- Open Access
The threshold of a stochastic SIQS epidemic model
© Pang et al.; licensee Springer. 2014
- Received: 29 September 2014
- Accepted: 1 December 2014
- Published: 22 December 2014
In this paper, we discuss the dynamics of a stochastic SIQS epidemic model. When the noise is large, the infective decays exponentially to zero regardless of the magnitude of . When the noise is small, sufficient conditions for extinction exponentially and persistence in the mean are established. The results are illustrated by computer simulations.
MSC:34F05, 37H10, 60H10, 92D25, 92D30.
- stochastic SIQS epidemic model
One intervention procedure to control the spread of infectious diseases is to isolate some infectives, in order to reduce transmissions of the infection to susceptibles. Isolation may have been the first infection control method. Over the centuries quarantine has been used to reduce the transmission of human diseases such as leprosy, plague, cholera, typhus, yellow fever, smallpox, diphtheria, tuberculosis, measles, mumps, ebola, and lassa fever. Quarantine has also been used for animal diseases such as rinderpest, foot and mouth, psittacosis, Newcastle disease, and rabies. Studies of epidemic models with quarantine have become an important area in the mathematical theory of epidemiology, and they have largely been inspired by Refs. [1–4].
where parameters A, μ, and β are positive constants, and α, γ, ε, and δ are non-negative constants. Here denotes the number of members who are susceptible to an infection at time t. denotes the number of members who are infective at time t. denotes the number of members who are removed and isolated either voluntarily or coercively from the infectious class. The parameters in the model are summarized in the following list:
A: the recruitment rate of susceptibles corresponding to births and immigration;
β: transmission coefficient between compartments S and I;
μ: the per capita natural mortality rate;
δ: the rate for individuals leaving the infective compartment I for the quarantine compartment Q;
γ: recovery rate of infectious individuals;
ε: the rate at which individuals return to susceptible compartment S from compartments Q;
α: disease-caused death rate of infectious individuals.
which is globally asymptotically stable under a sufficient condition in invariant set Γ.
In fact, epidemic models are inevitably affected by environmental white noise which is an important component in realism, because it can provide an additional degree of realism in comparison to their deterministic counterparts. Many stochastic models for epidemic populations have been developed in [6–17]. Dalal et al.  have previously used the technique of parameter perturbation to examine the effect of environmental stochasticity in a model of AIDS and condom use. They found that the introduction of stochastic noise changes the basic reproduction number of the disease and can stabilize an otherwise unstable system. Tornatore et al.  propose a stochastic disease model where vaccination is included. They prove existence, uniqueness, and positivity of the solution and the stability of the disease-free equilibrium. Zhao et al.  discuss the dynamics of a stochastic SIS epidemic model with vaccination. They obtain the condition of the disease extinction and persistence according to the threshold of the deterministic system and the noise. Ji et al.  discuss the SDE SIR model with no delay. They obtain if , then the disease-free equilibrium is stochastically asymptotically stable in the large and exponentially mean-square stable. If , then the solution of the system is fluctuating around , which is the endemic equilibrium of the corresponding deterministic system. The disease will prevail if the white noise is small.
However, compared to deterministic systems, it is difficult to give the threshold of stochastic systems. Recently, Gray et al. in  investigate the stochastic SIS epidemic model with fluctuations around the transmission coefficient β. They prove that this model has a unique global positive solution and establish conditions for extinction and persistence of according to the threshold of the stochastic model. In the case of persistence they show the existence of a stationary distribution and derive expressions for its mean and variance. Zhao and Jiang  continue to discuss the stochastic SIS epidemic model with vaccination. When the noise is small, they obtain a threshold of the stochastic system which determines the extinction and persistence of the epidemic. Besides, they find that large noise will suppress the epidemic from prevailing.
This paper is organized as follows. In Section 2, we show there is a unique positive solution of system (1.3). In Section 3, we investigate system (1.3) is exponential stability when the noise is large. In this case, the infective decays exponentially to zero. When the noise is small, we deduce the condition which will enable the disease to die out exponentially in Section 3 and the condition for the disease being persistent is given in Sections 4. Throughout the paper, outcomes of numerical simulations are reported to support the analytical results.
Next, we give some basic theory in stochastic differential equations (see ).
Consider (1.4), assume and for all . So is a solution of (1.4), called the trivial solution or equilibrium position.
In this section we first show that the solution of system (1.3) is global and positive. To get a unique global (i.e. no explosion in a finite time) solution for any given initial value, the coefficients of the equation are generally required to satisfy the linear growth condition and local Lipschitz condition (cf. ). However, the coefficients of system (1.3) do not satisfy the linear growth condition, though they are locally Lipschitz continuous, so the solution of system (1.3) may explode in finite time. In this section, using the Lyapunov analysis method (mentioned in [11, 14]), we show the solution of system (1.3) is positive and global.
Theorem 2.1 There is a unique solution of system (1.3) on for any initial value , and the solution will remain in with probability 1, namely, for all almost surely.
The remainder of the proof follows that in Ji et al. . □
is a positively invariant set of system (1.3) on , which is similar to Γ of system (1.1).
From now on, we always assume that .
We first give a useful lemma.
Lemma 3.1 ( (Strong Law of Large Numbers))
The following is the main theorem of this section.
Conclusion (3.2) is proved.
This finishes the proof of Theorem 3.1. □
Remark 3.2 Theorem 3.1 tells us the disease will die out if and the white noise is not large satisfied . Moreover, we notice the conditions for to become extinct in the SDE model (1.3) are weaker than in the corresponding deterministic model. When the white noise is large enough such that is satisfied, the disease will also die out. The following examples illustrate this result more explicitly.
with any initial value . That is, will tend to zero exponentially with probability one.
Since there is no internal equilibrium model in the stochastic infectious disease model, how to choose the appropriate scale to reflect the widespread prevalence of the disease is a key issue. We first give a definition of persistence in the mean and a lemma to be used in proof.
Lemma 4.2 
Combining (4.4) with (4.6), conclusion (4.1) is proved.
This finishes the proof of Theorem 4.1. □
Remark 4.3 From Theorem 3.1 and Theorem 4.1, we can see when the noise is so small that , then the value of will lead to the disease dying out and the value of will lead to the disease prevailing. So we consider as the threshold of stochastic system (1.3).
That is to say, the disease will prevail.
The work was supported by NSFC grant 11371169 and RFDP 20120061110002.
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