Global stability of infectious disease models with contact rate as a function of prevalence index

  • Received: 11 August 2015 Accepted: 26 January 2017 Published: 01 August 2017
  • MSC : Primary: 34K20, 92D30

  • In this paper, we consider a SEIR epidemiological model with information-related changes in contact patterns. One of the main features of the model is that it includes an information variable, a negative feedback on the behavior of susceptible subjects, and a function that describes the role played by the infectious size in the information dynamics. Here we focus in the case of delayed information. By using suitable assumptions, we analyze the global stability of the endemic equilibrium point and disease-free equilibrium point. Our approach is applicable to global stability of the endemic equilibrium of the previously defined SIR and SIS models with feedback on behavior of susceptible subjects.

    Citation: Cruz Vargas-De-León, Alberto d'Onofrio. Global stability of infectious disease models with contact rate as a function of prevalence index[J]. Mathematical Biosciences and Engineering, 2017, 14(4): 1019-1033. doi: 10.3934/mbe.2017053

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  • In this paper, we consider a SEIR epidemiological model with information-related changes in contact patterns. One of the main features of the model is that it includes an information variable, a negative feedback on the behavior of susceptible subjects, and a function that describes the role played by the infectious size in the information dynamics. Here we focus in the case of delayed information. By using suitable assumptions, we analyze the global stability of the endemic equilibrium point and disease-free equilibrium point. Our approach is applicable to global stability of the endemic equilibrium of the previously defined SIR and SIS models with feedback on behavior of susceptible subjects.


    1. Introduction

    Although mathematical models which describe the spread of infectious diseases are among the most successful application of mathematics in biology [14], they were classically derived by using methods of mean field theories in statistical mechanics. In other words, the agents, who are persons or animals, were approximated by means of particles. This constitutes maybe the main limit of classical approach in mathematical epidemiology: agents involved in the infectious spread are not particles, and their behavior, including the psychological aspects are important in shaping the population dynamics. It was the first stressed by Capasso and Serio [13] in seventies, but only in recent years it increasingly became clear that the role of human behavior and also misconducts (as the pseudo-rational exemption) ought thus to be included in some manner in the modeling of infectious disease spreading, which is triggering a large corpus of scientific research (see, just to name a few of contributions, [1,2,3,5,6,19,20,21,23,24,43] and the collective book [39]). Although there is a wide range of approaches [39], all these works explicitly include the feedback (FB) that the information about an infectious disease has on the agents' behavior and thus on the spreading of the target disease [2,15,18,43,45].

    A first type of FB is the one given by the influence of the information on the behavior of healthy subjects [9,18].

    A second type of FB is the pseudo-rational exemption which is defined as the family's decision to not vaccinate children because of a pseudo-rational comparison between the perceived risk of infection and the perceived risk of side effects caused by the vaccine [7,8,15,16,17].

    We shall focus on the first kind of feedback, which has first been introduced in the above mentioned paper [13] in a SIR epidemic model, where the force of infection was modeled as a decreasing function of the fraction of infectious subjects. In [18], the pioneering work by Capasso and Serio was extended to take into the account that the behavior inducing the reduction of the contact rate is in reality influenced by the information on the spread. As previously stressed in [18] the information does not only reflect the current state of the spread but also past states, both due to delays and to memory of past epidemics. In case of exponentially fading memory kernel in [18] it was shown that there is a unique endemic equilibrium point (EEP), and that it is locally stable. Some sharp conditions for the global stability of EEP were given in [9]. They use the so-called geometrical approach to global stability problem, originally developed by Li and Muldowney [34,35], which has gained some popularity in recent years (see, e.g., [10,11,12,36,37,40]).

    The investigation of the global stability of EEP has not only an intrinsic mathematical interest, but also a practical one, since verifying the global behavior of an EEP allows avoiding simulation for each specific set of parameters and initial conditions.

    We note that the idea of a population changing its behaviour in response to external stimuli has been explored by other research groups [41,42]. This developed formalism has been applied to a model SIR type epidemics [41] and a predator-prey model [42]. In [41], it is shown how one can model the response of the susceptible agents to the stimuli such as information about the epidemics as switches, and, thus, the authors obtained a model similar to the considered in [9,18] for simple switches, and also obtained a model with hysteresis and permanent memory of past epidemics using bistable switches.

    Here, we will introduce a SEIR model and define the effects of the information-related behavior on the force of infection (FoI) of a disease. We will investigate its global behavior by means of appropriate Lyapunov's functions. In addition, we will adopt similar methods to briefly assess the global behavior of the previously defined SIR and SIS models with FB on behavior of susceptible subjects [9,18].

    The paper is organized as follows. In the next section, the general SEIR epidemic model with contact rate as a function of the available information on the past disease prevalence is introduced. In Section 3 some preliminary properties of the SEIR model are presented. Section 4 is concerned with the global stability properties of the equilibria by means of Lyapunov functions. In Section 5, we shall extend the method of Lyapunov functions to SIR and SIS models with negative feedback. Further comments on the biological relevance of our results and on the particulars of the chosen approach are stated in Section 6, together with a few concluding remarks.


    2. Modeling the influence of the behavior on the contact rate

    In this section, we consider the following family of SEIR epidemic models for a non fatal disease in a constant population, with information-related changes in contact patterns:

    dSdt=μ(1S)β(M)SI,dEdt=β(M)SI(μ+σ)E,dIdt=σE(μ+ν)I, (1)

    and

    dRdt=νIμR,

    where S(t), E(t), I(t) and R(t) denote the subpopulations of susceptible, exposed, infectious and recovered with permanent immunity, respectively. There is no disease-related death. The natural death rate and birth rate are assumed to be equal, denoted by μ>0, and thus S(t)+E(t)+I(t)+R(t)=1 for all time (the constant population size). The parameter σ>0 denote the transfer rate between the exposed subpopulation and the infectious subpopulation. The parameter ν>0 describes the rate that the infectious subpopulation becomes recovered. The contact rate is a function of some information index M(t) summarizing the current and the past history of the disease prevalence: FoI(M(t))=β(M(t))I(t), where M(t) is related to the past prevalence through a suitable function Fna as follows [8,9,18]:

    M(t)=tg(I(τ))Fna(tτ)dτ.

    The term Fna is a delaying kernel. Generally, Fna is the density function for a gamma distribution:

    Fna(u)=an+1unn!eau,

    where a> 0 is a constant and n0 is an integer. The average delay is defined by τ=(n+1)/a, and n is called the order of the delay kernel.

    Throughout this paper, we use the kernel with n=0, that is,

    F0a(u)=aeau.

    This kernel is called the weak exponential delay kernel or the exponentially fading memory kernel because it pays a declining weight to the past. The parameter ''a" assumes the biological meaning of inverse of the average delay of the collected information on the disease, as well as the average length of the historical memory concerning the disease in study. Such kernel was also used in another infectious disease models with negative feedback [7,8,9,16,17,18]. In this case we have,

    dMdt=ag(I)aM. (2)

    The function g(I) describes the role played by the infectious size in the information dynamics.

    From the latter equality and the equations of system (1) we obtain the SEIR model with information-dependent contact rate:

    dSdt=μ(1S)β(M)SI,dEdt=β(M)SI(μ+σ)E,dIdt=σE(μ+ν)I,dMdt=ag(I)aM. (3)

    Since R(t) does not appear in the equations of system (1), it is enough to consider only the equations for S(t), E(t) and I(t).

    The initial condition of ordinary differential equations (3) is given as

    S(0)>0, E(0)0,  I(0)>0,  M(0)0. (4)

    Finally, we shall make the following assumptions on the functions β(M) and g(I).

    (H1): β(0)>0; β(M)>0 for M>0.

    (H2): β(M)<0 for M>0.

    (H3): g(0)=0; g(I)>0 for I>0.

    (H4): g(I)>0 for I>0.

    It is clear from assumptions (H1) and (H2) that system (3) is an epidemic system with negative feedback. The following choice of a negative feedback is proposed in [9,18]: as a rational function β(M)=β0/(1+pM), where β0 and p are positive constants.

    As for g(I), the following choice is proposed in [9,18]: as a function of prevalence of infection g(I)=wI, where w is a parameter subsuming aspects such as pathogenicity [3]; or as a saturating function g(I)=wI/(1+qI), where w and q are positive constants.


    3. Preliminaries

    The dynamics of infectious disease crucially depend on the basic reproductive number R0. Following the definition of the basic reproductive number given by van den Driessche and Watmough [44], the basic reproductive number for system (3) is presented as

    R0=σβ(0)(μ+σ)(μ+ν). (5)

    Direct calculation shows that system (3) has two possible equilibrium points in the non-negative orthant R4+={(S,E,I,M)R4:S0,E0,I0,M0}: the disease-free equilibrium point P0=(1,0,0,0), and a endemic equilibrium point P=(S,E,I,M) where S=μ/(μ+Iβ(g(I))), E=(μ+ν)I/σ, M=g(I) and I is the solution of

    β(g(I))μμ+Iβ(g(I))(μ+ν)(μ+σ)σ=0. (6)

    The number of solutions of equation (6) can be analyzed geometrically through determining the points of intersection of the graphs of functions F1(I) and F2(I) in the first quadrant. The functions F1(I) and F2(I) are defined as

    F1(I)=μσ(μ+ν)(μ+σ)β(g(I))(μ+Iβ(g(I))),F2(I)=1.

    Using assumptions (H1) and (H3), and the expression of R0 in (5), we obtain

    F1(0)=σβ(0)(μ+σ)(μ+ν)=R0. (7)

    We calculate the derivative of F1(I)

    F1(I)=μσ(μ+ν)(μ+σ)μβ(g(I))g(I)β2(g(I))(μ+Iβ(g(I)))2.

    By (H2) and (H4) holds, it is easy to see that F1(I)0 as I+, and that the function F1(I) is decreasing (F1(I)<0). Note that if R0=F1(0)>1, then the graphs of functions F1(I) and F2(I) intersect at a single point in the first quadrant. This result indicates that if an EEP exists and it is unique. Note that an epidemiologically meaningful P does not exist if R0=F1(0)<1, and it becomes disease-free equilibrium point P0 when R0=F1(0)=1.

    We summarize the results for the existence of equilibrium points in the following theorem.

    Theorem 3.1. Suppose that the functions β(M) and g(I) satisfy the conditions (H1), (H2), (H3) and (H4). System (3) always has the disease-free equilibrium point P0=(1,0,0,0). If R0>1, there is a unique endemic equilibrium point P=(S,E,I,M).

    Finally, we shall show that the system (3) is bounded.

    Theorem 3.2. Let (S(t),E(t),I(t),M(t)) be the solution of system (3) satisfying initial conditions (4). Then S(t), E(t), I(t), and M(t) are all bounded for all t>0 at which the solution exists.

    Proof. From the first equation of (3), we obtain

    dSdtμ(1S),

    and thus lim suptS1. Adding the first three equations of (3), we get

    ddt(S+E+I)=μ(1SEI)νIμ(1SEI).

    By a standard comparison theorem, we can conclude that lim supt(S+E+I)1. This relation and the fourth equation of (3) imply

    dMdt=ag(I)aMag(1)aM,

    and thus lim suptMg(1). Therefore, S(t), E(t), I(t), and M(t) are all bounded for all t>0. This completes the proof.

    The dynamics of system (3) can be analyzed in the following bounded feasible region:

    Γ={(S,E,I,M)R4+:S, E, I0, S1, S+E+I1, 0Mg(1)}.

    Furthermore, the region Γ is positively invariant with respect to model (3).


    4. Global stability of equilibrium points

    In this section, we shall use the following Lyapunov function for systems with negative feedback:

    U(M)=MMMMβ(η)β(M)dη. (8)

    Using assumptions (H1) and (H2), it is easy to verify that the function U(M) has a global minimum at M=M and satisfies U(M)U(M) with equal sign taken when M=M.

    The function U(M) is introduced to prove the global stability of the positive equilibrium in virus dynamics models with nonlytic immune response [46,48].

    We shall use the family of Volterra-type Lyapunov function

    V(x)=x1lnx. (9)

    Thus, the function V(x) has a global minimum at x=1 and satisfies V(x)V(1) the equality case being x=1.

    The Volterra-type function V(x) is extensively used to demonstrate the global stability of the equilibria of Lotka-Volterra systems [22], infectious disease models [4,14,25,26] and virus dynamics models [27,46,47,48]. The function V(x) was originally discovered by Vito Volterra as the first integral of classic predator-prey model.

    We inspired by the Lyapunov function techniques that was developed during last decade [28,29,30,31,32] and particularly by the recent works [46,48], we will determine the conditions for the global stability of the endemic equilibrium point of the epidemic system (3).

    Remark 1. The functions U and V can be generalized to the form

    H(x,f)=xx(1f(x)f(η))dη.

    Volterra-type function is H(x,x) and the function U is H(M,1β).

    In Section 2, we assume that β(M) satisfies assumptions (H1) and (H2). We also make the following assumption about the negative feedback on behavior of healthy subjects β(M).

    (H5): (Mβ(M))>0 for M>0.

    This assumption is a technical one, required to prove Lemma 4.1 (and the Theorems 4.3 and 5.1).

    The following lemmas are used in the proof of the global stability of the EEP.

    Lemma 4.1 (See [48]) Let the hypotheses (H2) and (H5) hold, then

    (β(M)β(M)1)(Mβ(M)Mβ(M)1)<0

    for all M>0 and MM.


    4.1. Disease-free equilibrium point

    In the absence of the infectious disease, the system has a unique disease-free equilibrium point P0. By constructing a Lyapunov function, we can prove the global stability of the disease-free equilibrium point P0 when the basic reproductive number is less than or equal to unity.

    Theorem 4.2. Suppose that conditions (H1)-(H4) are satisfied. If R01, then the disease-free equilibrium point P0 of (3) is globally asymptotically stable in Γ.

    Proof. Let Ws=V(S). Calculating the time derivative of Ws(S), we obtain

    ddt[V(S)]=(11S)[μ(1S)β(M)SI],=μ(2S1S)β(M)SI+β(M)I. (10)

    Let Wei=E+(μ+σ)σI. Next, we obtain

    ddt[E+(μ+σ)σI]=β(M)SI(μ+σ)E+(μ+σ)σ[σE(μ+ν)I],=β(M)SI(μ+σ)(μ+ν)σI. (11)

    Now, define the Lyapunov function W:{(S,E,I,M)Γ:S>0}R by

    W(S,E,I,M)=cWs+cWei, (12)

    where c is a positive constant. Finally, adding (10) and (11), we obtain the derivative of W along the solutions of system (3):

    dWdt=cdWsdt+cdWeidt,=cμ(2S1S)+cβ(M)Ic(μ+σ)(μ+ν)σI.

    Using assumptions (H2), we obtain β(M)<β(0)

    dWdt<cμ(2S1S)c(μ+σ)(μ+ν)σ[1σβ(0)(μ+σ)(μ+ν)]I,<cμ[V(S)+V(1S)]c(μ+σ)(μ+ν)σ[1R0]I.

    V(S) and V(1S) are Volterra-type functions. These functions are positive definite. Thus, R01 implies that dW/dt0. If dW/dt=0 then S=1 and I=0. Hence, W is a Lyapunov function on Γ. Thus, (S,E,I)(1,0,0) as t. Using I=0 in the last equation of (3) shows that M0 as t. Therefore, it follows from the LaSalle's Invariance Principle [33], that every solution of the equations in the model (3), with initial conditions in Γ, approaches P0 as t. This completes the proof.


    4.2. Endemic equilibrium point

    We get the global stability of the EEP for the special case g(I)=wI.

    Theorem 4.3. Suppose that conditions (H1)-(H5) are satisfied. Assume that g(I)=wI. If R0>1 then the unique endemic equilibrium point P of system (3) is globally asymptotically stable in the interior of the feasible region Γ.

    Proof. At endemic equilibrium point, we have

    μ=μS+β(M)SI, (13)
    μ+σ=β(M)SIE, (14)
    μ+ν=σEI, (15)
    M=wI. (16)

    Let Ls=SV(SS). By using (13), we have

    ddt[SV(SS)]=(1SS)[μμSβ(M)SI],=(1SS)[μS(1SS)+β(M)SI(1β(M)β(M)SISI)],=μS(2SSSS)+β(M)SI(1β(M)β(M)SISISS+β(M)β(M)II). (17)

    Define Le=EV(EE). Using (14), we have

    ddt[EV(EE)]=(1EE)[β(M)SI(μ+σ)E]=β(M)SI(1EE)[β(M)β(M)SISIEE]=β(M)SI[β(M)β(M)SISIEEβ(M)β(M)SIESIE+1]. (18)

    Let Li=IV(II). By using (15), we have

    ddt[IV(II)]=(1II)[σE(μ+ν)I],=σE(1II)[EEII],=σE[EEIIIEIE+1]. (19)

    Let Lm=U(M). Here we used (16).

    ddt[U(M)]=(1β(M)β(M))[awIaM],=awI(IIMMβ(M)β(M)II+β(M)β(M)MM). (20)

    Let us consider the Lyapunov function

    L(S,E,I,M)=kLs+kLe+kβ(M)SIσELi+kβ(M)SawLm, (21)

    where k is a positive constant. Computing the derivative of (21) along the solutions of system (3), we obtain

    dLdt=kdLsdt+kdLedt+kβ(M)SIσEdLidt+kβ(M)SawdLmdt. (22)

    Substituting (17)-(20) in (22), we obtain

    dLdt=kμS(2SSSS)+kβ(M)SI(3SSβ(M)β(M)SIESIEIEIE)+kβ(M)SI(MM+β(M)β(M)MM),=kμS(2SSSS)+kβ(M)SI(4SSβ(M)β(M)SIESIEIEIEβ(M)β(M))+kβ(M)SI(β(M)β(M)MM+β(M)β(M)MM1),=kμS[V(SS)+V(SS)]kβ(M)SI[V(SS)+V(β(M)β(M)SIESIE)+V(IEIE)+V(β(M)β(M))]+kβ(M)SI(β(M)β(M)1)(Mβ(M)Mβ(M)1)β(M)β(M). (23)

    The terms between the brackets, in the expression (23), are Volterra-type functions. These functions are positive definite.

    By Lemma 4.1,

    (β(M)β(M)1)(Mβ(M)Mβ(M)1)<0

    holds for all M>0.

    It is easy to see that dL/dt is negative in the interior of Γ. We have dL/dt=0 if and only if SS=1, IEIE=1 and MM=1 holds. The largest compact invariant set in {(S,E,I,M)Γ:dL/dt=0} is the singleton {P}, where P is the EEP. By LaSalle's invariance principle [33] then implies that P is globally asymptotically stable in the interior of Γ. This completes the proof.


    5. SIR and SIS epidemic models

    Our approach is applicable for SIR and SIS epidemic models with an information-dependent contact rate that have been studied in [9,18]. We give conditions for global stability of the endemic equilibrium point, whenever it exists.

    First, SIR model is given by the following system of ordinary differential equations [9,18]:

    dSdt=μμSβ(M)SI,dIdt=β(M)SI(μ+ν)I,dMdt=ag(I)aM, (24)

    and the equation of the recovered subpopulation is given by dR(t)/dt=νIμR. The states variables and parameters are the same as in the SEIR model. For this model, the feasible region is given by

    Ω={(S,I,M)R3+:0S+I1, 0Mg(1)},

    and the basic reproductive number is still given by

    RSIR0=β(0)μ+ν. (25)

    For SIR model with special function g(I)=wI, we prove the global stability of EEP in the following theorem.

    Theorem 5.1. Suppose that conditions (H1)-(H6) are satisfied. Assume that g(I)=wI. If RSIR0>1 then a unique endemic equilibrium P=(S,I,M) of system (24) is globally asymptotically stable in the interior of Ω.

    Proof. The proof is similar to the proof of Theorem 4.3, but with the following Lyapunov function of the form

    L(S,I,M)=kLs+kLi+kβ(M)SawLm, (26)

    where k>0. The functions Ls, Li and Lm are previously defined in subsection 4.2.

    Define Li=IV(II). By using (μ+ν)=β(M)S, we have

    ddt[IV(II)]=(1II)[β(M)SI(μ+ν)I]=β(M)SI(1II)[β(M)β(M)SISIII]=β(M)SI[β(M)β(M)SISIIIβ(M)β(M)SS+1]. (27)

    The derivative of (26) along solution of (24) is given by

    dLdt=kdLsdt+kdLidt+kβ(M)SawdLmdt.

    By using (17), (27) and (20), we obtain

    dLdt=kμS(2SSSS)+kβ(M)SI(3SSβ(M)β(M)SSβ(M)β(M))+kβ(M)SI(β(M)β(M)1)(MMβ(M)β(M)),=kμS[V(SS)+V(SS)]kβ(M)SI[V(SS)+V(β(M)β(M)SS)+V(β(M)β(M))]+kβ(M)SI(β(M)β(M)1)(Mβ(M)Mβ(M)1)β(M)β(M)0.

    Clearly, dL/dt0, the conclusions are similar to the proof of Theorem 4.3.

    Second, the differential equations for the SIS model are [9]:

    dSdt=μμSβ(M)SI+δI,dIdt=β(M)SI(μ+δ)I,dMdt=ag(I)aM. (28)

    Here the parameter δ>0 is the recovery rate. The other parameters and variables are the same as in the previous models.

    To analyze the global stability of the EEP, first of all, we reduce the model to a two-dimensional model as follows. The system (28) is subject to the restriction S(t)+I(t)=1, and using S(t)=1I(t) in the model, we can eliminate S(t) from the equations. This substitution gives the simpler model:

    dIdt=I(β(M)(1I)(μ+δ)),dMdt=ag(I)aM. (29)

    The feasible region of system (29) is given by

    Σ={(I,M)R2+:0I1, 0Mg(1)},

    and the corresponding basic reproductive number is given by

    RSIS0=β(0)μ+δ. (30)

    For SIS model, we prove the global stability of EEP, in the following theorem.

    Theorem 5.2. Suppose that conditions (H1)-(H4) are satisfied. If RSIS0>1 then a unique endemic equilibrium P=(I,M) of system (29) is globally asymptotically stable in the interior of Σ.

    Proof. Consider the Lyapunov function

    L(I,M)=kIIg(η)g(I)ηdη+k(μ+δ)aLm, (31)

    where k is a positive constant. The function Lm is previously defined in subsection 4.2.

    By using 1=I+(μ+δ)β(M), we have

    ddt[IIg(η)g(I)ηdη]=I(g(I)g(I)I)[(μ+δ)(β(M)β(M)1)+β(M)I(1II)],=(μ+δ)(g(I)g(I))(β(M)β(M)1)+β(M)I(g(I)g(I))(1II). (32)

    Let Lm=U(M). We have

    ddt[U(M)]=(1β(M)β(M))[ag(I)aM],=(1β(M)β(M))[a(g(I)g(I))a(MM)]. (33)

    The time derivative of (31) along the solutions of system (29),

    dLdt=kddt[IIg(η)g(I)ηdη]+k(μ+δ)adLmdt.

    By using (32) and (33), we obtain

    dLdt=k(μ+δ)(g(I)g(I))(β(M)β(M)1)+kβ(M)I(g(I)g(I))(1II)+k(μ+δ)(1β(M)β(M))(g(I)g(I))k(μ+δ)(1β(M)β(M))(MM),=kβ(M)Ig(I)(1II)(1g(I)g(I))+k(μ+δ)M(1β(M)β(M))(1MM).

    Furthermore,

    (1II)(1g(I)g(I))0,

    since for an increasing function g(I), g(I)g(I) when II and g(I)g(I) when II. Also, for a decreasing function β(M) ensures that

    (1β(M)β(M))(1MM)0,

    with equality iff M=M.

    Hence dL/dt is negative definite. By the [38], then implies that E is globally asymptotically stable in the interior of Σ.

    Remark 2. When g(I)=wI the Lyapunov function IIg(η)g(I)ηdη is Volterra-type function.


    6. Concluding remarks

    We extended in this work the research of the dynamic implications of information-related changes in contact patterns for SEIR diseases.

    First, we study a SEIR model (3) with an information variable M, a negative feedback on the behavior of susceptible subjects β(M), and a function that describes the role played by the infectious size in the information dynamics g(I). This system is the case of the exponentially fading memory kernel with Tdelay=a1, which is the average delay of the collected information on the disease. We have identified the basic reproductive number, and we analyzed the global stability of both endemic equilibrium point P and the disease-free equilibrium point P0. For the special function g(I)=wI, we have shown the global asymptotic stability of the EEP. The results in this paper show the case of the exponentially fading memory kernel that does not affect the global asymptotic properties of the SEIR model (3).

    Second, an epidemiologically important consequence of the existence and uniqueness of an endemic equilibrium of system (3) is the analysis of the inhibitory effects of the information-related behavior on the force of infection. The effects of the information are to (ⅰ) increase the equilibrium number of susceptible, and (ⅱ) reduce the equilibrium numbers of infected and exposed. We showed that the coordinates of EEP can be controlled.

    Third, we extended our technique of Lyapunov functions to SIR and SIS models with contact rate as a function of prevalence index developed in [9,18]. Our global stability conditions improve other recent results for these previous models [9]. For SIR model (24) with special function g(I)=wI, we obtained the global stability conditions of EEP under the technical requirement (H5). For SIS model (29), we obtained the global stability of EEP without the technical condition (H5). Clearly, the non-monotone function β(M)=β0(1+pM2)1 and the negative exponential function β(M)=β0epM satisfy the stability conditions of Theorem 5.2. The low dimension of the SIS model facilitates global stability studies.

    Finally, the results of this work indicate that our method of Lyapunov functions construction and suitable estimates of the derivatives of the Lyapunov functions, can be especially useful for higher-dimension systems with negative feedback.


    Acknowledgments

    We would like to thank the anonymous referees for their valuable comments and suggestions.


    [1] [ C. Auld, Choices, beliefs, and infectious disease dynamics, J. Health. Econ., 22 (2003): 361-377.
    [2] [ C. T. Bauch,D. J. D. Earn, Vaccination and the theory of games, Proc. Natl. Acad. Sci. U S A., 101 (2004): 13391-13394.
    [3] [ C. T. Bauch, Imitation dynamics predict vaccinating behavior, Proc. R. Soc. London B, 272 (2005): 1669-1675.
    [4] [ E. Beretta,V. Capasso, On the general structure of epidemic systems. Global asymptotic stability, Comput. Math. Appl., Part A, 12 (1986): 677-694.
    [5] [ S. Bhattacharyya,C. T. Bauch, ''Wait and see'' vaccinating behaviour during a pandemic: A game theoretic analysis, Vaccine, 29 (2011): 5519-5525.
    [6] [ D. L. Brito,E. Sheshinski,M. D. Intriligator, Externalities and compulsory vaccinations, J. Public Econ., 45 (1991): 69-90.
    [7] [ B. Buonomo,A. d'Onofrio,D. Lacitignola, Global stability of an SIR epidemic model with information dependent vaccination, Math. Biosci., 216 (2008): 9-16.
    [8] [ B. Buonomo,A. d'Onofrio,D. Lacitignola, Rational exemption to vaccination for non-fatal SIS diseases: globally stable and oscillatory endemicity, Math. Biosci. Eng., 7 (2010): 561-578.
    [9] [ B. Buonomo,A. d'Onofrio,D. Lacitignola, Globally stable endemicity for infectious diseases with information-related changes in contact patterns, Appl. Math. Lett., 25 (2012): 1056-1060.
    [10] [ B. Buonomo,D. Lacitignola, On the use of the geometric approach to global stability for three dimensional ODE systems: a bilinear case, J. Math. Anal. Appl., 348 (2008): 255-266.
    [11] [ B. Buonomo,C. Vargas-De-León, Global stability for an HIV-1 infection model including an eclipse stage of infected cells, J. Math. Anal. Appl., 385 (2012): 709-720.
    [12] [ B. Buonomo,C. Vargas-De-León, Stability and bifurcation analysis of a vector-bias model of malaria transmission, Math. Biosci., 242 (2013): 59-67.
    [13] [ V. Capasso,G. Serio, A generalization of the Kermack-McKendrick deterministic epidemic model, Math.Biosci., 42 (1978): 43-61.
    [14] [ V. Capasso, null, Mathematical Structures of Epidemic Systems, 2 printing, Springer-Verlag, Berlin, 2008.
    [15] [ A. d'Onofrio,P. Manfredi,E. Salinelli, Vaccinating behaviour, information, and the dynamics of SIR vaccine preventable diseases, Theor. Popul. Biol., 71 (2007): 301-317.
    [16] [ A. d'Onofrio,P. Manfredi,E. Salinelli, Bifurcation threshold in an SIR model with information-dependent vaccination, Math. Model. Nat. Phenom., 2 (2007): 23-38.
    [17] [ A. d'Onofrio,P. Manfredi,E. Salinelli, Fatal SIR diseases and rational exemption to vaccination, Math. Med. Biol., 25 (2008): 337-357.
    [18] [ A. d'Onofrio,P. Manfredi, Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases, J. Theor. Biol., 256 (2009): 473-478.
    [19] [ P. E. M. Fine,J. A. Clarkson, Individual versus public priorities in the determination of optimal vaccination policies, Am. J. Epidemiol., 124 (1986): 1012-1020.
    [20] [ S. Funk,M. Salathe,V. A. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases: A review, J. Royal Soc. Interface, 7 (2010): 1247-1256.
    [21] [ P. Y. Geoffard,T. Philipson, Disease eradication: Private versus public vaccination, Am. Econ. Rev., 87 (1997): 222-230.
    [22] [ B. S. Goh, Global stability in two species interactions, J. Math. Biol., 3 (1976): 313-318.
    [23] [ V. Hatzopoulos,M. Taylor,P. L. Simon,I. Z. Kiss, Multiple sources and routes of information transmission: Implications for epidemic dynamics, Math. Biosci., 231 (2011): 197-209.
    [24] [ I. Z. Kiss,J. Cassell,M. Recker,P. L. Simon, The impact of information transmission on epidemic outbreaks, Math. Biosci., 225 (2010): 1-10.
    [25] [ A. Korobeinikov,G. C. Wake, Lyapunov functions and global stability for SIR, SIRS, and SIS epidemiological models, Appl. Math. Lett., 15 (2002): 955-960.
    [26] [ A. Korobeinikov, Lyapunov functions and global properties for SEIR and SEIS epidemic models, Math. Med. Biol., 21 (2004): 75-83.
    [27] [ A. Korobeinikov, Global properties of basic virus dynamics models, Bull. Math. Biol., 66 (2004): 879-883.
    [28] [ A. Korobeinikov,P. K. Maini, Non-linear incidence and stability of infectious disease models, Math. Med. Biol., 22 (2005): 113-128.
    [29] [ A. Korobeinikov, Lyapunov functions and global stability for SIR and SIRS epidemiological models with non-linear transmission, Bull. Math. Biol., 68 (2006): 615-626.
    [30] [ A. Korobeinikov, Global properties of infectious disease models with nonlinear incidence, Bull. Math. Biol., 69 (2007): 1871-1886.
    [31] [ A. Korobeinikov, Global asymptotic properties of virus dynamics models with dose-dependent parasite reproduction and virulence, and nonlinear incidence rate, Math. Med. Biol., 26 (2009): 225-239.
    [32] [ A. Korobeinikov, Stability of ecosystem: Global properties of a general prey-predator model, Math. Med. Biol., 26 (2009): 309-321.
    [33] [ J. La Salle, null, Stability by Liapunov's Direct Method with Applications, 1 printing, Academic Press, New York-London, 1961.
    [34] [ M. Y. Li,J. S. Muldowney, Global stability for the SEIR model in epidemiology, Math. Biosci., 125 (1995): 155-164.
    [35] [ M. Y. Li,J. S. Muldowney, A geometric approach to global-stability problems, SIAM J. Math. Anal., 27 (1996): 1070-1083.
    [36] [ M. Y. Li,L. Wang, Backward bifurcation in a mathematical model for HIV infection in vivo with anti-retroviral treatment, Nonlinear Anal. Real World Appl., 17 (2014): 147-160.
    [37] [ G. Lu,Z. Lu, Geometric approach for global asymptotic stability of three-dimensional Lotka-Volterra systems, J. Math. Anal. Appl., 389 (2012): 591-596.
    [38] [ A. M. Lyapunov, null, The General Problem of the Stability of Motion, Taylor and Francis, London, 1992.
    [39] [ P. Manfredi,A. d'Onofrio, null, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases, Springer-Verlag, New York, 1992.
    [40] [ L. Pei,J. Zhang, Losing weight and elimination of weight cycling by the geometric approach to global-stability problem, Nonlinear Anal. RWA, 14 (2013): 1865-1870.
    [41] [ A. Pimenov,T. C. Kelly,A. Korobeinikov,M. J. A. O'Callaghan,A. V. Pokrovskii,D. Rachinskii, Memory effects in population dynamics: Spread of infectious disease as a case study, Math. Model. Nat. Phenom., 7 (2012): 204-226.
    [42] [ A. Pimenov,T. C. Kelly,A. Korobeinikov,M. J. A. O'Callaghan,D. Rachinskii, Adaptive behaviour and multiple equilibrium states in a predator-prey model, Theor. Popul. Biol., 101 (2015): 24-30.
    [43] [ T. C. Reluga,C. T. Bauch,A. P. Galvani, Evolving public perceptions and stability in vaccine uptake, Math. Biosci., 204 (2006): 185-198.
    [44] [ P. van den Driessche,J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002): 29-48.
    [45] [ R. Vardavas,R. Breban,S. Blower, Can influenza epidemics be prevented by voluntary vaccination?, PLoS Comp. Biol., 3 (2007): e85.
    [46] [ C. Vargas-De-León,A. Korobeinikov, Global stability of a population dynamics model with inhibition and negative feedback, Math. Med. Biol., 30 (2013): 65-72.
    [47] [ C. Vargas-De-León, Global properties for virus dynamics model with mitotic transmission and intracellular delay, J. Math. Anal. Appl., 381 (2011): 884-890.
    [48] [ C. Vargas-De-León, Global properties for a virus dynamics model with lytic and nonlytic immune responses and nonlinear immune attack rates, J. Biol. Syst., 22 (2014): 449-462.
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