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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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.
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(1−S)−β(M)SI,dEdt=β(M)SI−(μ+σ)E,dIdt=σE−(μ+ν)I, | (1) |
and
dRdt=νI−μR, |
where
M(t)=∫t−∞g(I(τ))Fna(t−τ)dτ. |
The term
Fna(u)=an+1unn!e−au, |
where
Throughout this paper, we use the kernel with
F0a(u)=ae−au. |
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
From the latter equality and the equations of system (1) we obtain the SEIR model with information-dependent contact rate:
dSdt=μ(1−S)−β(M)SI,dEdt=β(M)SI−(μ+σ)E,dIdt=σE−(μ+ν)I,dMdt=ag(I)−aM. | (3) |
Since
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
(H1):
(H2):
(H3):
(H4):
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
As for
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
β(g(I∗))μμ+I∗β(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)=μσ(μ+ν)(μ+σ)β(g(I))(μ+I⋅β(g(I))),F2(I)=1. |
Using assumptions (H1) and (H3), and the expression of
F1(0)=σβ(0)(μ+σ)(μ+ν)=R0. | (7) |
We calculate the derivative of
F′1(I)=μσ(μ+ν)(μ+σ)μβ′(g(I))⋅g′(I)−β2(g(I))(μ+I⋅β(g(I)))2. |
By (H2) and (H4) holds, it is easy to see that
We summarize the results for the existence of equilibrium points in the following theorem.
Theorem 3.1. Suppose that the functions
Finally, we shall show that the system (3) is bounded.
Theorem 3.2. Let
Proof. From the first equation of (3), we obtain
dSdt≤μ(1−S), |
and thus
ddt(S+E+I)=μ(1−S−E−I)−νI≤μ(1−S−E−I). |
By a standard comparison theorem, we can conclude that
dMdt=ag(I)−aM≤ag(1)−aM, |
and thus
The dynamics of system (3) can be analyzed in the following bounded feasible region:
Γ={(S,E,I,M)∈R4+:S, E, I≥0, S≤1, S+E+I≤1, 0≤M≤g(1)}. |
Furthermore, the region
In this section, we shall use the following Lyapunov function for systems with negative feedback:
U(M)=M−M∗−∫MM∗β(η)β(M∗)dη. | (8) |
Using assumptions (H1) and (H2), it is easy to verify that the function
The function
We shall use the family of Volterra-type Lyapunov function
V(x)=x−1−lnx. | (9) |
Thus, the function
The Volterra-type function
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∗(1−f(x∗)f(η))dη. |
Volterra-type function is
In Section 2, we assume that
(H5):
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)M∗β(M∗)−1)<0 |
for all
In the absence of the infectious disease, the system has a unique disease-free equilibrium point
Theorem 4.2. Suppose that conditions (H1)-(H4) are satisfied. If
Proof. Let
ddt[V(S)]=(1−1S)[μ(1−S)−β(M)SI],=μ(2−S−1S)−β(M)SI+β(M)I. | (10) |
Let
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)=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(2−S−1S)+cβ(M)I−c(μ+σ)(μ+ν)σI. |
Using assumptions (H2), we obtain
dWdt<cμ(2−S−1S)−c(μ+σ)(μ+ν)σ[1−σβ(0)(μ+σ)(μ+ν)]I,<−cμ[V(S)+V(1S)]−c(μ+σ)(μ+ν)σ[1−R0]I. |
We get the global stability of the EEP for the special case
Theorem 4.3. Suppose that conditions (H1)-(H5) are satisfied. Assume that
Proof. At endemic equilibrium point, we have
μ=μS∗+β(M∗)S∗I∗, | (13) |
μ+σ=β(M∗)S∗I∗E∗, | (14) |
μ+ν=σE∗I∗, | (15) |
M∗=wI∗. | (16) |
Let
ddt[S∗V(SS∗)]=(1−S∗S)[μ−μS−β(M)SI],=(1−S∗S)[μS∗(1−SS∗)+β(M∗)S∗I∗(1−β(M)β(M∗)SIS∗I∗)],=μS∗(2−S∗S−SS∗)+β(M∗)S∗I∗(1−β(M)β(M∗)SIS∗I∗−S∗S+β(M)β(M∗)II∗). | (17) |
Define
ddt[E∗V(EE∗)]=(1−E∗E)[β(M)SI−(μ+σ)E]=β(M∗)S∗I∗(1−E∗E)[β(M)β(M∗)SIS∗I∗−EE∗]=β(M∗)S∗I∗[β(M)β(M∗)SIS∗I∗−EE∗−β(M)β(M∗)SIE∗S∗I∗E+1]. | (18) |
Let
ddt[I∗V(II∗)]=(1−I∗I)[σE−(μ+ν)I],=σE∗(1−I∗I)[EE∗−II∗],=σE∗[EE∗−II∗−I∗EIE∗+1]. | (19) |
Let
ddt[U(M)]=(1−β(M)β(M∗))[awI−aM],=awI∗(II∗−MM∗−β(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∗)S∗I∗σE∗Li+kβ(M∗)S∗awLm, | (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∗)S∗I∗σE∗dLidt+kβ(M∗)S∗awdLmdt. | (22) |
Substituting (17)-(20) in (22), we obtain
dLdt=kμS∗(2−S∗S−SS∗)+kβ(M∗)S∗I∗(3−S∗S−β(M)β(M∗)SIE∗S∗I∗E−I∗EIE∗)+kβ(M∗)S∗I∗(−MM∗+β(M)β(M∗)MM∗),=kμS∗(2−S∗S−SS∗)+kβ(M∗)S∗I∗(4−S∗S−β(M)β(M∗)SIE∗S∗I∗E−I∗EIE∗−β(M∗)β(M))+kβ(M∗)S∗I∗(β(M∗)β(M)−MM∗+β(M)β(M∗)MM∗−1),=−kμS∗[V(SS∗)+V(S∗S)]−kβ(M∗)S∗I∗[V(S∗S)+V(β(M)β(M∗)SIE∗S∗I∗E)+V(I∗EIE∗)+V(β(M∗)β(M))]+kβ(M∗)S∗I∗(β(M)β(M∗)−1)(Mβ(M)M∗β(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)M∗β(M∗)−1)<0 |
holds for all
It is easy to see that
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=μ−μS−β(M)SI,dIdt=β(M)SI−(μ+ν)I,dMdt=ag(I)−aM, | (24) |
and the equation of the recovered subpopulation is given by
Ω={(S,I,M)∈R3+:0≤S+I≤1, 0≤M≤g(1)}, |
and the basic reproductive number is still given by
RSIR0=β(0)μ+ν. | (25) |
For SIR model with special function
Theorem 5.1. Suppose that conditions (H1)-(H6) are satisfied. Assume that
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∗)S∗awLm, | (26) |
where
Define
ddt[I∗V(II∗)]=(1−I∗I)[β(M)SI−(μ+ν)I]=β(M∗)S∗I∗(1−I∗I)[β(M)β(M∗)SIS∗I∗−II∗]=β(M∗)S∗I∗[β(M)β(M∗)SIS∗I∗−II∗−β(M)β(M∗)SS∗+1]. | (27) |
The derivative of (26) along solution of (24) is given by
dLdt=kdLsdt+kdLidt+kβ(M∗)S∗awdLmdt. |
By using (17), (27) and (20), we obtain
dLdt=kμS∗(2−S∗S−SS∗)+kβ(M∗)S∗I∗(3−S∗S−β(M)β(M∗)SS∗−β(M∗)β(M))+kβ(M∗)S∗I∗(β(M)β(M∗)−1)(MM∗−β(M∗)β(M)),=−kμS∗[V(SS∗)+V(S∗S)]−kβ(M∗)S∗I∗[V(S∗S)+V(β(M)β(M∗)SS∗)+V(β(M∗)β(M))]+kβ(M∗)S∗I∗(β(M)β(M∗)−1)(Mβ(M)M∗β(M∗)−1)β(M∗)β(M)≤0. |
Clearly,
Second, the differential equations for the SIS model are [9]:
dSdt=μ−μS−β(M)SI+δI,dIdt=β(M)SI−(μ+δ)I,dMdt=ag(I)−aM. | (28) |
Here the parameter
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
dIdt=I(β(M)(1−I)−(μ+δ)),dMdt=ag(I)−aM. | (29) |
The feasible region of system (29) is given by
Σ={(I,M)∈R2+:0≤I≤1, 0≤M≤g(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
Proof. Consider the Lyapunov function
L(I,M)=k∫II∗g(η)−g(I∗)ηdη+k(μ+δ)aLm, | (31) |
where k is a positive constant. The function
By using
ddt[∫II∗g(η)−g(I∗)ηdη]=I(g(I)−g(I∗)I)[(μ+δ)(β(M)β(M∗)−1)+β(M)I∗(1−II∗)],=(μ+δ)(g(I)−g(I∗))(β(M)β(M∗)−1)+β(M)I∗(g(I)−g(I∗))(1−II∗). | (32) |
Let
ddt[U(M)]=(1−β(M)β(M∗))[ag(I)−aM],=(1−β(M)β(M∗))[a(g(I)−g(I∗))−a(M−M∗)]. | (33) |
The time derivative of (31) along the solutions of system (29),
dLdt=kddt[∫II∗g(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∗))(1−II∗)+k(μ+δ)(1−β(M)β(M∗))(g(I)−g(I∗))−k(μ+δ)(1−β(M)β(M∗))(M−M∗),=kβ(M)I∗g(I)(1−II∗)(1−g(I∗)g(I))+k(μ+δ)M∗(1−β(M)β(M∗))(1−MM∗). |
Furthermore,
(1−II∗)(1−g(I∗)g(I))≤0, |
since for an increasing function
(1−β(M)β(M∗))(1−MM∗)≤0, |
with equality iff
Hence
Remark 2. When
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
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
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.
We would like to thank the anonymous referees for their valuable comments and suggestions.
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