Discrete Dynamics in Nature and Society

Volume 2019, Article ID 8959681, 22 pages

https://doi.org/10.1155/2019/8959681

## 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

Correspondence should be addressed to M. De la Sen; sue.uhe@nesaled.leunam

Received 20 December 2018; Revised 20 May 2019; Accepted 16 June 2019; Published 8 July 2019

Academic Editor: Cengiz Çinar

Copyright © 2019 M. De la Sen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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.

#### 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 – 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 of such linearized systems around the equilibrium points are nonnegative. If the Perron root of the auxiliary matrix (next generation matrix) , with 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 and -matrices and on the uniqueness of the equilibrium points.

##### 1.1. Notation

, ; , ; , ; ; is the complex unity; is the closed circle in centred at and radius one of boundary (that is, the unit circumference), ; ;

If then (1) (in words: is a nonnegative matrix) means that ; , . An equivalent notation is . Similarly, if then means that and means that .

(2) (in words: is a positive matrix) means that , that is, with at least one positive entry. Similarly, if then means that and means that .

(3) (in words: is a strictly positive matrix) means that ; , . An equivalent notation is . Similarly, if then means that and means that ,

It turns out that .

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 then means that for some .

For vectors , similar nonnegativity, positivity, and strictly positivity notations are used, namely, , , and the corresponding nonpositivity, negativity, and strict negativity counterpart notations.

is the identity -matrix, and superscript denotes a matrix transposition,

is the spectral radius of ,

is a Metzler matrix, denoted by , if ; ,

is an -matrix if and ; ,

is the - norm of complex rational matrices which are analytic in ; . The argument “s” stands for the Laplace transform argument.

#### 2. Results for Delay-Free Epidemic Models

A general compartmental disease model of dimension can be written as [1]for any given , where and are the respective dimensions of the disease and nondisease compartments of respective state vectors and , and are, respectively, the disease transmission and state transition matrices of the linearized system around the disease-free equilibrium point, and describes the higher-order contributions to the infective compartment dynamics, where is the whole model state vector, is the scalar positive bounded incidence rate, and , where is a partitioned block matrix function of the form . We can also rename, by obvious reasons, the state vectors and 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*. ; , , when , and for all .

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 and are real square -matrices defined as the following Jacobian matrices around the disease-free equilibrium point : The following assumptions on the above Jacobian matrices are reasonable in the context of epidemic models [1]. The notations , stand through the manuscript for the Jacobian matrices around the disease-free equilibrium point. The notations , stand for their counterparts around the endemic equilibrium point while the notations , stand for any generic equilibrium point . It is well-known in many typical existing epidemic models that and are coincident when the reproduction number is unity.

*Assumptions 2*. (a) The disease-free equilibrium point is unique and the noninfective substate dynamics is locally asymptotically stable.

(b) The minus transition matrix of the infective states of the linearized infective subsystem around the disease-free equilibrium point is a nonsingular -matrix with while the corresponding transmission matrix fulfills .

The above assumptions are very relevant from a physical point of view having in mind that and are, respectively, the transition and transmission matrices of the linearized system around the disease-free equilibrium point while is the matrix of the dynamics of such a system. It turns out, in practice, that if and only if the coefficient transmission rate 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 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 .

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 . Note also that depends on the disease-free equilibrium substate of the noninfective components and on the reproduction number associated with the disease propagation. If then . Also, it usually occurs that if the epidemic model has only a single constant transmission coefficient rate , then and if . 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 :

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 and (i.e., the infective substate is nonnegative for all time) if and only if . A sufficient condition is that and .**(ii) A necessary condition for an epidemic model (1)-(2) to have a nonnegative solution trajectory, irrespective of the given particular initial conditions , , is that .**(iii) The linearized infective substate around the disease-free equilibrium point has a nonnegative solution which is uniformly bounded for all time with ; and exponentially as for any given initial conditions , if and only if any of the two equivalent conditions given below holds:**(1) is a stability matrix.**(2) is nonsingular (or, equivalently, is a nonsingular -matrix) and .**A joint sufficiency-type condition for any of the above Conditions (1)-(2) to hold is that is nonsingular (or, equivalently, is a nonsingular -matrix), , and .**(iv) The linearized infective substate around the disease-free equilibrium point is nonnegative for any given initial conditions , and unstable if some of the conditions given below hold:**(1) is not a stability matrix.**(2) and either does not exist or if it exists is not positive.**(3) and it exists as which is not positive, and .*

*Proof. *The solution of the linearized infective substate around the disease-free equilibrium point is It turns out that, for any , since for any , and [14], then, ; for any given if (first term of the solution ) or if and (second term of the solution ). Then and and are both sufficient conditions for ; . The necessity of follows by contradiction. If then there exists at least one entry such that for some . Then, it suffices to take and ; to conclude that so that the infective substate is not positive. Thus, 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 -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 if for any given then (otherwise, if ). Therefore, . 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., ; for any given initial condition if and only if the two subsequent constraints hold:Furthermore, one has that(ii) Assume, furthermore, that is a stability matrix or that any of the two constraints (1) or (2) of Theorem 1 (iii) holds. Then, provided that is everywhere uniformly continuous in its definition domain and ; ; and as (but nonnecessarily ) if is everywhere uniformly continuous in its definition domain.*

*Proof. *Property (i) follows directly from (1)-(3). On the other hand, if , and ; and it is everywhere uniformly continuous for all time for its arguments in the first closed orthant of for all time; then it follows that , and and as . Also, if is everywhere continuous for all time in and since and it is a stability matrix. Then, it follows from Barbalat’s lemma [18, 24] that as . Property (ii) has been proved.

Generally speaking, Theorem 1 (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 is stable which is needed to guarantee that no eigenvalue crosses the imaginary axis under perturbations of resulting in to lose the stability of when , i.e., for . In the following discussions is the transition matrix in-between state of the linearized subsystem of the infected variables around the disease-free equilibrium point while is the transmission matrix around such an equilibrium point.

On the other hand, it turns out that the nonnegativity condition ; of Theorem 2 for the whole trajectory solution, subject to nonlinear dynamics, of the infective substate does not require that is a stability matrix or any conditions on the nonlinear term . Note that the condition holds directly if since then ; so that . 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., ; for any given initial condition if and only if, for all , and all , the following conditions hold:(ii) Property (i) also implies that ; , ; if a disease-free equilibrium point exists.*

*Proof. *It is obvious that, for any given , if for some , then from the given conditions and it cannot give such that for some . As a result, ; . 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 . If ; then Property (ii) has been proved for the infective substate. Now, assume that there exists some such that . Since , it follows that so that is not an equilibrium point; hence a contradiction follows. In the same way, if for some then is not an equilibrium point; hence we have again a contradiction. Property (ii) has been proved.

Theorem 4. *Let be everywhere continuously differentiable with a Jacobian matrix:** at any with and , where , , . Assume that the matrix is nonsingular at any equilibrium point of (1)-(3). Then, the following properties hold**(i) There exists an open set of containing such that there exists a unique continuously differentiable function such that and ; . Furthermore, any equilibrium point can be expressed being dependent on the infective substate only, i.e., , while satisfyingprovided that is nonsingular at any equilibrium point, where and if is the disease-free equilibrium point and and if is the endemic equilibrium point.**(ii) Assume, furthermore, that the transition and transmission matrices of the disease-free equilibrium point satisfy that is a stability matrix and . Then, (a) the disease-free equilibrium point exists and it is defined by and it is unique and locally asymptotically stable if . If then it is unstable; and**(b) the endemic equilibrium point exists (while it is unique under the sufficiency-type conditions of Theorem B.2 of Appendix B) and it is defined by subject toIf it exists then it is reachable if and while it is unreachable, in the sense that , if supposing that if .*

*Proof. *Assume that is nonsingular. Then, there exits an open set of containing such that there exists a unique continuously differentiable function such that and ; . Let be either the disease-free equilibrium point or the endemic one . Both of them are unique in a neighbourhood centred at the respective equilibrium point, since is unique, while they satisfy (10) and, respectively, (11) if and, respectively, are nonsingular since is a nonsingular matrix. Property (i) has been proved. In order to prove Property (ii), first note that is nonsingular with (since is stable) with . Then, the disease-free equilibrium point exists being defined by ; it is unique since is unique and given by . Furthermore, it is locally asymptotically stable if since then is a stability matrix, equivalently 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 [Theorem 1 (iii)-(iv)]. The part (a) of Property (ii) has been proved. Now let be the endemic equilibrium point. The following cases can occur

(1) If , then note thatsince (being a strict inequality if and only if ) and is stable and then nonsingular

(2) If then , since , and . Therefore, and then exists, andNote that is unreachable if since , andThen, . But, for the case, , the endemic equilibrium point is coincident with the disease-free one, that is, and , while, for , ,; hence a contradiction to its reachability follows since the equilibrium point is never allocated in the open first orthant of . 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 Theorem B.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 . On the other hand, the endemic equilibrium point is unique in the whole state space if , i.e., its infective substate, is unique since from the uniqueness of , 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 number 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 then the endemic equilibrium point is stable and the disease-free one is unstable and, if , 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 , , and are constant and there is no impulsive control for the infectious corpses removal control action:where is a vaccination control of constant gain plus a linear feedback term with gain with information of the susceptible subpopulation. There is also an eventual treatment control on the infectious subpopulation of gain . The disease-free and endemic Jacobian matrices for the joint noninfective infective dynamically coupled compartments are, respectively,where the infective compartment has a Jacobian matrix uncoupled to the noninfective one withandwhere the infective compartment Jacobian matrix is withwith . Note that if ; if ; and if (what implies ), and irrespective that the infective variables be positive or negative since , , and have the same sign as , if , that is, if the endemic equilibrium point has not coincident components with the disease-free one, that is, if , as it is deduced from the epidemic model. Note from the matrix 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 bywhose roots are and . So, such a subsystem is asymptotically stable satisfying Assumption 2(a). As a result, the whole linearized subsystem is asymptotically stable if .

It has been proved in Theorem 4 that the disease-free equilibrium point is stable if , the endemic one is unattainable if , and the disease-free equilibrium point is unstable if while the endemic one is reachable if . 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 and are identical and independent of the reproduction number, and is a stability matrix.**(2) The disease-free and endemic equilibrium points exist and they are unique for any .**(3) The disease-free and the endemic transmission matrices are nonnegative, i.e., , and , where is the reproduction number, and .**(4) for some .**(5) if , if , and if .**Then, the endemic equilibrium point is locally asymptotically stable if and unstable if .*

*Proof. *Note thatSince is a stability matrix, the disease-free equilibrium point is locally asymptotically stable if and unstable if , the critical stability case being . Note also thatSince with for since , then and both of them are proportional matrices because of their structure, it follows that the endemic equilibrium point is unstable for (when the disease-free is locally stable ) and locally stable for (when the disease-free one is unstable).

Note that for Example 6, in the proof of Proposition 7. Note that assumption 4 of Proposition 7 is very reasonable if and 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 Example 6 with the following parameterization: 1/25550; ; ; ; ; ; ; ; ; ; ; . Figure 1 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 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.