
This paper proposes a non-smooth human influenza model with logistic source to describe the impact on media coverage and quarantine of susceptible populations of the human influenza transmission process. First, we choose two thresholds IT and ST as a broken line control strategy: Once the number of infected people exceeds IT, the media influence comes into play, and when the number of susceptible individuals is greater than ST, the control by quarantine of susceptible individuals is open. Furthermore, by choosing different thresholds IT and ST and using Filippov theory, we study the dynamic behavior of the Filippov model with respect to all possible equilibria. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or bistability endemic equilibria under some conditions. The regular/virtulal equilibrium bifurcations are also given. Lastly, numerical simulation results show that choosing appropriate threshold values can prevent the outbreak of influenza, which implies media coverage and quarantine of susceptible individuals can effectively restrain the transmission of influenza. The non-smooth system with logistic source can provide some new insights for the prevention and control of human influenza.
Citation: Guodong Li, Wenjie Li, Ying Zhang, Yajuan Guan. Sliding dynamics and bifurcations of a human influenza system under logistic source and broken line control strategy[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6800-6837. doi: 10.3934/mbe.2023293
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This paper proposes a non-smooth human influenza model with logistic source to describe the impact on media coverage and quarantine of susceptible populations of the human influenza transmission process. First, we choose two thresholds IT and ST as a broken line control strategy: Once the number of infected people exceeds IT, the media influence comes into play, and when the number of susceptible individuals is greater than ST, the control by quarantine of susceptible individuals is open. Furthermore, by choosing different thresholds IT and ST and using Filippov theory, we study the dynamic behavior of the Filippov model with respect to all possible equilibria. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or bistability endemic equilibria under some conditions. The regular/virtulal equilibrium bifurcations are also given. Lastly, numerical simulation results show that choosing appropriate threshold values can prevent the outbreak of influenza, which implies media coverage and quarantine of susceptible individuals can effectively restrain the transmission of influenza. The non-smooth system with logistic source can provide some new insights for the prevention and control of human influenza.
With the development of society, the number of deaths caused by influenza infection has had gradually increased. For example, the global pandemic in 1918 caused more deaths in the world than in the First World War[1,2]. According to statistical analysis, the global influenza pandemic is caused by H2N2 virus, which is called "Asian influenza" because it first occurred in Asia. The incidence rate was about 15–30% [3,4]. Recently, COVID-19 also has a great impact on human life. According to the latest real-time statistics of the World Health Organization, as of October 10, 2022, the cumulative number of confirmed cases worldwide exceeded 600 million, and the cumulative number of deaths was about 6.5 million[5]. Therefore, in order to better control and reduce the number of deaths, how to effectively and quickly control the spread of disease is worthy of further study.
In the face of the global pandemic, many countries have responded to and mitigated the impact of influenza on human life through media reports, vaccination, disinfection, the use of protective equipment (such as masks), quarantine and other measures. In addition, some biological mathematicians have also established many mathematical models in order to better understand influenza infection and quantify the effectiveness of various control measures [6,7,8,9,10,11,12,13]. For example, considering social factors such as vaccination, media reports and protective measures, [14,15,16,17,18,19,20,21] established some different forms of disease models to analyze the impact of disease transmission. Practice shows that frequent hand washing, disinfection or wearing protective masks, as well as reducing or avoiding close contact with infected persons, can effectively reduce transmission [22,23,24,25,26,27,28,29]. However, media reports on influenza cases and deaths may have a great impact on the public, because the public will take some protective measures after media reports. In addition, quarantine and other measures will greatly reduce the effective contact rate between vulnerable people and infected people, thus reducing the spread of disease.
Recently, many different control strategies have been proposed to apply to the control of some disease models. For example, by using multiple optimal control, Ndii and Adi [12] have studied the effects of individual awareness and vector controls on Malaria transmission dynamics. By using a non-smooth control strategy, Li et al.[13] have considered the bifurcations and dynamics of a plant disease system. By using the two thresholds control strategy, Li et al.[30] have considered the global dynamics of a Filippov predator-prey model. Chen et al. [31] have considered a two-thresholds policy for a Filippov model in combating influenza. Zhou et al. [32] have discussed a two-thresholds policy to interrupt transmission of West Nile Virus to birds. Meanwhile, based on the above mentioned transmission factors, some scholars have also done a lot of meaningful work. For example, Dong et al. [33] studied a kind of nonlinear incidence Filippov epidemic model to describe the impact of media in the process of epidemic transmission. They found that choosing an appropriate threshold value and control intensity can prevent the outbreak of infectious diseases, and media coverage can reduce the burden of disease outbreaks and shorten the duration of disease outbreaks. Can et al.[25] have studied a Filippov model describing the effects of media coverage and quarantine on the spread of human influenza. Xiao et al. [28] have discussed a media impact switching surface during an infectious disease outbreak. In real life, when the influenza started to spread, that is, when the epidemic was not serious, the public paid little attention to the influenza. Generally, only when the number of people who have already felt it reaches and exceeds a certain threshold level will the mass media begin to coverage in large numbers and the public begin take some countermeasures [11,17,19,20]. Currently, many control managements have been developed, including threshold [30], harvesting control management[34,35,36], impulsive control management[37,38] and other controls [39,40,41,42,43,44,45]. However, the implementation of this measure may cause some social impacts, such as socioeconomic decline and psychological impact on the quarantined people. It is important to consider when to take these control measures. Therefore, an appropriate threshold policy is needed to deal with the influenza outbreak, or at least reduce the number of infected people to an acceptable level. As far as we know, under logistic source and broken line control strategy, the sliding dynamics and bifurcations of a human influenza system have been seldom reported in existing work.
Motivation and inspiration come from the discussion above. In this paper, we propose a human influenza system under logistic source and broken line control strategy. The main contributions of this paper include three points: First, choose two thresholds IT and ST as a broken line control strategy: Once the number of infected people exceeds IT, the media influence comes into play, and when the number of susceptible individuals is greater than ST, the control by quarantine of susceptible individuals control is open. Furthermore, by choosing different thresholds IT and ST, the existence and stability of all possible equilibria considered, and then the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or bistability endemic equilibria under some conditions. It is worth pointing out that in our paper there exists a new phenomenon of two real equilibrium points co-existing.
In fact, from the perspective of control strategy, our paper puts forward a broken line control strategy. Different from the previous single-stage control strategy [30,34,35,37,38], our control strategy is divided into two stages and can control and simulate the real life situation well.
This paper is structured as follows. In Section 2, we propose a non-smooth model under logistic source and broken line control strategy. By changing the infection threshold values and susceptibility threshold values, in Section 3, we consider the global dynamics of the system under Case 1: ST<S∗1<S∗2=S∗3. In Section 4, we study the global dynamics of the system under Case 2: S∗1<ST<S∗2=S∗3. Section 5 considers the global dynamics of the system under Case 3: S∗2=S∗3<ST.The regular/virtulal equilibrium bifurcations are given in Section 6. Finally, we summarize the main results of this paper and discuss some biological conclusions in Section 7.
In [25], a Filippov human influenza model with effects of media coverage and quarantine was considered. Because a logistic source can better depict the real situation, motivated by the above discussion and the existing multiple threshold conditions [25,30,31,32,45], we consider the logistic source factor with the susceptible population, and then the new human influenza model can be described by
{St=γS(1−SK)−βSI,It=βSI−(d+δ+r)I,Rt=rI−dI, | (2.1) |
where S is susceptible individuals, I is infected individuals, and R is recovered individuals. γ is an intrinsic growth rate of susceptible individuals, and K is carrying capacity. β is the transmission rate. d is the natural death rate. δ is the death rate caused by disease, and r is the recovered rate.
To better control the spread of the disease, we give the following broken line control strategy. If the infected populations is lower than IT, no control is required. When the infected populations is greater than IT, the media coverage control is open. That is, the mass media being coverage of information about the disease, including the route of transmission, the number of infected cases and the number of deaths.
Therefore, the public realizes the harm of the disease, and they changed their behavior, leading to a decline in the contact rate β. In this paper, we consider that the positive number v1(0≤v1≤1) is the reduction amount of contact rate. In addition, we consider the quarantine control as follows. If the number of susceptible individuals is less than ST, we do not quarantine susceptible individuals. If S>ST holds, the quarantine control is open, and this time we assume that the positive number v2(0≤v2≤1) is the quarantine rate of susceptible individuals. Figure 1 shows the broken line control strategy. Based on system (2.1) and the broken line control strategy, in this paper, we propose a Filippov influenza system with media control and quarantine of susceptible populations as follows:
{St=γS(1−SK)−β(1−v1)SI−v2S,It=β(1−v1)SI−(d+δ+r)I,Rt=rI−dI | (2.2) |
with
(v1,v2)={(0,0), for I<IT,(p,0), for I>IT and S<ST,(p,q), for I>IT and S>ST. | (2.3) |
Notice that the third equation R does not contain the variables S and I of (2.2), so we can not consider the third equation R of the system. Then, in this paper, we investigate a Filippov system
{St=γS(1−SK)−β(1−v1)SI−v2S,It=β(1−v1)SI−(d+δ+r)I. | (2.4) |
Then, the first quadrant is divided by the following five regions:
Γ1={(S,I)∈R2+:I<IT},Γ2={(S,I)∈R2+:I>IT and S<ST},Γ3={(S,I)∈R2+:I>IT and S>ST},Π1={(S,I)∈R2+:I=IT},Π2={(S,I)∈R2+:I>IT and S=ST}. |
The non-smooth system in region Γi for i=1,2,3 is described by
(StIt)=(γS(1−SK)−βSIβSI−(d+δ+r)I)=F1(S,I),(S,I)∈Γ1;(StIt)=(γS(1−SK)−β(1−p)SIβ(1−p)SI−(d+δ+r)I)=F2(S,I),(S,I)∈Γ2;(StIt)=(γS(1−SK)−β(1−p)SI−qSβ(1−p)SI−(d+δ+r)I)=F3(S,I), (S,I)∈Γ3. | (2.5) |
The normal vector of the system is defined as n1=(0,1)T in Π1, while the normal vector of the system is n2=(1,0)T in Π2. Denote the right-hand side of system (2.4) by f. The following definitions are necessary in this paper[14,46,47].
Definition 1. [14] A point E is called a real equilibrium of system (2.4) if there exists i∈{1,2,3} such that Fi(E)=0 and E∈Γi, denoted by ER.
Definition 2. [14] A point E is called a virtual equilibrium of system (2.4) if there exists i∈{1,2,3} such that Fi(E)=0 and E∉¯Γi, where ¯Γi is the closure of Γi, denoted by EV.
Denote the equations that describe the sliding mode dynamics on the sliding mode domain ℓi⊂Πi by Fsi(S,I),i∈{1,2}.
Definition 3. [14] A point E is called a pseudo-equilibrium of system (2.4) if point E is an equilibrium of Fsi(S,I) on sliding mode domain ℓ. That is, Fsi(E)=0, and point E∈ℓ⊂Πi,i∈{1,2}.
Lemma 1. The solution (S(t),I(t)) of system (2.4) with the positive initial value S(0) and I(0) satisfy S(t)>0 and I(t)>0 for t∈[0,+∞).
Proof. First, we prove that S(t)>0. If not, we assume that there is a time t1 satisfying the solution S(t1)≤0. Then we know that there exists the other time t∗>0 such that the solution S(t∗)=0 and S(t)>0 for t∈[0,t∗). From the first equation of system (2.4), one has
dSdt=γS(1−SK)−β(1−v1)SI−v2S=S[r(1−SK)−β(1−v1)I−v2]. |
Further, we obtain
S(t∗)=S(0)e∫t∗0[r(1−SK)−β(1−v1)I−v2]ds>0, |
which is a contradiction with S(t∗)=0. Thus, S(t)>0, for all t>0.
Next, we prove that I(t)>0. If not, we assume there is a time t2 satisfying the solution I(t2)≤0. Then, there exists the other time ˜t>0 such that the solution I(˜t)=0, and I(t)>0 for time t∈[0,˜t). From the second equation of system (2.4), one gets
dIdt=I[(β(1−v1)S−(d+δ+r)]≥−(d+δ+r)I,t∈[0,˜t]. |
Then, if t=˜t, we have
I(˜t)≥I0e−(d+δ+r)˜t>0, |
which is a contradiction with I(˜t)=0. Thus, I(t)>0, for all t>0. In a word, we have that (S(t),I(t)) of (2.4) with the positive initial value S(0) and I(0) satisfies S(t)>0 and I(t)>0 for t∈[0,+∞). The proof is finished.
Lemma 2. The solution (S,I) of system (2.4) is bounded.
Proof. Notice the first equation of system (2.4). Then,
dSdt∣x=K=−β(1−v1)KI−v2S<0,anddSdt∣x>K<0. |
Hence, there is a positive number T such that the solution S(t)<K for t≥T.
Let N=S+I, and then for t≥T, we obtain
dNdt=γS(1−SK)−v2S−(d+δ+r)I≤γK(1−SK)−v2S−(d+δ+r)I≤γK−min{γ+v2,d+δ+r}N. |
Moreover,
lim supt→∞N≤γKmin{γ+v2,d+δ+r}. |
Therefore, under Lemma 1, we know that the solution of (2.4) is bounded. The proof is finished.
Next, we give the basic reproduction number R0i,i=1,2,3, of the system. For example, in region Γ1, the basic reproduction number is defined as R01=Kβd+δ+r. In region Γ2, the basic reproduction number is defined as R02=Kβ(1−p)d+δ+r. In region Γ3, the basic reproduction number is defined as R03=(γ−p)Kβ(1−p)γ(d+δ+r).
For system (2.4), clearly, in region Γ1, (2.4) has three equilibria, i.e., E10=(0,0), E11=(K,0) and E1=(S∗1,I∗1)=(d+δ+rβ,γβ(1−d+δ+rKβ)), which is a stable node if R01>1.
In region Γ2, system (2.4) has three equilibria, i.e., E20=(0,0), E21=(K,0) and E2=(S∗2,I∗2)=(d+δ+rβ(1−p),γβ(1−p)(1−d+δ+rKβ(1−p))), which is a stable node (focus) if R02>1.
In region Γ3, system (2.4) has three equilibria, i.e., E30=(0,0), E31=(Kγ−qγ,0) and E3=(S∗3,I∗3)=(d+δ+rβ(1−p),γβ(1−p)(1−d+δ+rKβ(1−p)−qr)), which is a stable node (focus) if R03>1.
Theorem 1. Suppose that R0i<1, and the disease free equilibrium Ei1(i=1,2,3) of (2.4) is globally asymptotically stable. In addition, the endemic equilibrium Ei(i=1,2,3) of (2.4) is globally asymptotically stable if R0i>1.
Proof. Since R0i<1,i=1,2,3, a Lyapunov function is considered as
L(t)=S−Si1−Si1lnSSi1+I. |
Applying LaSalle's invariance principle[13,14], we know that Ei1(i=1,2,3) of system (2.4) is globally asymptotically stable.
When R0i>1,i=1,2,3, the following Lyapunov function is considered:
L(t)=S−S∗i−S∗ilnSS∗i+I−I∗i−I∗ilnII∗i. |
Using LaSalle's invariance principle [13,14], we know that points Ei(i=1,2,3) of system (2.4) areglobally asymptotically stable. The proof is finished.
This paper only considers the global dynamics of (2.4) under case R0i>1 (i=1,2,3). Next, we aim to address the richness of the possible equilibria and sliding modes on Π1 and Π2 that the system with can exhibit.
From (2.4), when S∗1<S∗2=S∗3 and I∗3<I∗2, we consider the following three cases: ST<S∗1,S∗1<ST<S∗2=S∗3, and S∗2=S∗3<ST with varied IT. Further, according to the dynamics in each case, the biological phenomena of (2.4) are described in this section.
Throughout the paper, the S-nullclines and I-nullclines of (2.4) are represented by the dashed curves and dash-dot lines, respectively. Thus, S=S∗1,S∗2 and S∗3 are the I-nullclines of F1,F2 and F3, denoted by L12,L22 and L32, respectively. That is, the curves
L12:={(S,I)∈Γ1:γS(1−SK)−βSI=0}, |
L22:={(S,I)∈Γ2:γS(1−SK)−β(1−p)SI=0} |
and
L32:={(S,I)∈Γ3:γS(1−SK)−β(1−p)SI−qS=0} |
are the S-nullclines of systems F1,F2 and F3, denoted by L11,L21 and L31, respectively.
In this part, we first consider sliding mode dynamics of (2.4) on Π1 under Case 1: ST<S∗1<S∗2=S∗3. Second, the sliding mode dynamics on Π2 are also given under Case 1: ST<S∗1<S∗2=S∗3. In addition, we investigate the bifurcations of (2.4) under Case 1: ST<S∗1<S∗2=S∗3. Finally, some numerical simulations are displayed to confirm the results.
This part investigates the existence of the sliding mode region on Π1. Based on Definition 3, if ⟨n1,F1⟩>0 and ⟨n1,F3⟩<0 hold, then we know that there is the sliding mode region ℓ1, which is expressed as
ℓ1={(S,I)∈Π1:S∗1<S<S∗3}. |
Using the Filippov convex method [13,14], we obtain
(StIt)=λ1F1(S,I)+(1−λ1)F3(S,I),where λ1=⟨n1,F3⟩⟨n1,F3−F1⟩. |
So, we have the differential equations describing the sliding mode dynamics along the manifold ℓ1 for system (2.4):
(StIt)=(γS(1−SK)−qpS−(d+δ+r)IT+(d+δ+r)qβp0). | (3.1) |
Next, we analyze the existence of the positive equilibriums on ℓ1 of (3.1). Let
Δ1=(γ−qp)2−4γK[(d+δ+r)IT−(d+δ+r)qβp],I∗T=qβp+(γ−qp)2K4γ(d+δ+r). |
Proposition 1. For varied IT, we have the following results.
● If IT>I∗T, then system (3.1) has no equilibrium.
● If I∗T>IT>qβp, system (3.1) has two positive equilibria E±s1=(S±s1,IT), where S±s1=(γ−qp)±√Δ12γK.
● If IT<qβp, then system (3.1) has a unique positive equilibrium Es2=(Ss2,IT), where Ss2=(γ−qp)+√Δ12γK.
In addition, when the sliding mode ℓ1 has a pseudo-equilibrium, we have
S∗T=γ−qp2γK. | (3.2) |
Proposition 2. Under the condition S∗T<S∗1, E−s1∉ℓ1, and the following results are given.
● If IT<I∗3, we have E+s1∉ℓ1.
● If I∗3<IT<I∗1, we have E+s1∈ℓ1.
● If IT>I∗1, we have E+s1∉ℓ1.
Proposition 3. Under the condition S∗1<S∗T<S∗3, the following results hold.
(1) Assume that I∗1<I∗3, and then
● if I∗1<IT<I∗3, we have E−s1∈ℓ1, E+s1∉ℓ1;
● if I∗3<IT<I∗T, we have E−s1∈ℓ1, E+s1∈ℓ1.
(2) Assume that I∗1>I∗3, and then
● if I∗3<IT<I∗1, we have E−s1∉ℓ1, E+s1∈ℓ1;
● if I∗1<IT<I∗T, we have E−s1∈ℓ1, E+s1∈ℓ1.
Proposition 4. Under the condition S∗T>S∗3, E+s1∉ℓ1, and the following conclusions hold.
● If IT<I∗1, we have E−s1∉ℓ1.
● If I∗1<IT<I∗3, we have E−s1∈ℓ1.
● If IT>I∗3, we have E−s1∉ℓ1.
Theorem 2. If I∗T>IT>qβp, then a stable pseudo-equilibriums E+s1 is located on the sliding mode ℓ1, and the unstable pseudo-equilibriums E−s1 is located on the sliding mode ℓ1.
Proof. Notice that
∂∂S(rS(1−SK)−qpS−(d+δ+r)IT+(d+δ+r)qβp)|S±s1=∓√Δ1. |
Therefore, the point E+s1 is attracting, and the point E−s1 is repelling.
Proposition 5. Assume that γ−qpγK>d+δ+rβ(1−p), and then the pseudo-equilibrium Es2 is not located on ℓ1.
Proof. From Proposition 1, Eq (3.2) and the condition of Proposition 5, one has
Ss2=(γ−qp)+√Δ12γK>2S∗T=2γ−qpγK>d+δ+rβ(1−p)=S∗3, |
which implies that Ss2≥2S∗T>S∗3. Based on the definition of sliding mode region ℓ1, we know that Ss2∉ℓ1. Then, ℓ1 does not have a pseudo-equilibrium Es2. The proof is completed.
Let
J1=γβ(1−p)(1−STK−qγ),J2=γβ(1−p)(1−STK). |
Clearly J1<J2. A subset of Π2 is a sliding mode domain if ⟨F2,n2⟩⟨F3,n2⟩<0. If IT>J2, there is no sliding mode domain on Π2. If IT<J2, then the sliding mode domain of (2.4) on Π2 is given as
ℓ2={(S,I)∈Π2:max{IT,J1}<I<J2}. | (3.3) |
Using the Filippov convex method[13], we have the differential equations describing the sliding mode dynamics along the manifold ℓ2 for system (2.4) with (2.3):
(StIt)=(0β(1−p)STI−(d+δ+r)I). | (3.4) |
Clearly, there is not a positive equilibrium. Thus, if there exists a sliding domain ℓ2 on Π2, we know the system does not have a pseudo-equilibrium.
Due to ST<S∗1<S∗2=S∗3, we know that E2∉Γ2 is a virtual equilibrium, denoted by EV2. However, point E1 and point E3 may exist depending on IT, and we have the following three Propositions:
Proposition 6. Assume that S∗T<S∗1, and the following assertions hold.
● If IT<I∗3, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∉Γ1,E3∈Γ3.
● If I∗3<IT<I∗1, we have E−s1∉ℓ1,E+s1∈ℓ1,E1∉Γ1,E3∉Γ3.
● If I∗1<IT<I∗T, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∉Γ3.
● If IT>I∗T, we have E−s1andE+s1don't exist,E1∈Γ1,E3∉Γ3.
Proposition 7. Under the condition S∗1<S∗T<S∗3, the following assertions hold.
(1) Assume that I∗1<I∗3, and further,
● if IT<I∗1, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<I∗3, we have E−s1∈ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<I∗T, we have E−s1∈ℓ1, E+s1∈ℓ1,E1∈Γ1,E3∉Γ3;
● if IT>I∗T, we have E−s1andE+s1don't exist,E1∈Γ1,E3∉Γ3.
(2) Assume that I∗1>I∗3, and further,
● if IT<I∗3, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<I∗1, we have E−s1∉ℓ1,E+s1∈ℓ1,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<I∗T, we have E−s1∈ℓ1, E+s1∈ℓ1,E1∈Γ1,E3∉Γ3;
● if IT>I∗T, we have E−s1andE+s1don't exist,E1∈Γ1,E3∉Γ3.
Proposition 8. Under the condition S∗T>S∗3, the following assertions hold.
● If IT<I∗1, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∉Γ1,E3∈Γ3.
● If I∗1<IT<I∗3, we have E−s1∈ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∈Γ3.
● If I∗3<IT<I∗T, we have E−s1∉ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∉Γ3.
● If IT>I∗T, we have E−s1andE+s1don't exist,E1∈Γ1,E3∉Γ3.
Based on Propositions 6–8, we have the following summary:
B1. Let E−s1∉ℓ1,E+s1∉ℓ1,E1∉Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set B1−1. Then, we conclude that there does not exist a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to ER3, as shown in Figure 2(a), where
B1−1={Υ∈R2+:ST<S∗1,IT<min{I∗1,I∗3}}. |
B2. Let E−s1∉ℓ1,E+s1∈ℓ1,E1∉Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set B2−1. Then, we know that E+s1∈ℓ1⊂Π1 is a stable pseudo-equilibrium, and all solutions of the system will approach E+s1, as shown in Figure 2(b), where
B2−1={Υ∈R2+:ST<S∗1,I∗3<IT<I∗1}. |
B3. Let E−s1∉ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set B3−1∪B3−2∪B3−3. Then, we conclude that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to the equilibrium point ER1, as shown in Figure 2(c), where
B3−1={Υ∈R2+:ST<S∗1,I∗1<IT<I∗T, if S∗T<S∗1},B3−2={Υ∈R2+:ST<S∗1,IT>I∗T, if S∗1<S∗T<S∗3},B3−3={Υ∈R2+:ST<S∗1,I∗3<IT<I∗T, if S∗T>S∗3}. |
B4. Let E−s1∈ℓ1,E+s1∉ℓ1,E1∈Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set B4−1. Then, we know that E−s1∈ℓ1⊂Π1 is an unstable pseudo-equilibrium, and all solutions will approach ER1 or ER3. The result of this numerical simulation is shown in Figure 2(d), where
B4−1={Υ∈R2+:ST<S∗1,I∗1<IT<I∗3}. |
B5. Let E−s1∈ℓ1,E+s1∈ℓ1,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set B5−1. Then, we conclude that E−s1∈ℓ1⊂Π1 is an unstable pseudo-equilibrium, and all solutions of the system (2.4) will tend to ER1 or E+s1. The result of this numerical simulation is shown in Figure 2(e), where
B5−1={Υ∈R2+:ST<S∗1,max{I∗1,I∗3}<IT<I∗T, if S∗1<S∗T<S∗3}. |
For the B1 of case 1, when system (2.4) has a unique equilibrium ER3, we have the following.
Theorem 3. If ST<S∗1<S∗3=S∗2 and IT<min{I∗1,I∗3}, then the point ER3 of system (2.4) is globally asymptotically stable.
Proof. Suppose that the system (2.4) has a closed orbit U (shown in Figure 3(a)) that surrounds the real equilibrium ER32 and the sliding mode ℓ1. Define U=U1+U2+U3, where Ui=U∩Γi,i=1,2,3. Let Ω be the bounded region delimited by U and Ωi=Ω∩Γi for i=1,2,3. Considering the Dulac function D=1SI. Three Steps are given as follows:
Step 1: System (2.4) does not have a closed orbit in region Γi,i=1,2,3.
∬U(∂(Df1)∂S+∂(Df2)∂I)dSdI=3∑i=1∬Ui(∂(DFi1)∂S+∂(DFi2)∂I)dSdI=−3γK3∑i=1∬Ui1IdSdI<0, |
where f1 is the first component of f, and f2 is the second component of f.Fi1 is the first component of Fi and Fi2 is the second component of Fi,i=1,2,3. Let ˜Ωi be the region bounded by ˜Ui,Pi and Qi, where ˜Ωi and ˜Ui depend on ϵ and converge to Ωi and Ui as ϵ approaches 0.
Step 2: System (2.4) does not have a closed trajectory in region Γ.
We can get
∬Ωi(∂(DFi1)∂S+∂(DFi2)∂I)dSdI=limϵ→0∬˜Ωi(∂(DFi1)∂S+∂(DFi2)∂I)dSdI. |
Since dS=F11dt and dI=F12dt along ˜U1 and dI=0 along P1, by using Green's theorem, for region ˜Ω1, we have
∬˜Ω1(∂(DF11)∂S+∂(DF12)∂I)dSdI=∮∂˜Ω1DF11dI−DF12dS=∫˜U1DF11dI−DF12dS+∫P1DF11dI−DF12dS=−∫P1DF12dS. | (3.5) |
Similarly, we obtain
∬˜Ω2(∂(DF21)∂S+∂(DF22)∂I)dSdI=−∫P2DF22dS+∫Q2DF21dI | (3.6) |
and
∬˜Ω3(∂(DF31)∂S+∂(DF32)∂I)dSdI=−∫P3DF32dS+∫Q3DF31dI. | (3.7) |
From Eqs (3.5)–(3.7), we have
0>3∑i=1∬Ωi(∂(DFi1)∂S+∂(DFi2)∂I)dSdI=limϵ→03∑i=1∬˜Ωi(∂DFi1∂S+∂DFi2∂I)dSdI=limϵ→0(−∫P1DF12dS−∫P2DF22dS+∫Q2DF21dI−∫P3DF32dS+∫Q3DF31dI). | (3.8) |
Denote the intersection points of the closed trajectory U and the line I=IT by A1 and A2 and the intersection point of U and line S=ST if I>IT by A3. In addition, denote the intersection point of the line I=IT and the line S=ST by ET. Note that A1S<ST<A2S and A3I>IT. Then, Eq (3.6) becomes
0>−∫A1SA2S(β−d+δ+rS)dS−∫STA1S(β(1−p)−d+δ+rS)dS+∫A3IIT(γ(1−SK)I−β(1−p))dI−∫A2SST(β(1−p)−d+δ+rS)dS+∫ITA3I(γ(1−SK)−qI−β(1−p))dI=−∫A2SA1S−pdS+∫ITA3I(−qI)dI=p(A2S−A1S)+qln(A3IIT)>0, | (3.9) |
which is a contradiction. Therefore, we know that there does not have a closed orbit U surrounding the sliding mode ℓ1 and the real equilibrium ER3.
Step 3: System (2.4) does not have a closed trajectory in regions Γi and Γj(i≠j). With a similar proof procedure to Step 2, it is easy to that there is no closed trajectory in Γ1 and Γ2 (see Figure 3(d)), Γ1 and Γ3 (see Figure 3(b)), Γ2 and Γ3 (see Figure 3(c)), respectively.
Therefore, based on Steps 1–3, we know that the point ER3 of (2.4) is globally asymptotically stable if ST<S∗1<S∗3=S∗2 and IT<min{I∗1,I∗3}. This completes this theorem.
With a similar proof procedure to Theorem 3, we have the following.
Theorem 4. If ST<S∗1<S∗2=S∗3 and the conditions of (B3) hold, then the point ER1 of system (2.4) is globally asymptotically stable.
In this part, we first consider sliding mode dynamics of (2.4) on Π1 under Case 2: S∗1<ST<S∗2=S∗3. Second, we study the sliding mode dynamics on Π2 under Case 2: S∗1<ST<S∗2=S∗3. In addition, the bifurcations of (2.4) are investigated under Case 2: S∗1<ST<S∗2=S∗3. Finally, some numerical simulations are displayed to confirm the results.
For Case 2, S∗1<ST<S∗2=S∗3, two sliding domains on Π1 are given as
ℓ3={(S,I)∈Π1:S∗1<S<ST},ℓ4={(S,I)∈Π1:ST<S<S∗3}. |
The dynamics on ℓ3 are governed by
(StIt)=(γS(1−SK)−(d+δ+r)IT0). | (4.1) |
Now, we investigate the existence of a positive equilibrium on ℓ3 of system (4.1). Let
Δ2=γ2−4γ(d+δ+r)ITK,I∗′T=γK4(d+δ+r). |
Proposition 9. For varied IT, we have the following results.
(1) If IT>I∗′T, then system (4.1) does not have an equilibrium;
(2) If 0<IT<I∗′T, system (4.1) has two positive equilibria E±s3=(S±s3,IT), where S±s3=γ±√Δ22γK.
Next, we find the conditions of the pseudo-equilibrium on the sliding mode ℓ3. Let
H1=−γKd+δ+rST2+γd+δ+rST,S∗′T=K2. |
Proposition 10. Under the condition S∗′T<S∗1<ST, E−s3∉ℓ3, and the following assertions hold.
(1) If IT>I∗1, we have E+s3∉ℓ3;
(2) If H1<IT<I∗1, we have E+s3∈ℓ3;
(3) If IT<H1, we have E+s3∉ℓ3.
Proposition 11. Under the condition S∗1<S∗′T<ST, we have the following results.
(1) If IT<min{I∗1,H1}, we have E±s3∉ℓ3.
(2) If min{I∗1,H1}<IT<max{I∗1,H1}, we have
● E−s3∈ℓ3,E+s3∉ℓ3ifI∗1<H1;
● E−s3∉ℓ3,E+s3∈ℓ3ifI∗1>H1.
(3) If max{I∗1,H1}<IT<I∗′T, we have E±s3∈ℓ3.
Proposition 12. Under the condition S∗1<ST<S∗′T, E+s3∉ℓ3, and the following assertions hold.
(1) If IT<I∗1, we have E−s3∉ℓ3;
(2) If I∗1<IT<H1, we have E−s3∈ℓ3;
(3) If IT>H1, we have E−s3∉ℓ3.
Theorem 5. If 0<IT<I∗′T, then a stable pseudo-equilibrium E+s3 of (2.4) is located on the sliding mode ℓ3, and the unstable pseudo-equilibrium E−s3 of (2.4) is located on the sliding mode ℓ3.
Proof. Notice that
∂∂S(γS(1−SK)−(d+δ+r)IT)|E±s3=∓√Δ2. |
Therefore, the point E+s3 is attracting, and the point E−s3 is repelling.
The dynamics on region ℓ4 are described by (3.1). Let
H2=qβp−γK(d+δ+r)ST2+(γd+δ+r−qp(d+δ+r))ST. |
From Proposition 1, we have the following.
Proposition 13. Under the condition S∗T<ST<S∗3, E−s1∉ℓ4, and the following assertions hold.
(1) If IT<I∗3, we have E+s1∉ℓ4;
(2) If I∗3<IT<H2, we have E+s1∈ℓ4;
(3) If IT>H2, we have E+s1∉ℓ4.
Proposition 14. Under the condition ST<S∗T<S∗3, we have the following results.
(1) If IT<min{I∗3,H2}, we have E±s1∉ℓ4.
(2) If min{I∗3,H2}<IT<max{I∗3,H2}, we have
● E−s1∈ℓ4,E+s1∉ℓ4ifI∗3>H2;
● E−s1∉ℓ4,E+s1∈ℓ4ifI∗3<H2.
(3) If max{I∗3,H2}<IT<I∗T, we have E±s1∈ℓ4.
Proposition 15. Under the condition ST<S∗3<S∗T, E+s1∉ℓ4, and the following assertions hold.
(1) If IT<H2, we have E−s1∉ℓ4;
(2) If H2<IT<I∗3, we have E−s1∈ℓ4;
(3) If IT>I∗3, we have E−s1∉ℓ4.
Theorem 6. If 0<IT<I∗′T, then a stable pseudo-equilibrium E+s1 is located on ℓ4, and the unstable pseudo-equilibrium E−s1 is located on ℓ4.
If S∗1<ST<S∗2=S∗3, the sliding mode dynamics on region Π2 are the same as Section 3.2. We omit it here.
For this case, we conclude that the point E2 is a virtual equilibrium. E1 and E3 are changeable depending on IT, and we have the following five situation.
Proposition 16. Under the conditions S∗′T<S∗1<ST and S∗T<ST<S∗3, the following assertions hold.
(1) If IT<I∗3, we have E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
(2) If I∗3<IT<H2, we have E+s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
(3) If H2<IT<H1, we have E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
(4) If H1<IT<I∗1, we have E+s3∈ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
(5) If I∗1<IT<I∗T, we have E+s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
Proposition 17. Under the conditions S∗1<S∗′T<ST and S∗T<ST<S∗3, the following assertions hold.
(1) Assume that I∗1>H1, and further,
● if IT<I∗3, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if H2<IT<H1, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
● if H1<IT<I∗1, we have E−s3∉ℓ3,E+s3∈ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<I∗′T, we have E−s3∈ℓ3,E+s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(2) Assume that H2<I∗1<H1, and further,
● if IT<I∗3, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if H2<IT<I∗1, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<H1, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∈ℓ3,E+s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(3) Assume that I∗3<I∗1<H2, and
● if IT<I∗3, then E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<I∗1, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∈ℓ3,E+s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(4) Assume that I∗1<I∗3, and further,
● if IT<I∗1, we have E−s3∉ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<I∗3, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E+s3∉ℓ3,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∈ℓ3E+s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
Proposition 18. Under the conditions S∗1<ST<S∗′T and S∗T<ST<S∗3, we have the following results.
(1) Assume that I∗1>H2, and further,
● if IT<I∗3, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if H2<IT<I∗1, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<H1, we have E−s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(2) Assume that I∗3<I∗1<H2, and further,
● if IT<I∗3, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<I∗1, we have E−s3∉ℓ3,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(3) Assume that I∗1<I∗3, and further,
● if IT<I∗1, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<I∗3, we have E−s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∈ℓ3,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∉ℓ3,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
Proposition 19. Under the conditions S∗1<ST<S∗′T and ST<S∗T<S∗3, we have the following results.
(1) Assume that I∗3>H1, I∗1<H2<H1<I∗3, and
● if IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if H1<IT<I∗3, we have E−s3∉ℓ3,E−s1∈ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<I∗T, we have E−s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3.
(2) Assume that H2<I∗3<H1, I∗1<H2<I∗3<H1, and further,
● if IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if H2<IT<I∗3, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if IT>I∗3, then
◇ if I∗T>H1, and further,
◇ if I∗3<IT<H1, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇ if H1<IT<I∗T, we have E−s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3.
◇ if I∗T<H1, and
◇if I∗3<IT<I∗T, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇ if I∗T<IT<H1, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
◇ if H1<IT<I∗′T, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(3) Assume that I∗1<I∗3<H2, I∗1<I∗3<H2<H1, and
● if IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<I∗3, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if IT>H2,
◇ if I∗T>H1, and further,
◇ if H2<IT<H1, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇ if H1<IT<I∗T, we have E−s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3.
◇ if I∗T<H1, and further,
◇ if H2<IT<I∗T, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇ if I∗T<IT<H1, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
◇ if H1<IT<I∗′T, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(4) Assume that I∗3<I∗1, further, I∗3<I∗1<H2<H1, and
● if IT<I∗3, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗3<IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
● if IT>H2, and further,
◇ if I∗T>H1, and
◇if H2<IT<H1, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇if H1<IT<I∗T, we have E−s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3.
◇ if I∗T<H1, and further
◇ if H2<IT<I∗T, we have E−s3∈ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3;
◇ if I∗T<IT<H1, we have E−s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3;
◇ if H1<IT<I∗′T, we have E−s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3.
Proposition 20. Suppose S∗1<ST<S∗′T and ST<S∗3<S∗T, E+s3∉ℓ3 and E+s1∉ℓ4⊂Π1, we have the following results.
(1) Assume that I∗3>H1, and further,
● if IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if H2<IT<H1, we have E−s3∈ℓ3,E−s1∈ℓ4,E1∈Γ1,E3∈Γ3;
● if H1<IT<I∗3, we have E−s3∉ℓ3,E−s1∈ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<I∗′T, we have E−s3∉ℓ3E−s1∉ℓ4,E1∈Γ1,E3∉Γ3.
(2) Assume that I∗3<H1, and further,
● if IT<I∗1, we have E−s3∉ℓ3,E−s1∉ℓ4,E1∉Γ1,E3∈Γ3;
● if I∗1<IT<H2, we have E−s3∈ℓ3,E−s1∉ℓ4,E1∈Γ1,E3∈Γ3;
● if H2<IT<I∗3, we have E−s3∈ℓ3,E−s1∈ℓ4,E1∈Γ1,E3∈Γ3;
● if I∗3<IT<H1, we have E−s3∈ℓ3,E−s1∉ℓ4,E1∈Γ1,E3∉Γ3;
● if H1<IT<I∗′T, we have E−s3∉ℓ3,E−s1∉ℓ4,E1∈Γ1,E3∉Γ3.
Based on Propositions 16–20, the following summary is given.
C1. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set C1−1. Then, we conclude that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to the equilibrium point ER3. The result of this numerical simulation is shown in Figure 4(a), where
C1−1={Υ∈R2+:S∗1<ST<S∗3,IT<min{I∗1,I∗3}}. |
C2. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∈ℓ4,E1∉Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C2−1. Then, we know that E+s1∈ℓ4⊂Π1 is a stable pseudo-equilibrium, and all solutions of the system (2.4) will approach the point E+s1, as shown in Figure 4(b), where
C2−1={Υ∈R2+:S∗1<ST<S∗3,I∗3<IT<min{H2,I∗1}}. |
C3. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∉Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C3−1. Then, we conclude that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to the point ET=(ST,IT). The result of this numerical simulation is shown in Figure 4(c), where
C3−1={Υ∈R2+:S∗1<ST<S∗3,H2<IT<min{H1,I∗1}}. |
C4. Let E−s3∉ℓ3,E+s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4⊂Π1,E1∉Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C4−1. Then, we know that the point E+s3∈ℓ3 is a stable pseudo-equilibrium, and all solutions of the system (2.4) will approach the point E+s3, as shown in Figure 4(d), where
C4−1={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗1}. |
C5. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C5−1∪C5−2∪C5−3∪C5−4. Then, we know that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to the point ER1. The result of this numerical simulation is shown in Figure 4(e), where
C5−1={Υ∈R2+:S∗1<ST<S∗3,I∗1<IT<I∗T, if S∗′T<S∗1<ST and S∗T<ST<S∗3},C5−2={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗′T, if S∗1<ST<S∗′T and S∗T<ST<S∗3},C5−3={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗′T, if S∗1<ST<S∗′T and ST<S∗T<S∗3,I∗3<H1},C5−4={Υ∈R2+:S∗1<ST<S∗3,max{H1,I∗3}<IT<I∗′T, if S∗1<ST<S∗′T and ST<S∗3<S∗T}. |
C6. Let E−s3∈ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set C6−1. Then, we conclude that E−s3∈ℓ3 is an unstable pseudo-equilibrium, and the solution of the system (2.4) will approach the point ER1 or ER3 or ET. The result of this numerical simulation is shown in Figure 4(f), where
C6−1={Υ∈R2+:S∗1<ST<S∗3,I∗1<IT<min{H2,I∗3}}. |
C7. Let E−s3∈ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C7−1. Then, we know that E−s3∈ℓ3 is an unstable pseudo-equilibrium, where E+s1∈ℓ4 is stable. The solution of the system (2.4) will tend to E+s1 or ER1 or ET. The result of this numerical simulation is shown in Figure 5(a), where
C7−1={Υ∈R2+:S∗1<ST<S∗3,max{I∗1,I∗3}<IT<H2}. |
C8. Let E−s3∈ℓ3,E+s3∉ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C8−1∪C8−2∪C8−3∪C8−4. Then, we conclude that E−s3∈ℓ3 is an unstable pseudo-equilibrium. The solution of the system (2.4) will approach the equilibrium point ER1 or ET, as shown in Figure 5(b), where
C8−1={Υ∈R2+:S∗1<ST<S∗3,max{I∗1,H2}<IT<H1, if S∗1<S∗′T<ST and S∗T<ST<S∗3},C8−2={Υ∈R2+:S∗1<ST<S∗3,max{I∗1,H2}<IT<H1, if S∗1<ST<S∗′T and S∗T<ST<S∗3},C8−3={Υ∈R2+:S∗1<ST<S∗3,I∗T<IT<H1, if S∗1<ST<S∗′T and ST<S∗T<S∗3},C8−4={Υ∈R2+:S∗1<ST<S∗3,I∗3<IT<H1, if S∗1<ST<S∗′T and ST<S∗3<S∗T}. |
C9. Let E−s3∈ℓ3,E+s3∈ℓ3,E−s1∉ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to C9−1. Then, we conclude that E−s3∈ℓ3 is a unstable pseudo-equilibrium, where E+s3∈ℓ3 is stable. The solution of the system (2.4) will tend to the equilibrium point E+s3 or ER1 or ET. The results of this numerical simulation are shown in Figure 5(c), where
C9−1={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗′T, if S∗1<S∗′T<ST and S∗T<ST<S∗3}. |
C10. Let E−s3∈ℓ3,E+s3∉ℓ3,E−s1∈ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set C10−1∪C10−2. Then, we conclude that E−s3∈ℓ3 is an unstable pseudo-equilibrium. The solution of the system (2.4) will converge to ER1 or ER3 or ET, as shown in Figure 5(d), where
C10−1={Υ∈R2+:S∗1<ST<S∗3,H2<IT<min{H1,I∗3}, if S∗1<ST<S∗′T and ST<S∗T<S∗3},C10−2={Υ∈R2+:S∗1<ST<S∗3,H2<IT<min{H1,I∗3}, if S∗1<ST<S∗′T and ST<S∗3<S∗T}. |
C11. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∈ℓ4,E+s1∉ℓ4,E1∈Γ1,E3∈Γ3, and the value Υ=(ST,IT) belongs to the set C11−1∪C11−2. Then, we show that E−s1∈ℓ4 is an unstable pseudo-equilibrium. The solution of the system (2.4) will tend to ER1 or ER3 or ET, as shown in Figure 5(e), where
C11−1={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗3, if S∗1<ST<S∗′T and ST<S∗T<S∗3},C11−2={Υ∈R2+:S∗1<ST<S∗3,H1<IT<I∗3, if S∗1<ST<S∗′T and ST<S∗3<S∗T}. |
C12. Let E−s3∈ℓ3,E+s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3, if S∗1<ST<S∗′T and ST<S∗T<S∗3, and the value Υ=(ST,IT) belongs to the set C12−1. Then, we know that the point E−s3∈ℓ3 and the value E−s1∈ℓ4 are unstable pseudo-equilibriums, where E+s1∈ℓ4 is stable. The solution of the system (2.4) will approach E+s1 or ER1 or ET. The result of this numerical simulation is shown in Figure 5(f), where
C12−1={Υ∈R2+:S∗1<ST<S∗3,max{I∗3,H2}<IT<min{H1,I∗T}}. |
C13. Let E−s3∉ℓ3,E+s3∉ℓ3,E−s1∈ℓ4,E+s1∈ℓ4,E1∈Γ1,E3∉Γ3, and the value Υ=(ST,IT) belongs to the set C13−1. Then, we conclude that E−s1∈ℓ4 is an unstable pseudo-equilibrium, where E+s1∈ℓ4⊂Π1 is stable. The solution of the system (2.4) will converge to E+s1 or ER1 or ET, as shown in Figure 6, where
C13−1={Υ∈R2+:S∗1<ST<S∗3,max{I∗3,H1}<IT<I∗T, if S∗1<ST<S∗′T and ST<S∗T<S∗3}. |
In this part, we first consider sliding mode dynamics of (2.4) on Π1 under Case 3: S∗2=S∗3<ST. Second, the sliding mode dynamics on Π2 are given under Case 3: S∗2=S∗3<ST. In addition, we investigate the bifurcations of (2.4) under Case 3: S∗2=S∗3<ST. Finally, some numerical simulations are displayed to confirm the results.
If ⟨n1,F1⟩>0 and ⟨n1,F2⟩<0 on ℓ5, then ℓ5 is described as
ℓ5={(S,I)∈Π1:S∗1<S<S∗2}. |
Next, the conditions of a pseudo-equilibrium on the sliding mode ℓ5 are given as follows.
Proposition 21. Under the condition S∗′T>S∗2, E+s3∉ℓ5 and the following assertions hold.
(1) If IT<I∗1, we have E−s3∉ℓ5;
(2) If I∗1<IT<I∗2, we have E−s3∈ℓ5;
(3) If IT>I∗2, we have E−s3∉ℓ5.
Proposition 22. Under the condition S∗1<S∗′T<S∗2, the following assertions hold.
(1) Assume that I∗1<I∗2, and further,
● if I∗1<IT<I∗2, we have E−s3∈ℓ5, E+s3∉ℓ5;
● if I∗2<IT<I∗′T, we have E−s3∈ℓ5, E+s3∈ℓ5.
(2) Assume that I∗1>I∗2, and further,
● if I∗2<IT<I∗1, we have E−s3∉ℓ5, E+s3∈ℓ5;
● if I∗1<IT<I∗′T, we have E−s3∈ℓ5, E+s3∈ℓ5.
Proposition 23. Under the condition S∗′T<S∗1, E−s3∉ℓ5 and the following assertions hold.
(1) If IT<I∗2, we have E+s3∉ℓ5;
(2) If I∗2<IT<I∗1, we have E+s3∈ℓ5;
(3) If IT>I∗1, we have E+s3∉ℓ5.
Theorem 7. If 0<IT<I∗′T, the sliding mode ℓ5 has a stable pseudo-equilibrium E+s3, and the sliding mode ℓ5 E−s3 has an unstable pseudo-equilibrium.
When S∗2=S∗3<ST, the sliding mode dynamics on Π2 are the same as Section 3.2. We omit it here.
For this Case 3, the point E3 is a virtual equilibrium, denoted by EV3. Points E1 and E2 are changeable depending on IT, and then we have the following.
Proposition 24. Under the condition S∗′T>S∗2, the following assertions hold.
(1) If IT<I∗1, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∉Γ1,E2∈Γ2;
(2) If I∗1<IT<I∗2, we have E−s3∈ℓ5,E+s3∉ℓ1,E1∈Γ1,E2∈Γ2;
(3) If I∗2<IT<I∗′T, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∈Γ1,E2∉Γ2;
(4) If IT>I∗′T, we have that E−s3andE+s3do not exist,E1∈Γ1,E2∉Γ2.
Proposition 25. Under the condition S∗1<S∗′T<S∗2, the following assertions hold.
(1) Assume that I∗1<I∗2, and further,
● if IT<I∗1, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∉Γ1,E2∈Γ2;
● if I∗1<IT<I∗2, we have E−s3∈ℓ5,E+s3∉ℓ5,E1∈Γ1,E2∈Γ2;
● if I∗2<IT<I∗′T, we have E−s3∈ℓ1, E+s3∈ℓ5,E1∈Γ1,E2∉Γ2;
● if IT>I∗′T, we have that E−s1andE+s1do not exist,E1∈Γ1,E2∉Γ2.
(2) Assume that I∗1>I∗2, and further,
● if IT<I∗2, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∉Γ1,E2∈Γ2;
● if I∗2<IT<I∗1, we have E−s3∉ℓ5,E+s3∈ℓ5,E1∉Γ1,E2∉Γ2;
● if I∗1<IT<I∗′T, we have E−s3∈ℓ5, E+s3∈ℓ5E1∈Γ1,E2∉Γ2;
● if IT>I∗′T, we have that E−s3andE+s3do not exist,E1∈Γ1,E2∉Γ2.
Proposition 26. Under the condition S∗′T<S∗1, the following assertions hold.
(1) If IT<I∗2, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∉Γ1,E2∈Γ2;
(2) If I∗2<IT<I∗1, we have E−s3∉ℓ5,E+s3∈ℓ5,E1∉Γ1,E2∉Γ2;
(3) If I∗1<IT<I∗′T, we have E−s3∉ℓ5,E+s3∉ℓ5,E1∈Γ1,E2∉Γ2;
(4) If IT>I∗′T, we have that E−s3andE+s3do not exist,E1∈Γ1,E2∉Γ2.
Based on Propositions 24–26, the following summary is given.
D1. Let E−s3∉ℓ5,E+s3∉ℓ5,E1∉Γ1,E2∈Γ2, and the value Υ=(ST,IT) belongs to the set D1−1. Then, we conclude that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to ER2. The result of this numerical simulation is shown in Figure 7(a), where
D1−1={Υ∈R2+:S∗2<ST,IT<min{I∗1,I∗2}}. |
D2. Let E−s3∈ℓ5,E+s3∉ℓ5,E1∈Γ1,E2∈Γ2, and the value Υ=(ST,IT) belongs to the set D2−1. Then, we show that E−s3∈ℓ5 is an unstable pseudo-equilibrium. The solution of the system (2.4) will approach ER1 or ER2, as shown in Figure 7(b), where
D2−1={Υ∈R2+:S∗2<ST,I∗1<IT<I∗2}. |
D3. Let E−s3∉ℓ5,E+s3∉ℓ5,E1∈Γ1,E2∉Γ2, and the value Υ=(ST,IT) belongs to the set D3−1∪D3−2∪D3−3. Then, we know that the system (2.4) does not have a pseudo-equilibrium, and all trajectories of the system (2.4) will converge to ER1, as shown in Figure 7(c), where
D3−1={Υ∈R2+:S∗2<ST,I∗2<IT<I∗′T, if S∗′T>S∗2},D3−2={Υ∈R2+:S∗2<ST,IT>I∗′T if S∗1<S∗′T<S∗2},D3−3={Υ∈R2+:S∗2<ST,I∗1<IT<I∗′T, if S∗′T<S∗1}. |
D4. Let E−s3∈ℓ5,E+s3∈ℓ5,E1∈Γ1,E2∉Γ2, and the value Υ=(ST,IT) belongs to the set D4−1. Then, we show that E−s3∈ℓ5 is an unstable pseudo-equilibrium, and the solution of system (2.4) will approach ER1 or E+s3, as shown in Figure 7(d), where
D4−1={Υ∈R2+:S∗2<ST,max{I∗1,I∗2}<IT<I∗′T, if S∗1<S∗′T<S∗2}. |
D5. Let E−s3∉ℓ5,E+s3∈ℓ5,E1∉Γ1,E2∉Γ2, and the value Υ=(ST,IT) belongs to the set D5−1. Then, we conclude that E+s3∈ℓ5 is a stable pseudo-equilibrium. All solutions of the system (2.4) will tend to E+s3. The result of this numerical simulation is shown in Figure 7(e), where
D5−1={Υ∈R2+:S∗2<ST,I∗2<IT<I∗1}. |
Remark 1. For smooth system (2.1), we have discussed the three equilibrium points, that is, (0,0), the disease free equilibrium point Ei1(i=1,2,3) and the endemic equilibrium point Ei(i=1,2,3) of (2.4). By constructing a Lyapunov function, we obtain the global stability of system (2.1) in Theorem 1. For the non-smooth system (2.4), we investigate the non-smooth system (2.4) with two threshold control strategies. Using a Filippov analysis method, Green's formula, the comparison theorem and numerical simulation method, the rich dynamics of the system are given, such as the bistability phenomenon, the globally stable pseudo-equilibrium and the regular/virtulal equilibrium bifurcations. Through the two control strategies, we can control the disease individuals to the appropriate balance. In particular, Theorems 3, 6 and 7 in this paper cannot appear in the smooth system (2.1); please see Figure 4(b), (c). There is bistability in the system (2.4).
Remark 2. In [25], a Filippov model describing the effects of media coverage and quarantine on the spread of human influenza was considered, and the threshold conditions for stability switches were obtained analytically. The discontinuous system (2.4) considered in our paper is a logistic source, and [25] considered a linear source. Second, the dynamics are different. Our paper employs the Green's theorem and a Dulac function. Then, we show that two real equilibria occur simultaneously in our paper. Using numerical simulation methods, the sliding dynamics and bifurcations of a human influenza system under logistic source and broken line control strategy are given. The results of this paper are new with respect to[25].
Based on the previous discussion, it is shown that system (2.4) will exhibit multiple equilibriums and sliding modes. In order to better construct the bifurcation diagram, we choose γ and IT as bifurcation parameters, and the other parameters are fixed as shown in Figure 8. With the expressions of equilibria found in Section 2.3, the lines to divide the relevant parameter plane are given as follows:
l1:={(γ,IT)|IT=I∗1=γβ(1−d+δ+rKβ)}, |
l2:={(γ,IT)|IT=I∗2=γβ(1−p)(1−d+δ+rKβ(1−p))}, |
l3:={(γ,IT)|IT=I∗3=γβ(1−p)(1−d+δ+rKβ(1−p)−qr)}. |
The three solid lines l1, l2 and l3 divide the γ−IT two-dimensional plane space into four regions in the first quadrant. Suppose that the control value IT satisfies I∗3<IT<I∗2 and I∗1<I∗3 (that is, regions Ω∗2 and region Ω∗3; see Figure 8), the points E2 and E3 are virtual equilibria points (denoted by Ev2, and Ev3, respectively), and E−s1 exists with the sliding mode domain. If the control value IT satisfies IT>I∗2 (that is, Ω∗1, as shown Figure 8), E2 is a regular equilibrium, while the point E3 is a virtual equilibrium (denoted by the equilibrium ER2 and the equilibrium Ev3, respectively), and point E−s1 does not exist with the sliding mode domain. If IT<I∗3 (that is, Ω∗4; see Figure 8), E3 is a regular equilibrium, while point E2 is a virtual equilibrium (denoted by ER3 and Ev2), and point E−s1 does not exist with the sliding mode domain.
Next, we choose q and IT as bifurcation parameters, and the other parameters are fixed. From Proposition 1 in this paper, the lines to divide the relevant parameter plane are given as follows:
l4:={(q,IT)|IT=I∗T=qβp+(γ−qp)2K4γ(d+δ+r)}, |
l5:={(q,IT)|IT=qβp}. |
The two solid lines l4 and l5 divide the q−IT two-dimensional plane space into three regions in the first quadrant R+. Suppose that the control value IT satisfies IT>I∗T (that is, region Ω7; see Figure 9(a)), then system (3.1) has no equilibrium. If the control value IT satisfies I∗T>IT>qβp (that is, region Ω6, as shown in Figure 9(a)), system (3.1) has two positive equilibria E+s1=(S+s1,IT) and E−s1=(S−s1,IT), where S±s1=(γ−qp)±√Δ12γK. When the control value IT satisfies 0<IT<qβp (that is, region Ω5; see Figure 9(a)), system (3.1) has a unique positive equilibrium Es2=(Ss2,IT), where Ss2=(γ−qp)+√Δ12γK.
We choose γ and IT as bifurcation parameters, and the other parameters are fixed. With Proposition 9 in this paper, the line to divide the relevant parameter plane is given as
l6:={(γ,IT)|IT=I∗′T=γK4(d+δ+r)}. |
The solid line l6 divides the γ−IT two-dimensional plane space into two regions in the first quadrant R+. Suppose that the control value IT satisfies IT>I∗′T (that is, region Ω8, as shown in Figure 9(b)), and then system (4.1) does not have an equilibrium. If the control value IT satisfies 0<IT<I∗′T (that is, region Ω9, as shown in Figure 9(b)), system (4.1) has two positive equilibria E+s3=(S+s3,IT) and E−s3=(S−s3,IT), where S±s3=γ±√Δ22γK.
Remark 3. Notice that[30] considered the global dynamics of a Filippov predator-prey model with two thresholds for integrated pest management. By using Filippov theory, the sliding mode dynamics and global dynamics were established. Different from [30], our paper shows the dynamic behavior of the Filippov model with respect to all possible equilibria. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or two endemic equilibria under some conditions. Second, although both this paper and [30] discuss the global dynamics of a Filippov model with two thresholds, this paper first gives different control strategies. In particular, the two real equilibria occur simultaneously using methods such as Green's theorem and a Dulac function.
In this paper, we have established a non-smooth system to determine whether it is necessary to adopt the control strategy of media coverage and quarantine of susceptible individuals according to the number of infected and susceptible individuals. Media coverage changes the transmission mode of influenza. Further, in order to reduce the spread of influenza, when the number of cases exceeds the larger infection threshold IT, and the number of susceptible individuals is greater than ST, we will quarantine the susceptible individuals. It is worth noting that there are two difficulties in this paper. First, the traditional continuity theory cannot be applied due to the non-smooth system with the broken line control strategy. For example, when proving the global stability of discontinuous systems, the traditional Lyapunov function cannot be similarly constructed. Second, Green's formula of continuous systems cannot be used to prove the existence of global stability of the pseudo equilibriums in discontinuous systems. In this paper, by choosing different thresholds IT and ST and using Filippov theory, we study the dynamic behavior of the Filippov model with respect to all possible equilibria. The regular/virtulal equilibrium bifurcations are given. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or two endemic equilibria under some conditions.
Next, we summarize the corresponding biological results of Tables 1–3. These results show that the choice of values of IT and ST is very important, and it determines whether to adopt control strategies.
Condition 1 | Condition 2 | Result | |
ST<S∗1 | (ST,IT)∈B1−1 | Figure 2(a) | |
S∗1<ST<S∗2 | (ST,IT)∈C1−1 | Figure 4(a) | |
S∗2<ST | (ST,IT)∈D1−1 | Figure 7(a) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B2−1 | Figure 2(b) |
(ST,IT)∈B3−1∪B3−2∪B3−3 | Figure 2(c) | |
S∗1<ST<S∗2 | (ST,IT)∈C2−1 | Figure 4(b) |
(ST,IT)∈C3−1 | Figure 4(c) | |
(ST,IT)∈C4−1 | Figure 4(d) | |
(ST,IT)∈C5−1∪C5−2∪C5−3∪C5−4 | Figure 4(e) | |
S∗2<ST | (ST,IT)∈D3−1∪D3−2D3−3 | Figure 7(c) |
(ST,IT)∈D5−1 | Figure 7(e) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B4−1 | Figure 2(d) |
(ST,IT)∈B5−1 | Figure 2(e) | |
S∗1<ST<S∗2 | (ST,IT)∈C6−1 | Figure 4(f) |
(ST,IT)∈C7−1 | Figure 5(a) | |
(ST,IT)∈C8−1∪C8−2∪C8−3∪C8−4 | Figure 5(b) | |
(ST,IT)∈C9−1 | Figure 5(c) | |
(ST,IT)∈C10−1∪C10−2 | Figure 5(e) | |
(ST,IT)∈C11−1∪C11−2 | Figure 5(e) | |
(ST,IT)∈C12−1 | Figure 5(f) | |
(ST,IT)∈C13−1 | Figure 6 | |
S∗2<ST | (ST,IT)∈D2−1 | Figure 7(b) |
(ST,IT)∈D4−1 | Figure 7(d) |
● In Table 1, we know that the infection threshold value IT is chosen to be small enough, i.e., I≪IT, and then the number of infected individuals will reach the equilibrium ER1 of system (2.4).
● From Table 2, system (2.4) has a unique globally asymptotically stable pseudo-equilibrium E+s1 or E+s3 if I=IT or admits a unique globally asymptotically stable equilibrium when I<IT. Our control goal can be achieved finally, and there is no need to adjust the threshold strategy.
● In Table 3, the solution of system (2.4) will converge to a locally asymptotically stable equilibrium if I<IT or tends to a locally asymptotically stable equilibrium ER3 when I>IT or pseudo-equilibrium E+s1,E+s3 if I=IT. We show that it may be necessary to adjust the threshold policy according to the initial number of susceptible individuals and infected individuals. The results obtained have certain guiding significance for choosing thresholds and designing a corresponding threshold strategy.
Next, we consider the effect of key parameters in the subsystem on the basic regeneration number R0i as follows.
The three-dimensional diagram of the parameter space (δ,d,R01) is shown in Figure 10(a) under the parameter values of K=4, β=0.5, r=0.2. It observe when the parameter δ=0.8,d increase from 0.83 to 1, the basic reproduction number R01 decreases correspondingly and is less than unit 1. The trajectory of the subsystem (2.4) will converge globally to the free equilibrium (see Theorem 1), implying that the infected individuals extinct and then a stable free steady state occurs.
The three-dimensional diagram of the parameter space (β,p,R02) is shown in Figure 10(b) under the parameter values of K=2, d=0.1, r=0.05, δ=0.05. It is easy to observe when fixing the p=0.5, β increase from 0.78 to 1, R02 decreases correspondingly and is less than unit 1. By using Theorem 1, the infected individuals persist and the trajectory of the subsystem (2.4) will converge globally to the endemic steady state.
The three-dimensional diagram of the parameter space (β,δ,R01) is shown in Figure 10(c) under the parameter values of K=2, d=0.1, r=0.1. We observe when the parameters δ,β increase from 0.6 to 1, R01 also increases correspondingly and is greater than the unit 1. By using Theorem 1, the infected individuals persist and the trajectory of the subsystem (2.4) will converge globally to the endemic steady state.
The three-dimensional diagram of the parameter space (β,p,R03) is shown in Figure 10(d) under the parameter values of K=1, γ=1.8, r=127, δ=127, d=127. It is easy to observe when fixing the parameter p=0.2, with a transmission rate β increase from 0.81 to 1, R03 increases correspondingly and is greater than the unit 1. By using Theorem 1, the infected individuals persist, and the trajectory of the subsystem (2.4) will converge globally to the endemic steady state. When fixing the parameters p=0.6, transmission rate β increase from 0.81 to 1, R03 decreases correspondingly and is less than the unit 1. The trajectory of the subsystem (2.4) will converge globally to the free equilibrium (see Theorem 1 of our paper), implying that the infected individuals become extinct, and then a stable free steady state occurs.
In addition, this model has not been validated by actual influenza data, and we only have analyzed theoretically. In the next stage, we will verify and simulate the validity of the conclusions in actual time from some websites and statistics of health departments. However, the paper has studied the non-smooth system of two threshold control strategies and has validated the correctness of the theory through numerical simulation. Due to the serious lack of current influenza data from SARS-CoV-2 infections, the verification of the work is extremely difficult. However, the theory of this paper can provide appropriate guidance for the current influenza by SARS-CoV-2 infection. In this paper, we only consider the dynamics of the system (2.4) if the basic reproduction number R0i>1. However, under the saturation rate βSI1+I, and the conditions R02<R01<1 and R02<1<R01, the dynamical behaviors of system (2.4) and the method of proving global stability are not yet fully clear and would be our further topic.
We sincerely thank the anonymous referees for their very detailed and helpful comments on which improved the quality of this paper. This work is supported in part by the Yunnan Fundamental Research Projects (No: 202101BE070001-051).
The authors have no conflict of interest to declare in carrying out this research work.
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1. | Wenjie Li, Yajuan Guan, Jinde Cao, Fei Xu, Global dynamics and threshold control of a discontinuous fishery ecological system, 2024, 182, 09600779, 114817, 10.1016/j.chaos.2024.114817 |
Condition 1 | Condition 2 | Result | |
ST<S∗1 | (ST,IT)∈B1−1 | Figure 2(a) | |
S∗1<ST<S∗2 | (ST,IT)∈C1−1 | Figure 4(a) | |
S∗2<ST | (ST,IT)∈D1−1 | Figure 7(a) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B2−1 | Figure 2(b) |
(ST,IT)∈B3−1∪B3−2∪B3−3 | Figure 2(c) | |
S∗1<ST<S∗2 | (ST,IT)∈C2−1 | Figure 4(b) |
(ST,IT)∈C3−1 | Figure 4(c) | |
(ST,IT)∈C4−1 | Figure 4(d) | |
(ST,IT)∈C5−1∪C5−2∪C5−3∪C5−4 | Figure 4(e) | |
S∗2<ST | (ST,IT)∈D3−1∪D3−2D3−3 | Figure 7(c) |
(ST,IT)∈D5−1 | Figure 7(e) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B4−1 | Figure 2(d) |
(ST,IT)∈B5−1 | Figure 2(e) | |
S∗1<ST<S∗2 | (ST,IT)∈C6−1 | Figure 4(f) |
(ST,IT)∈C7−1 | Figure 5(a) | |
(ST,IT)∈C8−1∪C8−2∪C8−3∪C8−4 | Figure 5(b) | |
(ST,IT)∈C9−1 | Figure 5(c) | |
(ST,IT)∈C10−1∪C10−2 | Figure 5(e) | |
(ST,IT)∈C11−1∪C11−2 | Figure 5(e) | |
(ST,IT)∈C12−1 | Figure 5(f) | |
(ST,IT)∈C13−1 | Figure 6 | |
S∗2<ST | (ST,IT)∈D2−1 | Figure 7(b) |
(ST,IT)∈D4−1 | Figure 7(d) |
Condition 1 | Condition 2 | Result | |
ST<S∗1 | (ST,IT)∈B1−1 | Figure 2(a) | |
S∗1<ST<S∗2 | (ST,IT)∈C1−1 | Figure 4(a) | |
S∗2<ST | (ST,IT)∈D1−1 | Figure 7(a) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B2−1 | Figure 2(b) |
(ST,IT)∈B3−1∪B3−2∪B3−3 | Figure 2(c) | |
S∗1<ST<S∗2 | (ST,IT)∈C2−1 | Figure 4(b) |
(ST,IT)∈C3−1 | Figure 4(c) | |
(ST,IT)∈C4−1 | Figure 4(d) | |
(ST,IT)∈C5−1∪C5−2∪C5−3∪C5−4 | Figure 4(e) | |
S∗2<ST | (ST,IT)∈D3−1∪D3−2D3−3 | Figure 7(c) |
(ST,IT)∈D5−1 | Figure 7(e) |
Condition 1 | Condition 2 | Result |
ST<S∗1 | (ST,IT)∈B4−1 | Figure 2(d) |
(ST,IT)∈B5−1 | Figure 2(e) | |
S∗1<ST<S∗2 | (ST,IT)∈C6−1 | Figure 4(f) |
(ST,IT)∈C7−1 | Figure 5(a) | |
(ST,IT)∈C8−1∪C8−2∪C8−3∪C8−4 | Figure 5(b) | |
(ST,IT)∈C9−1 | Figure 5(c) | |
(ST,IT)∈C10−1∪C10−2 | Figure 5(e) | |
(ST,IT)∈C11−1∪C11−2 | Figure 5(e) | |
(ST,IT)∈C12−1 | Figure 5(f) | |
(ST,IT)∈C13−1 | Figure 6 | |
S∗2<ST | (ST,IT)∈D2−1 | Figure 7(b) |
(ST,IT)∈D4−1 | Figure 7(d) |