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Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Apr 9;37:105481. doi: 10.1016/j.rinp.2022.105481

Backward bifurcation and optimal control in a co-infection model for SARS-CoV-2 and ZIKV

Andrew Omame a,b,, Mujahid Abbas c,d, Chibueze P Onyenegecha e
PMCID: PMC8994284  PMID: 35433239

Abstract

In co-infection models for two diseases, it is mostly claimed that, the dynamical behavior of the sub-models usually predict or drive the behavior of the complete models. However, under a certain assumption such as, allowing incident co-infection with both diseases, we have a different observation. In this paper, a new mathematical model for SARS-CoV-2 and Zika co-dynamics is presented which incorporates incident co-infection by susceptible individuals. It is worth mentioning that the assumption is missing in many existing co-infection models. We shall discuss the impact of this assumption on the dynamics of a co-infection model. The model also captures sexual transmission of Zika virus. The positivity and boundedness of solution of the proposed model are studied, in addition to the local asymptotic stability analysis. The model is shown to exhibit backward bifurcation caused by the disease-induced death rates and parameters associated with susceptibility to a second infection by those singly infected. Using Lyapunov functions, the disease free and endemic equilibria are shown to be globally asymptotically stable for R01, respectively. To manage the co-circulation of both infections effectively, under an endemic setting, time dependent controls in the form of SARS-CoV-2, Zika and co-infection prevention strategies are incorporated into the model. The simulations show that SARS-CoV-2 prevention could greatly reduce the burden of co-infections with Zika. Furthermore, it is also shown that prevention controls for Zika can significantly decrease the burden of co-infections with SARS-CoV-2.

Keywords: SARS-CoV-2, Zika, Lyapunov functions, Stability, Backward bifurcation, Optimal control

Introduction

Arbovirus diseases (ARBOD) transmitted by Aedes aegypti, such as zika, dengue and chikungunya and the concurrent circulation of these diseases are of major public health concerns in tropical and subtropical regions. The Coronavirus pandemic caused by the “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2) has posed serious health challenges in countries with overlapping epidemics, consequently increasing the burden on public health system [1]. This is why, SARS-CoV-2 and arboviruses (ARBOD) epidemics co-occurrence has become a matter of great concern to government and health agencies in tropical regions of the world. Indeed, the resemblance in clinical symptoms of Zika and SARS-CoV-2, especially at the early stages of infection is a great challenge which makes appropriate diagnosis very difficult. Hence, the delay in the administration of an appropriate treatment leads to increase in the spread of infection. [2], [3]. Wrong diagnosis can result in lack of the proper care of the right disease and leads to worst health conditions [1], [4]. Rosario and Siqueira [5], in a recent study, also observed that arboviral infections could have life-threatening implications, such as Guillain-Barré syndrome (GBS), encephalitis, myelitis and others.

Mathematical modeling has become an important tool for studying the dynamics of infectious diseases [6], [7], [8], [9], [10], [11], [12]. Several models have been developed to study the dynamics of SARS-CoV-2 [13], [14], [15], [16], [17]. Atangana [13] developed and analyzed a fractal-fractional model for SARS-CoV-2 to assess the impact of lockdown prior to the advent of vaccination, and showed that effective lockdown strategy was very appropriate to contain the spread of the disease at the onset of the pandemic. Also, Khan and Atangana [14] modeled the dynamics of SARS-CoV-2 with quarantine and isolation. They analyzed the dynamical behavior of the disease by describing the interactions among the bats and unknown hosts. Kolebaje and co-authors [15] modeled the dynamics of COVID-19 in some African countries using a real data. They estimated the basic reproduction numbers for some countries and also presented how the disease could be controlled. In another study, Bonyah and co-workers [16] investigated a fractional optimal control model for COVID-19. They highlighted the importance of different control strategies in mitigating the spread of the disease. Moreover, the stability and optimal analysis for a COVID-19 model with quarantine and media awareness were discussed by the authors in [17].

Numerous mathematical studies have investigated the dynamics of SARS-CoV-2 and its co-infection with other diseases such as dengue [26], HIV [27], diabetes [28], [29], [30], [31], tuberculosis [32], [33] and malaria [34], [35], [36]. Most of the co-infection models in the literature do not include the assumption that susceptible individuals can get incident co-infection with the two diseases (an assumption which is possible for some diseases, and yet always ignored). Since there has not been any model yet to study the co-infection between SARS-CoV-2 and Zika virus, we therefore consider a robust novel mathematical model for the co-interactions between these two diseases, capturing incident co-infection by susceptible individuals. We shall also examine how this assumption could influence the dynamics of a co-infection model.

The major contributions of the paper are highlighted as follows:

  • i.

    The positivity and boundedness of solution of the model are discussed.

  • ii.

    The model presented herein is qualitatively analyzed for the occurrence of backward bifurcation.

  • iii

    Using Lyapunov functions, the stability of both the disease free and endemic equilibria are examined, when R0<1 and R0>1, respectively.

  • iv.

    Time dependent controls are incorporated into the model and analyzed via the Pontryagin’s principle.

  • v.

    The entire model is simulated to examine the impact of various optimal control strategies on the dynamics of SARS-CoV-2, Zika virus and their co-infections.

Model formulation

At any time t, the total human population Nh(t) consists of the following epidemiological states: Susceptible humans Sh(t), infectious humans with SARS-CoV-2 KCh(t), infectious humans with Zika virus KZh(t), humans co-infected with SARS-CoV-2 and zika virus KCZh(t), with R(t),KZh(t) denotes infected population recovered from SARS-CoV-2 and zika virus, respectively. The total vector population, at any time t, Nv(t) consists of the following states: Sv(t),KZv(t), denoting susceptible vectors and vectors infected with Zika virus, respectively. Susceptible humans catch SARS-CoV-2 at the rate β1KChNh. Individuals in this state may catch zika virus either from infected humans or vectors at the rate β2KZh+β2hKZvNh, respectively. Furthermore, since concurrent infection with both diseases is possible, we have assumed that susceptible individuals can get co-infected with SARS-CoV-2 and zika virus at the rate β3KCZhNh. Human–human-transmission of Zika has been investigated in the literature (see, for example [22]). It is also assumed that the natural death rate for each epidemiological group is μh. Infected individuals with SARS-CoV-2 can get infected with zika virus at the rate, α1(β2KZh+β2hKZv)Nh. Likewise, those infected with zika virus can get infected with SARS-CoV-2 at the rate α2β1KChNh. The death rates due to SARS-CoV-2, zika or co-infection are given by ηC,ηZ and ηCZ, respectively. Moreover, recovery rates from SARS-CoV-2, zika virus or co-infection are denoted by ζC,ζZ and ζCZ, respectively. In this model, we have assumed that no reversion or re-infection after recovery either from single or dual infections. Incorporating re-infection could be an extension to the proposed model. Parameters in the model are well defined in Table Table 1.

dShdt=Λhβ1KChNh+(β2KZh+β2hKZv)Nh+β3KCZhNh+μhShdKChdt=β1KChNhShηC+ζC+μhKChα1(β2KZh+β2hKZv)NhKChdKZhdt=(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZhα2β1KChNhKZhdKCZhdt=β3KCZhNhSh+α1(β2KZh+β2hKZv)NhKCh+α2β1KChNhKZhηCZ+ζCZ+μhKCZhdRdt=ζCKCh+ζZKZh+ζCZKCZhμhRdSvdt=Λvβ2v(KZh+KCZh)Nh+μvSvdKZvdt=β2v(KZh+KCZh)NhSvμvKZv (1)

Table 1.

Description of parameters in the model (1).

Parameter Description Value References
ηC SARS-CoV-2 disease-induced death rate 0.015/day [18]
ηZ Zika disease-induced death rate, respectively 0.001 [19]
ζC SARS-CoV-2 recovery rate 115/day [20], [21]
ζZ Zika recovery rates 0.090.15 [22]
ηCZ Co-infected disease-induced death rate 0.015/day Assumed
ζC Co-infected recovery rate 115/day Assumed
Λh Human recruitment rate 4,108,50874.9×365 per day [23]
Λv Vector recruitment rate 20,000 per day [19]
β1 Contact rate for SARS-CoV-2 infection 0.5944 [24]
β2 Contact rate for zika infection (human to human) 0.0100 [22]
β2h Contact rate for zika infection (vector to human) 0.43 [22]
β2v Contact rate for zika infection (human to vector) 0.600.75 [22]
β3 Co-infection contact rate (human to human) 0.200 Assumed
μh Human natural death rate 174.9×365 per day [25]
μv Vector removal rate 121 per day [19]
α1,α2 Modification parameters 1.0 Assumed

Analysis of the model

We shall now analyze the model qualitatively (1) without considering the controls. We begin with the following:

Positivity of solutions

For the model (1) to be epidemiologically meaningful, it is appropriate to show that all its state variables are non- negative over time. We prove the results below:

Theorem 1

Let the initial data be Sh(0)0,KCh(0)0,KZh(0)0,KCZh(0)0,R(0)0,Sv(0)0,KZv(0)0 .

Then the solutions, (Sh,KCh,KZh,KCZh,R,Sv,KZv) , of the model (1) are non-negative for all time t>0 .

Proof

See Appendix A “Proof of Theorem 1”.

Boundedness

We claim the following result:

Theorem 2

The closed set Q=Qh×Qv , with

Qh=.{(Sh,KCh,KZh,KCZh,R)R+6:Sh+KCh+KZh+KCZh
+RΛhμh.},
Qv=.{(Sv,KZv)R+2:Sv+KZvΛvμv.}.

is positively invariant with respect to the model (1) .

Proof

Adding all the equations corresponding to the human components of the system (1), we have

dNhdt=ΛhμhNh(t)[ηCKCh+ηZKZh+ηCZKCZh]. (2)

It follows from (2) that

Λh(μh+3η)NhdNhdtΛhμhNh,

where η=min{ηC,ηZ,ηCZ}.

which can be re-written as

dNhdtΛhμhNh. (3)

By applying the comparison theorem [37] and simplifying, we obtain that

Nh(t)Λhμh. (4)

Therefore, the total human population, Nh(t)Λhμh as t. Following the arguments similar to those given above, the total vector population, Nv(t)Λvμv. Hence, the system (1) has the solution in Q. Thus, the given system is positively invariant.

The basic reproduction number of the model

By setting the right-hand sides of the equations in the model (1) to zero, we obtain the disease free equilibrium (DFE) as follows:

ψ0=Sh,KCh,KZh,KCZh,R,Sv,KZv=Λhμh,0,0,0,0,Λvμv,0.

The stability of the DFE is studied by applying the next generation operator method [38] to the system (1). The transfer matrices are given by

F=β10000β20β2h00β300β2vSvNhβ2vSvNh0,V=G10000G20000G30000μv (5)

where,

G1=ηC+ζC+μh,G2=ηZ+ζZ+μh,G3=ηCZ+ζCZ+μh.

The basic reproduction number of the model (1) is given by

R0=ρ(FV1)=max{R0C,R0Z,R0CZ}, where R0C, R0Z and R0CZ are the associated reproduction numbers for SARS-CoV-2, Zika and co-infection of both diseases, respectively and are given by

R0C=β1G1,R0Z=12β2G2+12β2G22+4β2hβ2vΛvμhΛhμv2G2,R0CZ=β3G3.

For the sake of simplicity, reproduction number associated with the human-to-human Zika transmission is denoted by R0Zh=β2G2, and the reproduction number associated with the vector-to-human-to-vector Zika transmission denotes R0Zvh=β2hβ2vΛvμhΛhμv2G2. Thus, the Zika associated reproduction number can be re-written as

R0Z=12R0Zh+12R0Zh2+4R0Zv2.

Local asymptotic stability of the disease free equilibrium (DFE) of the model

Theorem 3

The DFE, H0 , of the model (1) is locally asymptotically stable (LAS) if R0<1 , and unstable if R0>1 .

Proof

The local stability of the model (1) is analyzed by the Jacobian matrix of the system (1) evaluated at the disease-free equilibrium, H0 and is given by:

μhβ1β2β300β2h0β1G10ζZ00000β2G2ζC00β2h000β3G30000ζCζZζCZμh0000β2vSvNhβ2vSvNh0μv000β2vSvNhβ2vSvNh00μv. (6)

The eigenvalues are given by

p1=μv,p2=μh(with multiplicity of 2), (7)

whereas, the remaining eigenvalues are the solutions of the equations given by

(p+G1(1R0C))=0,p2+G2+μvβ2SvNhp+μvG2(1R0ZhR0Zvh2)=0,(p+G3(1R0CZ))=0 (8)

Applying the Routh Hurwitz criterion, the roots of equations in (8) have negative real parts if and only if R0C<1 and R0Z<1. Thus, the DFE, H0 is locally asymptotically stable if R0=max{R0C,R0Z,R0CZ}<1.

Endemic equilibrium points of the model

Suppose the reproduction number R0=max{R0C,R0Z,R0CZ}>1.

Also, let the factors playing a significant role in the disease transmission for the proposed model at steady state be denoted by:

λCh=β1KChNh,λZh=(β2KZh+β2hKZv)Nh,λCZh=β3KCZhNh,λZv=β2v(KZh+KCZh)NhSv,λC2=α2β1KChNh,λZ2=α1(β2KZh+β2hKZv)Nh (9)

Then the model (1) will have multiple endemic equilibria E=(Sh,KCh,KZh,KCZh,R,Sv,KZv), where

Sh=Λh(λCh+λZh+λCZh+μh),KCh=ΛhλCh(G1+λC2h)(λCh+λZh+λCZh+μh),KZh=ΛhλZh(G2+λC2h)(λCh+λZh+λCZh+μh)KCZh=λCZhShh+λC2hKCh+λC2hKZhG3,Rh=ζCKCh+ζZKZh+ζCZKCZhμh,Sv=Λv(λZv+μv)KZv=ΛvλZvμv(λZv+μv) (10)

Backward bifurcation analysis of the model to assess the impact of incident co-infection

In this section, we shall examine the impact of the assumption of allowing incident co-infection with both diseases

(β30) with the help of backward bifurcation analysis. The phenomenon of backward bifurcation, which has been observed in several disease models, is usually characterized by the co-existence of a stable disease free equilibrium and a stable endemic equilibrium when the associated reproduction number of the model is less than unity. The public health implication of the backward bifurcation phenomenon of model (1) is that, the classical epidemiological requirement of having the reproduction number R0 less than unity, although necessary, is no longer sufficient for the effective control of the diseases. The following result is obtained using the approach in [39].

To carry out the analysis, we set the disease induced death rates equal to zero because the disease-induce death rates give rise to backward bifurcation in a vector-host model (see for example, [19]). Note that, the sub-model for SARS-CoV-2 does not undergo backward bifurcation, as already shown by the authors in [36]. Furthermore, some researchers on co-infection models (without the assumption of incident co-infection by susceptible individuals[36], [40], [41] are of the view that the dynamical behavior of the sub-models usually predict the behavior of the complete co-infection models. This motivates the analysis in this paper, under the assumption of incident co-infection with both diseases.

Setting the disease induced death rates ηC=ηZ=ηCZ=0, results in a constant population model, where, Nh=Λhμh with β¯1=μhβ1Λh,β¯2=μhβ2Λh,β¯3=μhβ3Λh,β¯2h=μhβ2hΛh,β¯2v=μhβ2vΛh. The resulting model with bilinear incidence rates is thus presented in (11). We are interested to investigate the question that if backward bifurcation occurs or not under this situation. If it does not occur, then the disease induced death will be the actual cause and the dynamics of the sub-model surely influences the complete model. However, if it occurs, we need to determine the parameter(s) which cause(s) such occurrence in addition to disease induced death rate in the co-infection model.

Theorem 4

The model (1) with negligible induced death rates ηC=ηZ=ηCZ=0 , exhibits backward bifurcation if the coefficient a given below

a=2(β¯1φ1α1β¯2φ3α1β¯2hφ7)φ2δ2+2β¯2φ1φ3δ3+2β¯2hφ1φ7δ32α2β¯1φ2φ3δ3+2β¯2φ1φ4δ4+2β¯2vφ3φ6δ7+2β¯2vφ4φ6δ7+2(α1β¯2+α22β¯1)φ2φ3δ4+2α1β¯2hφ2φ7δ4

is positive.

Proof

Suppose

He=(Sh,KCh,KZh,KCZh,R,Sv,KZv)

denote an arbitrary endemic equilibrium of the model. By the following change of variables,

S=x1,KC=x2,KZ=x3,KCZ=x4,R=x5,Sv=x6,KZv=x7,

the model (1) can be re-presented in the following form

dx1dt=Λhβ¯1x2+(β¯2x3+β¯2hx7)+β¯3x4+μhx1x2dt=β¯1x2x1ζC+μhα1(β¯2x3+β¯2hx7)x2dx3dt=(β¯2x3+β¯2hx7)x1ζZ+μhx3α2β¯1x2x3dx4dt=β¯3x4x1+α1(β¯2x3+β¯2hx7)x2+α2β¯1x2x3ζCZ+μhx4dx5dt=ζCx2+ζZx3+ζCZx4μhx5dx6dt=Λvβ¯2v(x3+x4)+μvx6dx7dt=β¯2v(x3+x4)x6μvx7 (11)

Consider the case when R0=max{R0C,R0Z,R0CZ}=1. If the contact rate β3 (say) is chosen as a bifurcation parameter, then solving for β3=β3 from R0=1 we have

β3=β3=G3

Similarly, for R0=max{R0C,R0Z,R0CZ}=1, we have β3=G3. Evaluating the Jacobian of the system (11) at the DFE, J(H0), we obtain:

J(H0)=μhβ1x1β2x1β3x100β2hx10β1x1G10ζZ00000β2x1G2ζC00β2hx1000β3x1G30000ζCζZζCZμh0000β2vx7β2vx70μv000β2vx7β2vx700μv (12)

Using the approach in [39], the matrix J(H0) has a right eigenvector associated with the zero eigenvalue of J(H0) given by φ=φ1,φ2,φ3,,φ7T, where the components are:

φ1=1μ[β1x1φ2β2x1φ3+β2hx1φ7+β3x1φ4]<0,
φ2=β1x1G1>0,φ3=φ3>0,φ4=β3x1G3>0,
φ5=1μ(ζCφ2+ζZφ3+ζCZφ4)>0,
φ6=1μv(β2vx6(φ3+φ4))=φ7<0,φ7=1μv(β2vx6(φ3+φ4))

The non-zero components of the left eigenvector of J(H0)|β3=β3, δ=[δ1,δ2,,δ7] satisfying φ.δ=1 are

δ2=β1x1G1>0,δ3=δ3>0,δ4=β3x1G3>0,δ7=β2hx1δ3μv

Using Theorem 4.1 in [39] and computing the non-zero partial derivatives of f(x) at the disease free equilibrium, (H0)), the associated bifurcation coefficients are defined below

a=k,i,j=17δkφiφj2fkxixj(0,0)andb=k,i=17δkφi2fkxiβ3h(0,0),

where,

a=2(β¯1φ1α1β¯2φ3α1β¯2hφ7)φ2δ2+2β¯2φ1φ3δ3+2β¯2hφ1φ7δ32α2β¯1φ2φ3δ3+2β¯2φ1φ4δ4+2β¯2vφ3φ6δ7+2β¯2vφ4φ6δ7+2(α1β¯2+α22β¯1)φ2φ3δ4+2α1β¯2hφ2φ7δ4 (13)
b=k,i=17δkφi2fkxiβ(0,0)=φ4δ4x1>0 (14)

It clearly shows that even under the assumption that the disease induced death rates are zero, the bifurcation coefficients a and b are positive which indicates the occurrence of backward bifurcation. However, if we set the parameters associated with the susceptibility to infection with a second disease by individuals infected with single infection α1=α2=0, then the co-efficient satisfies

a=2(β¯1φ2δ2+β¯2φ3δ3+β¯2hφ7δ3+β¯2φ4δ4)φ1+2β¯2v(φ3+φ4)φ6δ7<0(sinceφ1<0,φ6<0),

which rules out the possibility of backward bifurcation in the co-infection model. Thus, it is concluded that, human disease-induced death rates and terms associated with susceptibility to additional infection by singly infected individuals can induce backward bifurcation in the co-infection model for two diseases. Therefore, it is observed that, in addition to disease induced death rates (which induced bifurcation in the sub-model), parameters associated with infection with a second disease cause backward bifurcation in the complete model. Hence, by allowing incident co-infection with both diseases, the dynamics of the sub-model does not always drive or influence the dynamics of the complete co-infection model.

Global asymptotic stability of the disease-free equilibrium of the model (1) for a special case

Theorem 5

In the absence of infection with a second disease by singly infected individuals (that is, α1=α2=0 ), the DFE of the model (1) given by Q0 , is GAS in Q provided that R01 .

Proof

See Appendix B “Proof of Theorem 5”

The epidemiological implication of Theorem 5 is; if those already infected with a single disease do not get infected with a second disease, then both SARS-CoV-2 and Zika can be eliminated from the population provided that the threshold quantity, R0<1, regardless of the initial sizes of the sub-populations.

Global asymptotic stability of endemic equilibrium of the model (1) for a special case

Theorem 6

In the absence of infection with a second disease by singly infected individuals (that is, α1=α2=0 ), the endemic equilibrium Qe , of the model (1) with ηC=ηZ=ηCZ=0 is globally asymptotically stable (GAS) in QQ0 with Q0=Q0h×Q0v whenever R0<1 , where

Q0h=.{(Sh,KCh,KZh,KCZh,R)Qh:KCh=KZh=KCZ=0.}
Q0v=.{(Sv,KZv)Qv:KZv=0.}

Proof

See Appendix C “Proof of Theorem 6”

The epidemiological significance of Theorem 6 is; if those already infected with a single infection do not get infected with a second disease, and if diseases induced death is negligible, then both SARS-CoV-2 and HBV will persist in the population provided that the threshold quantity, R¯0>1.

Optimal control analysis

It was observed in the preceding sections that the occurrence of backward bifurcation in the model (1) makes the effective control of both diseases difficult in the population. The aim of this section is to incorporate the time dependent controls into the model (1) to obtain the optimal interventions for the elimination of the co-infections. They are defined as follows: θ1(t): SARS-CoV-2 prevention control, θ2(t): Zika prevention control, θ3(t): Control against incident co-infection, and θ4(t): Control against infection with a second disease, and the optimal control model is given by:

dShdt=Λh(1θ1)β1KChNh+(1θ2)(β2KZh+β2hKZv)Nh+(1θ3)β3KCZhNh+μhShdKChdt=(1θ1)β1KChNhShηC+ζC+μhKChα1(1θ4)(β2KZh+β2hKZv)NhKChdKZhdt=(1θ2)(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZhα2(1θ4)β1KChNhKZhdKCZhdt=(1θ3)β3KCZhNhSh+α1(1θ4)(β2KZh+β2hKZv)NhKCh+α2(1θ4)β1KChNhKZhηCZ+ζCZ+μhKCZhdRdt=ζCKCh+ζZKZh+ζCZKCZhμhRdSvdt=Λv(1θ2)β2v(KZh+KCZh)Nh+μvSvdKZvdt=(1θ2)β2v(KZh+KCZh)NhSvμvKZv (15)

subject to the initial conditions

Sh0=Sh(0),KC0h=KCh(0),KZ0=KZ(0),KCZ0h=KCZh(0),
R0=R(0),SV0=Sv(0),KZ0v=KZv(0).

Let us consider the following objective function

J[θ1,θ2,θ3,θ4]=0T[KCh(t)+KZh(t)+KCZh(t)+SV(t)+KZv(t)+ω12θ12+ω22θ22+ω32θ32+ω42θ42]dt, (16)

where T is the final time. The total cost includes the cost of SARS-CoV-2 and arboviruses preventive measures. We need to find an optimal control θ1,θ2,θ3,θ4 such that

J(θ1,θ2,θ3,θ4)=min{J(θ1,θ2,θ3,θ4)|θ1,θ2,θ3,θ4U}, (17)

where U={(θ1,θ2,θ3,θ4)} is the control set such that θ1,θ2,θ3,θ4 are measurable with 0θ1,θ2,θ3,θ41 for t[0,T]. The Hamiltonian is given by:

X=KCh(t)+KZh(t)+KCZh(t)+SV(t)+KZv(t)+ξ12θ12+ξ22θ22+ξ32θ32+ξ42θ42+ϱ1(Λh(1θ1)β1KChNh+(1θ2)(β2KZh+β2hKZv)Nh+(1θ3)β3KCZhNh+μhSh)+ϱ2((1θ1)β1KChNhShηC+ζC+μhKChα1(1θ4)(β2KZh+β2hKZv)NhKCh)+ϱ3((1θ2)(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZh (18)
α2(1θ4)β1KChNhKZh)+ϱ4((1θ3)β3KCZhNhSh+α1(1θ4)(β2KZh+β2hKZv)NhKCh+α2(1θ4)β1KChNhKZhηCZ+ζCZ+μhKCZh)+ϱ5(ζCKCh+ζZKZh+ζCZKCZhμhR)+ϱ6(Λv(1θ2)β2v(KZh+KCZh)Nh+μvSv)+ϱ7((1θ2)β2v(KZh+KCZh)NhSvμvKZv)

Existence

We now establish the existence of solution for the optimal control that minimizes the objective functional J.

Theorem 7

SupposeJis defined on the control setUsubject to system(15)with non-negative initial conditions att=0, then there exists an optimal controlu=(θ1,θ2,θ3,θ4)such thatJ(u)=minJ(θ1,θ2,θ3,θ4)|θ1,θ2,θ3,θ4U, if the following conditions given in [42] hold:

  • (i.)

    The admissible control set U is convex and closed.

  • (ii.)

    The state system is bounded by a linear function in the state and control variables.

  • (iii.)

    The integrand of the objective functional in (16) is convex with respect to the controls.

  • (iv.)

    The Lagrangian is bounded below by ϖ1|θ|ϖ3ϖ2 , where, ϖ1>0,ϖ2>0,ϖ3>1 .

Proof

Let U=[0,1]4 be the control set consisting of θ=(θ1,θ2,θ3,θ4)U, x=(S,KCh,KZh,KCZh,R,Sv,KZv) and f(t,x,θ) the right hand of (15), that is (Eq. (19) is given in Box I),

To prove Theorem 7, we proceed as follows:

  • (i.)
    It is obvious to note that the U=[0,θmax]4 is closed. In addition, consider any two arbitrary elements v,wU, where v=(v1,v2,v3,v4),w=(w1,w2,w3,w4). Then,
    λv+(1λ)w[0,θmax]4,λ[0,θmax].
    That is, λv+(1λ)wU, and hence U is a convex set.
  • (ii.)
    The control system (15) can be expressed as a linear function of control variables (θ1,θ2,θ3,θ4) with the coefficients as functions of time and state variables:
    f(t,x,θ)=ϑ(t,x)+ϕ(t,x)θ
    with (ϕ(t,x) is given in Box II)
    ϑ(t,x)=Λhβ1KZhNh+(β2KZh+β2hKZv)Nh+β3KCZhNh+μhShβ1KZhNhShηC+ζC+μhKZhα1(β2KZh+β2hKZv)NhKZh(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZhα2β1KZhNhKZhβ3KCZhNhSh+α1(β2KZh+β2hKZv)NhKZh+α2β1KZhNhKZhηCZ+ζCZ+μhKCZhζCKZh+ζZKZh+ζCZKCZhμhKZvΛvβ2v(KZh+KCZh)Nh+μvSvβ2v(KZh+KCZh)NhSvμvKZv,
    As the parameters and variables of the model are positive, we have
    f(t,x,θ)ϑ(t,x)+ϕ(t,x)θa+bθ,wherea>0,b>0.
  • (iii.)
    The optimal control problem’s Lagrangian is given by
    L=KCh(t)+KZh(t)+KCZh(t)+SV(t)+KZv(t)+12i=14ξiθi2. (20)
    Let us consider two arbitrary elements v,wU, with v=(v1,v2,v3,v4),w=(w1,w2,w3,w4). and λ[0,θmax]. We now show that,
    L[t,x,(1λ)v+λw](1λ)L(t,x,v)+λL(t,x,w).
    it follows from (20) that,
    L[t,x,(1λ)v+λw]=KCh(t)+KZh(t)+KCZh(t)+SV(t)+KZv(t)+12i=14ξi[(1λ)θi+λwi]2,(1λ)L(t,x,v)+λL(t,x,w)=KCh(t)+KZh(t)+KCZh(t)+SV(t)+KZv(t)+12(1λ)i=14ξivi2+12λi=14ξiwi2. (21)
    Taking the difference of the two equations given above, we have
    L[t,x,(1λ)v+λw][(1λ)L(t,x,v)+λL(t,x,w)]=12(λ2λ)i=14ξi(viwi)20,sinceλ[0,θmax]. (22)
    Thus,
    L[t,x,(1λ)v+λw][(1λ)L(t,x,v)+λL(t,x,w)],
    gives
    L[t,x,(1λ)v+λw][(1λ)L(t,x,v)+λL(t,x,w)]0,
    and hence convexity of L.
  • (iv.)
    There exists constants ϖ1,ϖ2 and ϖ3 such that, Lϖ1|θ|ϖ3ϖ2, ϖ1>0, ϖ2>0, ϖ3>1 We now establish the bound on L. Note that ς4θ42ς4. As θ4[0,1], 12ς4θ4212ς4. Now,
    L>ξ12θ12+ξ22θ22+ξ32θ32+ξ42θ42ξ12θ12+ξ22θ22+ξ32θ32+ξ42θ42ξ42minξ12,ξ22,ξ32,ξ42θ12+θ22+θ32+θ42ξ42minξ12,ξ22,ξ32,ξ42|θ1,θ2,θ3,θ4|2ξ42
    Hence,
    Lϖ1|θ|ϖ3ϖ2,where,ϖ1=minξ12,ξ22,ξ32,ξ42>0,
    ϖ2=ξ42>0andϖ3=2>1.

Box I.

f(t,x,θ)=Λh(1θ1)β1KZhNh+(1θ2)(β2KZh+β2hKZv)Nh+(1θ3)β3KCZhNh+μhSh(1θ1)β1KZhNhShηC+ζC+μhKZhα1(1θ4)(β2KZh+β2hKZv)NhKZh(1θ2)(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZhα2(1θ4)β1KZhNhKZh(1θ3)β3KCZhNhSh+α1(1θ4)(β2KZh+β2hKZv)NhKZh+α2(1θ4)β1KZhNhKZhηCZ+ζCZ+μhKCZhζCKZh+ζZKZh+ζCZKCZhμhKZvΛv(1θ2)β2v(KZh+KCZh)Nh+μvSv(1θ2)β2v(KZh+KCZh)NhSvμvKZv (19)

Box II.

ϕ(t,x)=β1KZhNhShβ2KZh+β2hKZvNhShβ3KCZNhSh0β1KZhNhSh00α1β2KZh+β2hKZvNhKZh0β2KZh+β2hKZvNhSh0α2β1KZhNhKZh00β3KCZNhShα1β2KZh+β2hKZvNhKZhα2β1KZhNhKZh00000β2v(KZh+KCZh)NhSv000β2v(KZh+KCZh)NhSv00

Theorem 8

Suppose the set θ={θ1,θ2,θ3,θ4} minimizes J over U , then adjoint variables ϱ1,ϱ2,,ϱ7 , satisfy the adjoint equations

ϱit=Xi,

with (where the adjoint functions given in the Appendix D “Adjoint functions”)

ϱi(tf)=0,where,i=S,KCh,KZh,KCZh,R,Sv,KZv. (23)

Furthermore,

θ1=min1,max0,β1KChSh(ϱ2ϱ1)ξ1Nh,θ2=min1,max×0,(β2KZh+β2hKZv)Sh(ϱ3ϱ1)+β2v(KZh+KCZh)Sv(ϱ7ϱ6)ξ2Nh,θ3=min1,max0,β3KCZhSh(ϱ4ϱ1)ξ3Nh,θ4=min1,max×0,(β2KZh+β2hKZv)α1KCh(ϱ4ϱ2)+β1KChα2KZh(ϱ4ϱ2)ξ4Nh, (24)

Proof of Theorem 8

Considering U=(θ1,θ2,θ3,θ4) and the associated solutions Sh,KCh,KZh,KCZh,R,Sv,KZv, Pontryagin’s Maximum Principle [43] is applied to obtain the following:

dϱ1dt=XSh,ϱ1(tf)=0,dϱ2dt=XKCh,ϱ2(tf)=0,dϱ3dt=XKZh,ϱ3(tf)=0,dϱ4dt=XKCZh,ϱ4(tf)=0,dϱ5dt=XR,ϱ5(tf)=0,dϱ6dt=XSv,ϱ6(tf)=0,dϱ7dt=XKZv,ϱ7(tf)=0. (25)

On the interior of the set, where 0<θj<1    (j=1,,4), we have

0=Xθ1=ξ1Nθ1β1KChSh(ϱ2ϱ1),0=Xθ2=ξ2Nθ2[(β2KZh+β2hKZv)Sh(ϱ3ϱ1)+β2v(KZh+KCZh)Sv(ϱ7ϱ6)],0=Xθ3=ξ3Nθ3β3KCZhSh(ϱ4ϱ1),0=Xθ4=ξ4Nθ4(β2KZh+β2hKZv)α1KCh(ϱ4ϱ2)+β1KChα2KZh(ϱ4ϱ2). (26)

Therefore,

θ1=β1KChSh(ϱ2ϱ1)ξ1Nh,θ2=(β2KZh+β2hKZv)Sh(ϱ3ϱ1)+β2v(KZh+KCZh)Sv(ϱ7ϱ6)ξ2Nh,θ3=β3KCZhSh(ϱ4ϱ1)ξ3Nh,θ4=(β2KZh+β2hKZv)α1KCh(ϱ4ϱ2)+β1KChα2KZh(ϱ4ϱ2)ξ4Nh. (27)
θ1=min1,max0,β1KChSh(ϱ2ϱ1)ξ1Nh,
θ2=min1,max×0,(β2KZh+β2hKZv)Sh(ϱ3ϱ1)+β2v(KZh+KCZh)Sv(ϱ7ϱ6)ξ2Nh,
θ3=min1,max0,β3KCZhSh(ϱ4ϱ1)ξ3Nh,
θ4=min1,max×0,(β2KZh+β2hKZv)α1KCh(ϱ4ϱ2)+β1KChα2KZh(ϱ4ϱ2)ξ4Nh, (28)

Numerical simulations

In this section, simulations of the control system (15) are carried out. This is done with MATLAB using the forward backward sweep by Runge Kutta method. The quadratic cost functions 12ω1θ12,12ω2θ22,12ω3θ32 and 12ω4θ42 are used. The weight constants are assumed as: ω1=25,ω2=20,ω3=35 and ω4=45.

In the subsequent sections, we investigate the impact of various control strategies on the co-dynamics of both diseases and their co-infection. For the demographic parameters, Λh and μh, we obtained their values based on the total population and life expectancy of Espirito Santo state, Brazil (a country with co-endemicity of both SARS-CoV-2 and zika virus), which are approximately given by 4,108,508 and 74.9 respectively [23], [25]. The initial conditions used for the simulations are set as follows: Sh(0)=3,600,000;ICh(0)=800;IZh(0)=24;ICZh(0)=100;R(0)=100;Sv(0)=4000;IZv(0)=600.

Strategy A: Impact of SARS-CoV-2 prevention control (θ10)

The simulations of the optimal control system (15) when the strategy that prevents SARS-CoV-2 (θ10) is implemented, are depicted in Fig. 1, Fig. 1, respectively. On implementation of this intervention strategy, for β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1, we observe a significant decrease in the total number of individuals infected with SARS-CoV-2, as expected (shown in Fig. 1(a)). Interestingly, this strategy has significant impact on co-infected cases. It is observed that, significant co-infected cases of SARS-CoV-2 and Zika are averted by this control strategy (as depicted in Fig. 1(b)). The control profile for this intervention strategy is depicted in Fig. 1(c), showing very high impact against incident infections either with SARS-CoV-2 or co-infections.

Fig. 1.

Fig. 1

Impact of SARS-CoV-2 prevention (θ1) on individuals in KCh and KCZh epidemiological classes. Here, β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1.

Strategy B: Impact of Zika prevention controls (θ20)

The simulations of the optimal control system (15) when the strategy that prevents Zika transmission (from both human and vectors) (θ20) is implemented, are depicted in Figs. 2(a), 2(b), 2(c), respectively. Implementation of this intervention strategy, for β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1 shows a significant decrease in the total number of individuals infected with Zika virus, as expected (shown in Fig. 2(a)). Also, this strategy has high positive population level impact on co-infected cases, as observed in Fig. 2(b). It is interesting to note that zika prevention strategy averts more co-infection cases than the SARS-prevention strategy. Equally, this strategy caused great reduction in infected vector populations as observed in Fig. 2(c). The control profiles for this strategy are depicted by Fig. 2(d). It is observed that the control has very high effect against Zika transmission.

Fig. 2.

Fig. 2

Impact of Zika prevention (θ2) on individuals in KZh and KCZh, and vectors in KZv epidemiological class. Here, β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1.

Strategy C: Impact of control against co-infections with both diseases (θ30,θ40)

The simulations of system (15) when the strategy that prevents incident co-infection with SARS-CoV-2 and Zika virus (θ30) is implemented, are depicted in Fig. 3, Fig. 3. Implementation of this intervention strategy, for β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1 indicates a significant decrease in the total number of individuals co-infected with SARS-CoV-2 and Zika virus as depicted by Fig. 3(a). Thus, to avert co-infection cases in the population, it is not enough to prevent new single cases. Efforts must also be made to prevent individuals from getting infected with concurrent infections. This control strategy also caused a great reduction of infected vector population, as shown in Fig. 3(b). Also, the simulation of the system (15) when the strategy that prevents infection with a second disease by those already infected, is depicted by Fig. 3(c). It is observed that a good number of co-infected cases is averted when this strategy is strictly put in place. The control profiles for prevention of incident co-infections and prevention of an additional infection by singly infected individuals, are presented in Fig. 3, Fig. 3. It is observed that, the control strategy against incident co-infections attains its peak value nearly after 50 days into the simulation period and throughout maintains this value. Moreover, the control against infection with a second disease, was at its peak throughout the simulation period.

Fig. 3.

Fig. 3

Impact of control against incident co-infections θ3 and θ4 on individuals in KCZh and KCZh, and vectors in KZv epidemiological class. Here, β1=0.2441,β2=0.02441,β2h=0.200,β3=0.18,β2v=0.501, so that R0=max{R0C,R0Z,R0CZ}=1.1938>1.

Conclusion

In this paper, a new mathematical model for SARS-CoV-2 and Zika co-dynamics was presented, incorporating incident co-infection by susceptible individuals which is not common in the existing models. We discussed the impact of this assumption on the dynamics of a co-infection model. The model also incorporated sexual transmission of Zika. The positivity and boundedness of solution of the developed model was investigated, in addition to local asymptotic stability analysis. The model was shown to exhibit backward bifurcation, caused by disease induced death rates and parameters associated with susceptibility to a second infection by those already infected. Employing the Lyapunov functions, the disease free and endemic equilibria were shown to be globally asymptotically stable for R0<1 and R0>1, respectively. To manage the co-circulation of both infections in an effective manner, under an endemic setting, time dependent controls in the form of SARS-CoV-2, Zika and co-infection prevention strategies were incorporated into the model. The simulations showed that SARS-CoV-2 prevention could greatly reduce the burden of co-infections with Zika. Furthermore, it was shown that prevention controls for Zika can significantly reduce the burden of co-infections with SARS-CoV-2. We expect that the findings of this paper will open new avenues of research in this direction.

The model proposed in this paper focused only on SARS-CoV-2 and Zika co-infection, without vaccination or re-infection either with single or both diseases. Incorporating vaccination for SARS-CoV-2 and re-infection for both diseases, one could obtain an extension of the model. Also, the emergence of different variants of SARS-CoV-2 attracts further studies on their co-infections with other diseases, such as Zika, dengue, TB, influenza, Malaria and others. One could therefore, consider multi-strains of SARS-CoV-2 and co-infection with zika virus. Also, more investigations could be carried out to the mathematical (stochastic, agent based modeling, within/intra-host) and epidemiological dynamics of SARS-CoV-2 and Zika co-infection. The current research was not a case study because of insufficient data and information about the co-infection of both diseases. For instance, little is known about infection acquired cross-immunity between both diseases. Not much information is available to answer the question: whether the current available vaccines against SARS-CoV-2 could have any impact on the dynamics of zika virus. Thus, further studies with more reliable data and detailed information about the co-infection of both diseases is viable.

CRediT authorship contribution statement

Andrew Omame: Conceptualization, Formal analysis, Methodology, Writing – original draft, Software. Mujahid Abbas: Writing – original draft, Validation, Writing – review & editing, Supervision. Chibueze P. Onyenegecha: Writing – original draft, Review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Authors are thankful to the reviewers and managing editor for their constructive comments and useful remarks which helped us a lot to improve the presentation and quality of the manuscript.

Appendix A: Proof of Theorem 1

Let

t1=sup{t>0:Sh(0)>0,KCh(0)>0,KZh(0)>0,KCZh(0)>0,R(0)>0,Sv(0)>0,KZv(0)>0[0,t]}. Thus, t1>0.

Suppose further, λC=β1KChNh,λZ=(β2KZh+β2hKZv)Nh,λCZ=β3KCZhNh, then the first model equation can be written as:

dShdt=Λh(λC+λZ+λCZ+μ)Sh, (29)

Applying the integrating factor method on (29), we obtain

ddtSh(t)exp0t(λC(u)+λZ(u)+λCZ(u))du+μt=Λhexp0t(λC(u)+λZ(u)+λCZ(u))du+μt

and

Sh(t1)exp0t1(λC(u)+λZ(u)+λCZ(u))du+μ(t)1Sh(0)=Λh0t1exp0x(λC(u)+λZ(u)+λCZ(u))du+μxdx,

with,

Sh(t1)=Sh(0)exp0t1(λC(u)+λZ(u)+λCZ(u))duμt1+exp0t1(λC(u)+λZ(u)+λCZ(u))duμt1×Λh0t1exp0x(λC(u)+λZ(u)+λCZ(u))du+μxdx>0.

Similarly, it can be shown that:

KCh(0)>0,KZh(0)>0,KCZh(0)>0,R(0)>0,Sv(0)>0,KZv(0)>0.

Appendix B: Proof of Theorem 5

Consider the Lyapunov function

L1=ln(ShSh0)+KCh+KZh+KCZh+R+(SvSv0)+KZv+1G1KCh+1G2KZh+1G3KCZh+β2hμvG2KZv,

with Lyapunov derivative

L˙1=1(ShSh0)+KCh+KZh+KCZh+R+(SvSv0)+KZv×(Λhβ1KChNh+(β2KZh+β2hKZv)Nh+β3KCZhNh+μhSh+β1KChNhShηC+ζC+μhKCh+(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZh+β3KCZhNhShηCZ+ζCZ+μhKCZh+ζCKCh+ζZKZh+ζCZKCZhμhR+Λvβ2v(KZh+KCZh)Nh+μvSv+β2v(KZh+KCZh)NhSvμvKZv)+1G1β1KChNhShηC+ζC+μhKCh+1G2(β2KZh+β2hKZv)NhShηZ+ζZ+μhKZh+1G3β3KCZhNhShηCZ+ζCZ+μhKCZh+β2hμvG2β2v(KZh+KCZh)NhSvμvKZv

which can be further simplified into

L˙1=1(ShSh0)+KCh+KZh+KCZh+R+(SvSv0)+KZv×(Λhμh(Sh+KCh+KZh+KCZh+R)+Λvμv(Sv+KZv)(ηCKCh+ηZKZh+ηCZKCZh))+β1G11KCh+β2G2+β2hβ2vSvμvG2Nh1KZh+β3G3+β2hβ2vSvμvG2Nh1KCZh

Simplifying further (noting that Sh+KCh+KZh+KCZh+RΛhμh,Sv+KZvΛvμv, and SvNh<SvNh), we have

L˙1(ηCKCh+ηZKZh+ηCZKCZh)(ShSh0)+KCh+KZh+KCZh+R+(SvSv0)+KZv+β1G11KCh+β2G2+β2hβ2vSvμvG2N1KZh+β3G3+β2hβ2vSvμvG2N1KCZh=(ηCKCh+ηZKZh+ηCZKCZh)(ShSh0)+KCh+KZh+KCZh+R+(SvSv0)+KZv+R0C1KCh+R0Zh+R0Zvh21KZh+R0CZ+R0Zvh21KCZ

Since all the model parameters and variables are non-negative, it follows that L˙1<0 for R0=max{R0C,R0Z,R0CZ}1. Hence, L1 is a Lyapunov function on Q. Thus, the DFE is globally asymptotically stable [44].

Appendix C: Proof of Theorem 6

Consider the model (1) with ηC=ηZ=ηCZ=0(withNh=Λhμh,β¯1=μhβ1Λh,β¯2=μhβ2Λh,β¯2h=μhβ2hΛh,β¯3=μhβ3Λh,β¯2h=μhβ2hΛh,β¯2v=μhβ2vΛh) and R0>1, so that the associated unique endemic equilibrium exists. Also, consider the non-linear Lyapunov function:

L2=μv[ShShShlnShSh+KChKChKChlnKChKCh+KZhKZhKZhlnKZhKZh+KCZKCZhKCZhlnKCZKCZh]+β¯2hSh[SvSvSvlnSvSv+KZvKZvKZvlnKZvKZv]

with Lyapunov derivative,

L˙2=μv[1ShShSh˙+1KChKChK˙Ch+1KZhKZhK˙Zh+1KCZhKCZK˙CZh]+β¯2hSh[1SvSvSv˙+1KZvKZvK˙Zv] (30)

Substituting the derivatives in (1) into L˙2, we have

L˙2=μv1ShSh×Λhβ¯1KChSh(β¯2KZh+β¯2hKZv)Shβ¯3KCZhShμhSh+μv1KChKChβ¯1KChShηC+ζC+μhKCh+μv1KZhKZh(β¯2KZh+β¯2hKZv)ShηZ+ζZ+μhKZh (31)
+μv1KCZhKCZhβ¯3KCZhShηCZ+ζCZ+μhKCZh+β¯2hSh1KZvKZvΛvβ¯2v(KZh+KCZh)SvμvSv+β¯2hSh1KZvKZvβ¯2v(KZh+KCZh)SvμvKZv

From model (1) at steady state, we have

Λh=β¯1KChSh+(β¯2KZh+β¯2hKZv)Sh+β¯3KCZhSh+μhShβ¯1KChSh=ζC+μhKCh(β¯2KZh+β¯2hKZv)Sh=ζZ+μhKZhβ¯3KCZhSh=ζCZ+μhKCZhζCKCh+ζZKZh+ζCZKCZh=μhRΛv=β¯2v(KZh+KCZh)Sv+μvSvβ¯2v(KZh+KCZh)Sv=μvKZv (32)

Substituting the expressions in (32) into (31) gives

L˙2=μv1ShSh[β¯1KChSh+(β¯2KZh+β¯2hKZv)Sh+β¯3KCZhSh+μhShβ¯1KChSh(β¯2KZh+β¯2hKZv)Shβ¯3KCZhShμhSh]+μv1KChKChβ¯1KChShηC+ζC+μhKCh+μv1KZhKZh(β¯2KZh+β¯2hKZv)ShηZ+ζZ+μhKZh+μv1KCZhKCZhβ¯3KCZhShηCZ+ζCZ+μhKCZh+β¯2hSh1KZvKZv[β¯2v(KZh+KCZh)Sv+μvSvβ¯2v(KZh+KCZh)SvμvSv]+β¯2hSh1KZvKZvβ¯2v(KZh+KCZh)SvμvKZv

which after some algebraic manipulations is reduced to

L˙2=μhμvSh2ShShShSh+μvβ¯2hShSv2SvSvSvSv+μv[2β¯1KChSh+2β¯2KZhSh+2β¯2hKZvSh+2β¯3KCZhShβ¯1KChSh2Shβ¯1KChShβ¯2KZhSh2Shβ¯2KZhShβ¯2hKZvSh2Shβ¯2hKZvKZhShKZhβ¯3KCZhSh2Shβ¯3KCZhSh]+β2hSh[β¯2vKZhSv+β¯2vKCZhSvβ¯2vKZhSv2Sv+β¯2vKZhSv+β¯2vKCZhSvβ¯2vKCZhSv2Svβ¯2vKZhKZvSvKZvβ¯2vKCZhKZvSvKZv] (33)

The above can be simplified to

L˙2=μhμvSh2ShShShSh+μvβ¯2hShSv2SvSvSvSv+μvShShβ¯1KCh+β¯2KZh+β¯3KCZh2ShShShSh+β¯2hβ¯2vKZhShSv4ShShSvSvKZvKZhShKZvKZhShKZhKZvSvKZhKZvSv+β¯2hβ¯2vKCZhShSv4ShShSvSvKZvKCZhShKCZvKCZhShKCZhKZvSvKCZhKZvSv. (34)

As arithmetic mean is greater that geometric mean, the following inequalities from (34) hold:

2ShShShSh0,2SvSvSvSv0,
4ShShSvSvKZvKZhShKZvKZhShKZhKZvSvKZhKZvSv0,
4ShShSvSvKZvKCZhShKCZvKCZhShKCZhKZvSvKCZhKZvSv0.

Thus, L2˙0 for R¯0>1. Hence, L2 is a Lyapunov function in DD0 and we conclude that the GAS of EEP is globally asymptotically stable for R¯0>1.

Appendix D: Adjoint functions

ϱ1=ϱ3KZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+ShShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ4KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2KCZhβ3θ31KCh+KCZh+KZh+R+Sh+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2+ϱ2KChβ1θ11KCh+KCZh+KZh+R+ShKChShβ1θ11KCh+KCZh+KZh+R+Sh2+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+ϱ1μhKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+ShKChβ1θ11KCh+KCZh+KZh+R+ShKCZhβ3θ31KCh+KCZh+KZh+R+Sh+ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2+KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2+Svβv2ϱ6θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2Svβv2ϱ7θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2
ϱ2=ϱ1ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2Shβ1θ11KCh+KCZh+KZh+R+Sh+KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2ϱ5ζ1ϱ3ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2+KZhα2β1θ41KCh+KCZh+KZh+R+ShKChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ4KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2α1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+ShKZhα2β1θ41KCh+KCZh+KZh+R+Sh+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2+ϱ2η1+μh+ζ1+Shβ1θ11KCh+KCZh+KZh+R+Shα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+ShKChShβ1θ11KCh+KCZh+KZh+R+Sh2+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+Svβv2ϱ6θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2Svβv2ϱ7θ21KCZh+KZhKCh+KCZh+KZh+R+Sh21
ϱ3=ϱ3η2+μh+ζ2+Shβ2θ21KCh+KCZh+KZh+R+ShShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2KChα2β1θ41KCh+KCZh+KZh+R+Sh+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ4KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2KChα1β2θ41KCh+KCZh+KZh+R+ShKChα2β1θ41KCh+KCZh+KZh+R+Sh+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ5ζ2ϱ6Svβv2θ21KCh+KCZh+KZh+R+ShSvβv2θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2+ϱ7Svβv2θ21KCh+KCZh+KZh+R+ShSvβv2θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2+ϱ1ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2Shβ2θ21KCh+KCZh+KZh+R+Sh+KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2ϱ2KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KChα1β2θ41KCh+KCZh+KZh+R+ShKChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh21
ϱ4=ϱ7Svβv2θ21KCh+KCZh+KZh+R+ShSvβv2θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2ϱ3ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ5ζ3ϱ6Svβv2θ21KCh+KCZh+KZh+R+ShSvβv2θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2ϱ2KChShβ1θ11KCh+KCZh+KZh+R+Sh2KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+ϱ4η3+μh+ζ3+Shβ3θ31KCh+KCZh+KZh+R+ShKCZhShβ3θ31KCh+KCZh+KZh+R+Sh2KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2+ϱ1ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2Shβ3θ31KCh+KCZh+KZh+R+Sh+KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KCZhShβ3θ31KCh+KCZh+KZh+R+Sh21
ϱ5=ϱ5μhϱ3ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2ϱ2KChShβ1θ11KCh+KCZh+KZh+R+Sh2KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+ϱ1ShKZhβ2+KZvβh2θ21KCh+KCZh+KZh+R+Sh2+KChShβ1θ11KCh+KCZh+KZh+R+Sh2+KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2ϱ4KCZhShβ3θ31KCh+KCZh+KZh+R+Sh2+KChα1KZhβ2+KZvβh2θ41KCh+KCZh+KZh+R+Sh2+KChKZhα2β1θ41KCh+KCZh+KZh+R+Sh2+Svβv2ϱ6θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2Svβv2ϱ7θ21KCZh+KZhKCh+KCZh+KZh+R+Sh2
ϱ6=ϱ6μvβv2θ21KCZh+KZhKCh+KCZh+KZh+R+Sh+βv2ϱ7θ21KCZh+KZhKCh+KCZh+KZh+R+Sh1
ϱ7=ϱ7μvShβh2ϱ1θ21KCh+KCZh+KZh+R+Sh+Shβh2ϱ3θ21KCh+KCZh+KZh+R+ShKChα1βh2ϱ2θ41KCh+KCZh+KZh+R+Sh+KChα1βh2ϱ4θ41KCh+KCZh+KZh+R+Sh1

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