≻0; equivalently -V0∈MEn×n, it exists V0-1≻0, F0⪰0, and ρ0∞=ρF0V0-1<1), Fi⪰Vi; ∀i∈r-, and (40)ρ-0∞=sIn+V0-F0-1∑i=1rFi-Vie-shi∞=supω∈R0+⁡iωIn+V0-F0-1∑i=1rFi-Vi2<1(iii) The linearized infective substate around the disease-free equilibrium point is positive and globally exponentially stable independent of the delays for any admissible function of initial conditions φt⪰0; ∀t∈-h,0 if and only if the following conditions jointly hold: ∑i=0rFi-Vi is a stability matrix, F0-V0∈MEn×n (equivalently, -V0∈MEn×n and F0⪰0), Fi⪰Vi; ∀i∈r-, and the matrix ∑i=0rFi-Vie-shi has no nonzero imaginary eigenvalues.
(iv) The whole linearized model around the unique disease-free equilibrium point has a nonnegative state trajectory solution for any given admissible nonnegative function of initial conditions which converges asymptotically to the unique disease-free equilibrium point.Proof.
Note that the solution of the linearized infective substate around the disease-free equilibrium point is(41)xLt=eF0-V0txL0+∑i=1r∫0teF0-V0t-τFi-VixLτ-hidτ=eF0-V0txL0+∑i=1r∫-hit-hieF0-V0t-hi-τFi-VixLτdτ=eF0-V0txL0+∑i=1r∫-hi0eF0-V0t-hi-τFi-Viφτdτ+∑i=1r∫0t-hieF0-V0t-hi-τFi-VixLτdτThe sufficiency part is direct from (41) since if F0-V0∈MEn×n and Fi⪰Vi; ∀i∈r- then xLt⪰0 for any t>0 and any given φt⪰0; ∀t∈-h,0. The necessity is proved by contradiction in two separate steps. Assume that A0=A0ij=F0-V0∉MEn×n. Then, there is some pair i,k≠i∈n-×n- such that A0ik<0. Now, take a function of initial conditions satisfying φk0>0, φi0=0; ∀i≠k∈n-, and φt=0 for t∈-h,0. Thus, x˙Li0=-A0ikφk0<0 and xLi0=0 so that xLi0+<0 and the system is not positive. On the other hand, assume that there is some i∈r- such that Vi≻Fi so that there is some pair j,k∈n-×n- such that Vijk>Fijk. Take initial conditions as follows: φt=0 for t∈-h,-hi∪-hi,0, φl-hi=0; ∀k,l≠k∈n- and φk-hi>0. Then, x˙Lj0=-Fijk-Vijkφk-hi<0 and xLj0=0 so that xLj0+<0 and the system is not positive. Therefore, Fi⪰Vi; ∀i∈r- and F0-V0∈MEn×n are necessary for positivity. The first part of Property (i) for the linearized system around the disease-free equilibrium point has been proved. The fact that the conditions are also necessary for the positivity of the whole nonlinear model are obvious in the guidelines of Theorem 1 (i) for the delay-free case since the positivity of the whole model requires that of its linearized version around the disease-free equilibrium point. This proves the second part of Property (i). Property (ii) contains conditions to guarantee the positivity (borrowed from Property (i)) and, furthermore, the global asymptotic stability is guaranteed as follows. Since the transfer matrix sIn+V0-F0-1 is stable, since F0-V0 is a stability matrix, that is det⁡sIn+V0-F0≠0 for Re⁡s≥0, then condition (40) is equivalent to (42)det⁡ιωIn+∑i=0rVi-Fie-ιωhi=det⁡ιωIn+V0-F0det⁡In-ιωIn+V0-F0-1∑i=0rVi-Fie-ιωhi≠0;∀ω∈R0+.Thus, constraint (42) is a sufficient condition for global asymptotic stability. Since the linearized system is time-invariant and globally asymptotically stable and since time-invariant time-delay systems with constant point delays can also have a finite number of characteristic zeros to the right of any vertical line in the complex plane of abscissa greater than -∞, then the solution is a function of time of exponential negative order so that one concludes that the proved global asymptotic stability is also exponential. Property (ii) has been proved. On the other hand, note that if the jointly condition that ∑i=0rFi-Vi is a stability matrix and the matrix ∑i=0rFi-Vie-shi has no nonzero imaginary eigenvalues holds then the global asymptotic stability at exponential rate is also guaranteed since ∑i=0rFi-Vie-shi has no imaginary eigenvalues and it is a stability matrix in the delay-free case. Such a jointly condition is also necessary [28] for global asymptotic stability from the continuity of the characteristic zeros with respect to the parameters. Since the linearized system is time-invariant and globally asymptotically stable and since time-invariant time-delay systems with constant point delays can also have a finite number of characteristic zeros to the right of any vertical line in the complex plane of abscissa greater than -∞, then the solution is a function of time of exponential negative order so that one concludes that the proved global asymptotic stability is also exponential. Property (iii) has been proved. Since Assumptions 4-5 hold, then both the linearized infective substate and the noninfective one around the unique disease-free equilibrium point have a nonnegative state trajectory solution which converges exponentially to the disease-free equilibrium point for any given admissible nonnegative function of initial conditions. Hence, Property (iv) is proved.Taking into account (42) in the proof of Theorem 13, one gets the subsequent result.Corollary 14.
The linearized infective substate around the disease-free equilibrium point is positive and exponentially stable independent of the set ofr commensurate delays hi=ih1; ∀i∈r- for any bounded admissible function of initial conditions φt⪰0; ∀t∈-h,0 if and only if ∑i=0rFi-Vi is a stability matrix, F0-V0∈MEn×n, Fi⪰Vi; ∀i∈r-, and(43)det⁡ιωIn+∑i=0rVi-Fizi≠0;∀ω∈R0+,∀z∈C0,1.If the set S=ω,z∈R0+×∂C0,1≠⌀ then the infective substate around the disease-free equilibrium point is not exponentially stable for the sets of commensurate delays SH=hi=-ln⁡z/ω:ω,z∈S.Taking into account the expression (36) to describe the linearized infective dynamics around the disease-free equilibrium point, it is possible to give an alternative sufficiency-type condition for global stability of the infective subsystem as the subsequent result reflects.Corollary 15.
The linearized infective substate around the disease-free equilibrium point is exponentially stable independent of the set ofr incommensurate delays hi; ∀i∈r- if (44)ρ-00=2∑i=1rFi-Vi2supω∈R+⁡iωIn-∑i=1rFi-Vi-12<1 provided that ∑i=0rFi-Vi is a stability matrix, which holds if and only if ρ00=ρ∑i=0rFi∑i=0rVi-1<1.Proof.
One gets the characteristic equation from (37) as follows:(45)detsIn-∑i=0rFi-Vi-∑i=1rFi-Vie-his-1=0fors∈CSince ∑i=0rFi-Vi is a stability matrix then detsIn-∑i=0rFi-Vi≠0 for s∈C0+ so that(46)det⁡sIn-∑i=0rFi-Videt⁡In-sIn-∑i=0rFi-Vi-1∑i=1rFi-Vie-his-1≠0;∀s∈C0+provided that sIn-∑i=0rFi-Vi-1∑i=1rFi-Vie-his-1∞<1 which is guaranteed if ρ-00<1. Since ∑i=0rFi-Vi is a stability matrix, the above test is fulfilled at the origin of the complex plane. Note also that ∑i=0rFi-Vi=∑i=0r-ViIn-∑i=0rVi-1∑i=0rFi is a stability matrix if and only if ρ00<1 since ∑i=0r-Vi is a stability matrix. Therefore, a sufficient condition to achieve the global (exponential) asymptotic stability test independent of the delays is constraint (44) since supω∈R0+⁡e-ihiω-1≤2; ∀i∈r-.It follows from Corollary15 and the “If Part” of Theorem 13 (iii) that since ∑i=0rFi-Vi is a stability matrix, constraint (44) guarantees that ∑i=0rFi-Vie-shi has no nonzero imaginary eigenvalues. Otherwise, the infective part of the linearized system around the disease-free equilibrium point could not be globally asymptotically stable.Remark 16.
The infective linearized system around the disease-free equilibrium point has two important particular cases which define auxiliary linearized systems which are useful for the local stability analysis independent of the delays, namely,
(a)auxiliary infective linearized delay-free system (AILDFS) describing the particular case hi=0; ∀i∈r-:(47)x˙Lt=∑i=0rFi-VixLtfor t>0 for admissible initial conditions φ:-h,0→R0+n with φ0=x0=x0 and h=hr;
(b)auxiliary infective linearized delayed dynamics-free system (AILDDFS) describing the particular case Fi-Vi=0; ∀i∈r-:(48)x˙Lt=F0-V0xLtfor t>0 for admissible initial conditions φ:-h,0→R0+n with φ0=x0=x0 and a given maximum delay h=hr≤+∞. This particular system can also be interpreted for the case of point initial conditions φt=0 for t∈-∞,0 and φ0=x0=x0 as defined under any amounts of delayed dynamics associated with infinity delays hi=+∞; ∀i∈r-.
Note that the disease reproduction numberR00,h1,h2,...,hr is, in general, delay-dependent with particular values R00=R00, with 0∈Rn+1, which is the reproduction number of theAILDFS, and R0∞=R00,inf, where inf∈Rn has all components equal to +∞, which is the reproduction number of theAILDDFS. Define the maximum and minimum reproduction numbers for any given set of incommensurate delays:(49)RM0=max0≤hi≤∞⁡R00,h1,h2,…,hr;Rm0=min0≤hi≤∞⁡R00,h1,h2,…,hrNote also that (50)R00,h1,h2,…,hr∈Rm0,RM0;∀hi∈R0+,∀i∈r-,R00∈Rm0,RM0,R0∞∈Rm0,RM0.where R00=maxρ00,ρ-00, R0∞=maxρ0∞,ρ-0∞. Note that ρ00 and ρ0∞ are the respective reproduction numbers of theAILDFS and theAILDDFS which are delay-free auxiliary linearized systems and the amounts ρ-00 and ρ-0∞ express the tolerance to stability independent of the delays of the linearized systems around the disease-free equilibrium point compatible with guaranteeing the stability independent of the delay sizes provided that those amounts are less than unity. Define R-0=minR00,R0∞ and note that Rm0≤R00,h1,h2,...,hr≤R-0≤RM0; ∀hi∈R0+, ∀i∈r-.
Thus, the asymptotic stability independent of the delays of the linearized system around the disease-free equilibrium point holds ifRM0≤1 and it is then guaranteed if R-0≤1. However, the above constraint has only a partial information about the stability and attainability of the endemic equilibrium point for each particular combination of delays. For, instance, we can intuitively discuss the following cases.
(1) For a set of delayshi; ∀i∈r-, it holds that R00,h1,h2,…,hr≤1<R-0≤RM0. We get the conclusion that the disease-free equilibrium point is locally asymptotically stable and the endemic is unattainable for this value of R0·. Since RM0≥R-0>1, we can say that there are combinations of delays, all of them with the same amounts of delayed dynamics, for which the disease is endemic but it also holds that there exist some particular delays for which the disease asymptotically extinguishes. On the other hand, since RM0>1, there are other sets of delays h-i; ∀i∈r- for the same matrices of dynamics such that the disease-free equilibrium point is unstable and the endemic one is stable and attainable.
(  2) For a set of delays hi; ∀i∈r-, it holds that R00,h1,h2,…,hr≤R-0<1<RM0. We get the conclusion that the disease-free equilibrium point is locally asymptotically stable and the endemic one is unattainable. However, the reproduction number range for which this holds exceeds the lower-bound given by R-0. Furthermore, there are other sets of delays h-i; ∀i∈r-, under the same amounts of delayed dynamics, such that the disease-free equilibrium point is unstable and the endemic one is stable and attainable.
(3) In the inequalities of the above cases (a) and (b), ifR-0 is replaced with R00 or R0∞, we get similar conclusions.The following three results discuss the stability and positivity independent of the delays of the linearized system around the disease-free equilibrium point if eitherR00 (i.e., the reproduction number of theAILDFS) or R0∞ (i.e., the reproduction number of theAILDDFS) or both amounts are less than unity. In the first case, the complementary sufficient-type stability condition (44) of worst-case tolerance against delayed dynamics is also invoked. In the second case the complementary worst-case tolerance condition (40) replaces to (44).Corollary 17.
If Assumption 5 holds then the following properties hold.
(i) If constraint (44) holds, then there exists a real number ε=εh1,h2,…,hr∈R0+ dependent, in general, on the delays hi≥0 for i∈r-, such that the reproduction number is given by R00,h1,h2,…,hr=1+εR00-ε, so that R00≤1⇒R00,h1,h2,…,hr≤1, and the implication also holds under strict inequalities, for any given set of delays hi; ∀i∈r-, and then Rm0≤RM0≤1. Then, the infective substate of the linearized system around the disease-free equilibrium point is globally asymptotically stable independent of the delays, with a nonnegative state trajectory solution for any given admissible nonnegative function of initial conditions, if R00<1.
(ii) TheAILDFSis globally asymptotically stable around the disease-free equilibrium point, with a nonnegative state trajectory solution for any given admissible nonnegative function of initial conditions, if and only if ρ00≤1. If ρ00<1 then, furthermore, the endemic equilibrium point is unattainable. If ρ00=1 then the disease-free and the endemic equilibrium points coincide. If ρ00>1 then the disease-free equilibrium point is unstable.Proof.
The positivity and stability are guaranteed for the linearized infective substate from Theorem13 (ii) and Lemma 12 (ii). Now assume that ρ00≤1 and (44) holds, that is, if ρ-00<1, both combined guarantee that R00<1. Note that the critical stability of the infective linearized system around the disease-free equilibrium point is given by ρ00=1. It is now proved that R00≤1⇒R00,h1,h2,…,hr≤1 for any set of incommensurate delays hi; ∀i∈r- if (44) holds. Assume, on the contrary, that R00≤1⇒R00,h1,h2,…,hr>1 for some set of delays hi; ∀i∈r-, so that RM0>1. Then, the linearized infective substate around the disease-free equilibrium point is unstable for at least one set of delays, a contradiction to Theorem 13 (ii) from the joint conditions ρ00<1 and (44). Hence, R00≤1⇒R00,h1,h2,…,hr≤1. Since both R00,h1,h2,…,hr and R00 are positive real constants which depend on the model parameterization then(51)R00,h1,h2,…,hr=1+εR00-ε=R00-1ε+R00for some ε=εh1,h2,…,hr∈R0+ for each set of delays hi≥0; ∀i∈r-. The nonnegativity of any solution under any nonnegative admissible initial conditions follows from Assumption 5. Property (i) has been proved. Property (ii) follows since if Assumption 5 holds, then theAILDFSis globally asymptotically stable if and only if ρ00≤1 since ρ00 is the reproduction number of theAILDFS and any trajectory solution is nonnegative under nonnegative initial conditions. The other assertions in Property (ii) follow from Theorem 4 (ii) since theAILDFS is a delay-free system.Corollary 18.
Under Assumption 5, ifV=∑i=0rVi is nonsingular and (40) holds then the following properties hold.
(i)R00,h1,h2,…,hr=1+ε1R0∞-ε1 for any given set of delays hi≥0 for i∈r- and some ε1=ε1h1,h2,…,hr∈R0+. Thus, the infective substate of the linearized system around the disease-free equilibrium point is globally asymptotically stable independent of the delays with a nonnegative state trajectory solution for any given admissible nonnegative function of initial conditions if ρ0∞≤1 and (40) holds, that is, if ρ-0∞<1.
(ii) theAILDDFSis globally asymptotically stable around the disease-free equilibrium point, with a nonnegative state trajectory solution for any given admissible nonnegative function of initial conditions, if and only if ρ0∞<1. If ρ0∞<1 then the endemic equilibrium point of theAILDDFS is unattainable. If ρ0∞=1 then the disease-free and the endemic equilibrium points coincide. If ρ0∞>1 then the disease-free equilibrium point is unstable.Proof.
It follows under a similar reasoning as that in the proof of Corollary17 by using Lemma 12 (iii).Corollary 19.
If Assumption 5 holds,V is nonsingular and (40) and (44) jointly hold, then R00,h1,h2,…,hr=1+ε2ρ--ε2 for any given set of delays hi≥0 for i∈r- and some ε2=ε2h1,h2,…,hr∈R0+, where R-0=maxR00,R0∞<1, and then the infective substate of the linearized system around the disease-free equilibrium point has a globally asymptotically stable nonnegative state trajectory solution independent of the delays for any given nonnegative admissible function of initial conditions.Proof.
It follows under a similar reasoning as that in the proof of Corollary17 by taking into account Corollary 18.Remark 20.
If Assumption 5 holds, andV=∑i=0rVi and V′=∑i=1rVi=V-V0 are nonsingular then ρ-1=max⁡ρF0V0-1,ρ∑i=0rFi∑i=0rVi-1=ρ-≤1 and then the condition R00,h1,h2,…,hr≤ρ-1=ρ- for some set of delays hi≥0; ∀i∈r- if ρ-≤1 if both (40) and (44) hold included in Corollary 19. Thus, the linearized description of Lemma 12 (iv) does not add any new information to reformulate alternative conditions to those of Corollary 19.Remark 21.
Note that, since Theorem13 (ii) gives sufficient (while not necessary) stability conditions independent of the delays, it cannot be proved in Corollary 17 that R00>1⇒R00,h1,h2,…,hr>1 for some set of delays hi≥0; ∀i∈r- to conclude instability. That is, R00≤1 guarantees stability since R00,h1,h2,…,hr≤1 but the converse is not true, in general. In fact, if R00>1 then R00,h1,h2,…,hr>R00 since εR00-1>0 so that ρ00 is a lower-bound of the maximum reproduction number for any set of admissible delays. Similar considerations can be said related to Corollaries 18 and 19 which do not provide necessary conditions either, in general, having in mind Theorem 13. So, ρ0∞ is also a lower-bound of the maximum reproduction number for any set of admissible delays.Remark 22.
A case of interest might be whenFi=αiF0; i∈r-∪0 with α0=1. Then, ρ∑i=0rFi∑i=0rVi-1=1+∑i=1rαiρF0∑i=0rVi-1 in Corollaries 18 and 19. If 0<αi+1≤αi (respectively, αi+1>αi) for i∈r-1¯∪0 then the delays lose their influence, or, at least, such an influence does not increase (respectively, increases) in the transmission of the disease as the delay sizes increase. So, ρ∑i=0rFi∑i=0rVi-1≤r+1ρF0∑i=0rVi-1, respectively, ρ∑i=0rFi∑i=0rVi-1≥r+1ρF0∑i=0rVi-1.Example 23.
Consider the subsequent linearized model around the disease-free equilibrium point:(52)x˙Lt=∑i=01Fi-VixLt-hifor any delay h≥0. The nonnegativity of the solutions of the linearized system holds with -V0∈MEn×n and F0⪰0 (AILDDFS-positivity, that is, F0-V0∈MEn×n) and F1⪰V1 (these three joint conditions implyingAILDFS-positivity, that is, ∑i=01Fi-Vi∈MEn×n). If furthermore, the nonlinear contribution to the infective dynamics of the whole epidemic model fulfills -fx^t,y^t⪰0; ∀t∈R0+, independent of h, then the whole solution trajectory is nonnegative for all time independent of h. The relevant amounts to guarantee the asymptotic stability of the auxiliary delay-free systems around the disease-free equilibrium point and to keep it under a certain tolerance to the delayed dynamics amount (specified by the matrix F1-V1) independently of the delay h, via Theorem 13 and Corollaries 15 and 17, are(53)ρ0∞=ρF0V0-1;ρ-0∞=supω∈R0+⁡iωIn+V0-F0-1F1-V12;ρ00=ρF0+F1V0+V1-1;ρ-00=2F1-V12supω∈R+⁡iωIn-F1-V1-12;R-0=min⁡R00,R0∞;R00=maxρ00,ρ-00;R0∞=maxρ0∞,ρ-0∞.Note that
(1)ρ0∞≤1 if and only if theAILDDFSis locally asymptotically stable. Since the endemic equilibrium point is not attainable if ρ0∞<1 and it is confluent with the disease-free one if ρ0∞=1, the condition also implies that theAILDDFSis globally asymptotically stable.
(2)ρ0∞>1 implies that the disease-free equilibrium point is unstable and the endemic one is stable and attainable.
(3)ρ00≤1 if and only if theAILDFSis locally asymptotically stable. Since the endemic equilibrium point is not attainable if ρ00<1 and it is confluent with the disease-free one if ρ00=1, the condition also implies that theAILDFSis globally asymptotically stable.
(4)ρ00>1 implies that the disease-free equilibrium point is unstable and the endemic one is stable and attainable.
(5) IfR0∞<1, or if R00<1, then all the disease-free equilibrium points (which satisfy the respective conditions ρ-0∞<1 or ρ-00≤1) for any value of h≥0 are globally asymptotically stable. In particular, F0-V0 and F1-V1 are such that either ρ-00≤1 or ρ-0∞≤1 so that the corresponding auxiliary delay-free system around the disease-free equilibrium point is globally asymptotically stable. Under those respective conditions, all the resulting endemic equilibrium points are unattainable. They are attainable and stable if R0∞≥1 or if R00≥1.
The following result relies on sufficiency-type conditions of global asymptotic stability independent of the delays of the linearized infective substate around the disease-free equilibrium point including the case when the transmission matrices for the various delays are all identical.Theorem 24.
The following properties hold:
(i) Assume thatF0-V0 and ∑i=0rFi-Vi are stability matrices, and that(54)ρIn,In,⋯︷r,InTiωIn-F0+V0-1F1-V1,F2-V2,…,Fr-Vr<1;∀ω∈R0+.Then, the infective linearized subsystem around the disease-free equilibrium point is globally asymptotically stable independent of the delays.
(ii) IfFi=F0≻0; ∀i∈r-, -V0 and -V=-∑i=0rVi are stability matrices, ρF0V-1<1/r+1, and(55)ρIn,In,⋯︷r,InTiωIn-F0+V0-1F0-V1,F0-V2,…,F0-Vr<1;∀ω∈R0+.Then the infective linearized subsystem is globally asymptotically stable independent of the delays.Proof.
IfF0-V0 and ∑i=0rFi-Vi are stability matrices then the linearized subsystem is globally asymptotically stable for infinity and zero delays and the property also holds independent of the sizes of the delays [18] if (54) holds. Property (i) has been proved. Property (ii) follows in the same way when Fi=F0≻0; ∀i∈r- since ρF0V-1<1/r+1 guarantees that -V is a stability matrix if -V0 is a stability matrix and (55) is identical to (54) if Fi=F0≻0; ∀i∈r-.Remark 25.
Condition 2 of Theorem24 holds if -V0 is a nonsingular stability matrix (it suffices, for instance, that -V0∈MEn×n and it exists V0-1≻0, the second condition holds if and only if the Metzler matrix -V0 is a stability matrix) and ρV0-1∑i=1rVi<1 since such a condition implies that -V is a stability matrix provided that -V0 is also a stability matrix.
A related result to Theorem4 for the case of presence of delays adopts the subsequent form.Theorem 26.
Letg:Rn+m→Rm be everywhere continuously differentiable with a Jacobian matrix: Jg,zz0=Jg,xz0∣Jg,yz0 at any z0=x0T,y0TT∈Rn+m with x0∈Rn and y0∈Rm, where z=xT,yTT∈Rn+m, x∈Rn, y∈Rm. Assume that the m×m matrix Jg,yz0 is nonsingular at any equilibrium point z0of (24)-(26). Then, the following properties hold.
(i) There exists an open setU of Rn containing x0 such that there exists a unique continuously differentiable function h:U→Rm such that y0=hx0 and fx,hx=0; ∀x∈U. Also, any equilibrium point z0=x0T,y0TT can be expressed being dependent only on the infective substate, i.e., z0=x0T,hTx0T, while satisfying(56)y0=hx0;x0=∑i=0rFihx0-Vix0-1fx0,hx0provided that ∑i=0rFihx0-Vix0-1 is nonsingular at any equilibrium point z0=x0T,hTx0T, where Fih0=Fi and Viz0=Vi; ∀i∈r-∪0 if z0=zdf=0T,hT0T is the disease-free equilibrium point and Fihxend=Fie and Vixend=Vie; ∀i∈r-∪0 if z0=zend=xendT,hTxendT is the endemic equilibrium point.
(ii) Assume, furthermore, that-∑i=0rVi∈MEn×n is nonsingular with F0⪰0. Then, (a) the disease-free equilibrium point exists being defined by zdf=0T,hT0T, it is unique and locally asymptotically stable independent of the delays if R00<1 and
(b) the endemic equilibrium point exists being defined byzend=xendT,yendTT=xendT,hTxendT while it satisfies(57)yend=hxend;xend=Fe-Ve-1fxend,hxend.It is reachable independent of the delays if R00≥1 and fxend,hxend⪯fxdf,hxdf while it is unreachable, in the sense that zend∉R0+n+m, if R00<1 supposing that fxend,hxend≻fxdf,hxdf if zend≠zdf.Proof.
SinceJg,zx0,y0 is nonsingular, there exists an open set U of Rn containing x0 such that there exists a unique continuously differentiable function h:U→Rm such that y0=hx0 and fx,hx=0; ∀x∈U. Let z0 be either the disease-free equilibrium point z0=zdf=0T,y0TT or the endemic one z0=zend=xendT,yendTT. Property (i) follows if ∑i=0rFihx0-Vix0-1 exists for any equilibrium point.
To prove Property (ii), note that-V∈MEn×n is nonsingular with F⪰0. Then, the disease-free equilibrium point exists being defined by zdf=0T,hT0T, it is unique since h(·) is unique, given by xdf=F-V-1f0,h0=0 and it is locally asymptotically stable if R00<1 since then F-V∈MEn×n is a stability matrix, equivalently V-F-1≻0 so that the linearized system around the disease-free equilibrium point is locally exponentially (then asymptotically ) stable. On the other hand, it is unstable if R00>1 [Theorem 1 (iii)-(iv)]. The part (a) of Property (ii) has been proved. Now let z0=zend=xendT,yendTT be the endemic equilibrium point. Note that it is also unique, since f and h are everywhere continuous. Note that the following cases can occur.
(a) IfR00≥1, note that(58)xend=Ve-Fe-1-fxend,hxend⪰V-F-1-fxdf,hxdf⪰0since fxend,hxend⪯0 (being a strict inequality if and only if xend≠xdf=0) and F-V∈MEn×n is stable, then it is nonsingular.
(b) IfR00<1 then V-F-1≻0, since F-V∈MEn×n, and Fe≻F. Therefore, In+V-F-1Fe-F≻0 and ρV-F-1Fe-F>0 then In+In-V-1F-1V-1Fe-F-1 exists, and(59)Ve-Fe-1=In+V-F-1Fe-F-1V-F-1=In+In-V-1F-1V-1Fe-F-1V-F-1≺V-F-1Note that xend is unreachable if xend≠xdf=0 since -fxend,hxend≻-fxdf,hxdf=0, and(60)xend=Ve-Fe-1-fxend,hxend≺In+In-V-1F-1V-1Fe-F-1×V-F-1-fxdf,hxdf=In+In-V-1F-1V-1Fe-F-1xdf=0.Then, xend⪯0. But, for the case, xend=0, the endemic equilibrium point is coincident with the disease-free one, that is, xend=xdf=0 and R00=1 while, for R00<1, xend≺0, hence a contradiction to its reachability follows since the infective components of the endemic equilibrium point are never allocated in the open first orthant of Rn. Property (ii) has been proved.The above result can be reformulated directly under the replacementsR00→R0∞ and R00→R-0. Note that if R00≤1 or R0∞≤1 or R-0≤1, one concludes that the disease-free is guaranteed to be locally asymptotically stable independent of the delays. It can also be guaranteed that the endemic equilibrium point is attainable and stable independent of the delays if R-00≥1, or R0∞≥1, or R-0≥1. However, combinations of delays with identical amounts of associated delayed dynamics can exist with reproduction numbers less than R00, respectively, R0∞, or, respectively, R-0 so that the disease-free equilibrium point is unstable and the endemic one stable and attainable. From Theorem 13, and based on the previous results of Theorem 10 for the delay-free case, the following result holds on positivity and global asymptotically stability of the system subject to delays.Theorem 27.
Consider the epidemic model (27)-(29) under Assumptions 4-5 and, furthermore, assume that
(1) any of the sets of conditions of either Theorem13 (ii), or Theorem 13 (iii), or Corollary 18(ii), or Corollary 19 hold,
(2) there are a unique disease-free equilibrium point and a unique endemic equilibrium point,
(3)giz^t≥0 in (28) if yit=0 for any i∈m- and t∈R0+,
(4)fiz^t≤eiTF-Vxt if xit-hj=0 for any i∈n-; ∀j∈r-∪0 and t∈R0+ (it suffices that fiz^t≤0 if xit-hj=0),
(5) the epidemic model is subject to any bounded absolutely continuous function of initial conditionsφ:-h,0→R0+n+m with eventual finite jumps on a subset of -h,0 of zero measure, where φ0=xT0,yT0T, such that the resulting function of initial conditions φ-:-h,0→R0+n+m.
(i) Then, any trajectory solution is nonnegative and uniformly bounded independent of the delays while it is globally convergent to the disease-free equilibrium point ifR-0<1, which is also a globally asymptotically stable attractor.
If furthermore, the following condition holds:
(6) ifR-0>1 then the total population of (27)-(29), i.e., the sum of all the subpopulations, is uniformly bounded independent of the delays for all time.
(ii) Then, provided thatfx^t,y^t and gx^t,y^t are uniformly continuous in the first closed orthant of Rn+m, all the trajectory solutions are bounded and nonnegative for all time while they converge asymptotically to the endemic equilibrium point which is also the unique asymptotically stable attractor.Proof.
IfR-0<1 then conditions(1)-(5) with Assumptions 4-5 guarantee that the linearized model around the disease-free equilibrium point is locally asymptotically stable and all the trajectories within the open first orthant of Rn+m remain within it for all time. Since it is the unique attainable equilibrium the local asymptotic stability is also global (that is, for any bounded function of initial conditions within the first orthant) and asymptotic. Since all the subpopulations of both the infective and noninfective compartments are bounded and nonnegative for all time, the total population is also nonnegative and bounded through time. Property (i) has been proved. On the other hand, if condition (6) holds and R-0>1 then the disease-free equilibrium point is unstable and the endemic one is attainable. Assume the two subsequent cases potentially to occur.
Case a. The endemic equilibrium point is globally asymptotically stable independent of the delays. Property (ii) is proved directly.
Case b. The endemic equilibrium point is unstable independent of the delays. Since the total population is bounded by hypothesis and all the subpopulations are nonnegative for all time, all the subpopulations are bounded for all time and a bounded limit cycle should surround the endemic point to attract any trajectories in the first orthant from the admissible Poincaré-type combinations of allowed stable/instable combinations of configurations of singular points and limit cycles. Rewrite compactly (27) to (29) in the form z˙t=f^x^t,y^t by defining f^:R0+×Rn×Rm→Rn+m from the linearized infective dynamics and the functions f:R0+×Rn+m→Rn and g:R0+×Rn+m→Rm. Assume that ∫0∞f^x^τ,y^τdτ=±∞. Then zt→±∞ as t→∞ which is not compatible with the nonnegativity of the solution trajectories within the first orthant and their boundenness. Therefore, -∞<∫0∞f^x^τ,y^τdτ<+∞. Since f^x^·.y^· is uniformly continuous, it follows from Barbalat’s lemma that z˙t→0 as t→∞ so that zt cannot have a limit oscillation in any of its components. So, a stable limit cycle cannot surround the endemic equilibrium point. Therefore, zt→zend as t→∞ for any set of initial conditions satisfying condition (5). Property (ii) has been proved.Remarks 28.
Note that the equilibrium points do not depend on the delays but only on the delay-free and delayed matrices. However, the reproduction numbers are dependent, in general, on each particular set of delays although the thresholdR-0 is independent of the delays.
(2) Note also that Theorem27 concludes the stability (respectively, instability) of the disease-free equilibrium point for each set of delays if R-0<1 (respectively, R-0>1). However, it does not conclude that, for a particular set of delays, the endemic equilibrium point is unattainable/ unstable if R-0<1.
(3) From Remark21, Theorem 13, Corollaries 18 and 19, it follows that R00 and R0∞ are the reproduction numbers for the auxiliary delay-free systemsAILDFSandAILDDFS, respectively, and also lower bounds of the reproduction number for the whole set of delays. They also guarantee global asymptotic stability independent of the delays towards the disease-free equilibrium under sufficiency-type conditions of tolerance to the delayed dynamics given, respectively, by (44) and (40).



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