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Effects of quantum noises on χ state-based quantum steganography protocol

  • Since the good application of quantum mechanism in the field of communication, quantum secure communication has become a research hotspot. The existing quantum secure communication protocols usually assume that the quantum channel is noise-free. But the inevitable quantum noise in quantum channel will greatly interferes the transmission of quantum bits or quantum states, seriously damaging the security and reliability of the quantum system. This paper analyzes and discusses the performance of a χ state based steganography protocol under four main quantum noises, i.e., Amplitude Damping (AD), Phase damping (Phs), Bit Flip (BF) and Depolarizing (D). The results show that the protocol is least affected by amplitude damping noise when only the sender's first transmission in quantum channel is affected by quantum noise. Then, we analyze the performance of the protocol when both the sender's two transmissions are affected by quantum noise, and find that the specific combination of different noises will increase the performance of the protocol in quantum noisy channel. This means that an extra quantum noise can be intentionally added to quantum channel according to the noise intensity, so that the protocol can improve performance under the influence of quantum noises. Finally, the detailed mathematical analysis proves the conclusions.

    Citation: Zhiguo Qu, Shengyao Wu, Le Sun, Mingming Wang, Xiaojun Wang. Effects of quantum noises on χ state-based quantum steganography protocol[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4999-5021. doi: 10.3934/mbe.2019252

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  • Since the good application of quantum mechanism in the field of communication, quantum secure communication has become a research hotspot. The existing quantum secure communication protocols usually assume that the quantum channel is noise-free. But the inevitable quantum noise in quantum channel will greatly interferes the transmission of quantum bits or quantum states, seriously damaging the security and reliability of the quantum system. This paper analyzes and discusses the performance of a χ state based steganography protocol under four main quantum noises, i.e., Amplitude Damping (AD), Phase damping (Phs), Bit Flip (BF) and Depolarizing (D). The results show that the protocol is least affected by amplitude damping noise when only the sender's first transmission in quantum channel is affected by quantum noise. Then, we analyze the performance of the protocol when both the sender's two transmissions are affected by quantum noise, and find that the specific combination of different noises will increase the performance of the protocol in quantum noisy channel. This means that an extra quantum noise can be intentionally added to quantum channel according to the noise intensity, so that the protocol can improve performance under the influence of quantum noises. Finally, the detailed mathematical analysis proves the conclusions.


    Since December 2019, the outbreak of the novel coronavirus pneumonia firstly occurred in Wuhan, a central and packed city of China [1,2]. The World Health Organization(WHO) has named the virus as COVID-19 On January 12, 2020. Recently, COVID-19 has spread to the vast majority of countries, as United States, France, Iran, Italy and Spain etc. The outbreak of COVID-19 has been become a globally public health concern in medical community as the virus is spreading around the world. Initially, the British government adopted a herd immunity strategy. As of March 27, cases of the COVID-19 coronavirus have been confirmed more than 11,000 mostly In the UK. The symptoms of COVID-19 most like SARS(Severe acute respiratory syndrome) and MERS(Middle East respiratory syndrome), include cough, fever, weakness and difficulty breath[3]. The period for such symptoms from mild to severe respiratory infections lasts 2–14 days. The transmission routes contain direct transmission, such as close touching and indirect transmission consist of the air by coughing and sneezing, even if contacting some contaminated things by virus particles. There are many mathematica models to discuss the dynamics of COVID-19 infection [4,5,6,7,8].

    Additionally, coronaviruses can be extremely contagious and spread easily from person to person[9]. So a series of stringent control measures are necessary. For some diseases, such as influenza and tuberculosis, people often introduce the latent compartment (denoted by E), leading to an SEIR model. The latent compartment of COVID-19 is highly contagious[10,11]. Such type of models have been widely discussed in recent decades [12,13,14].

    Media coverage is a key factor in the transmission process of infectious disease. People know more about the COVID-19 and enhance their self-protecting awareness by the media reporting about the COVID-19. People will change their behaviours and take correct precautions such as frequent hand-washing, wearing masks, reducing the party, keeping social distances, and even quarantining themselves at home to avoid contacting with others. Zhou et al.[15] proposed a deterministic dynamical model to examine the interaction of the disease progression and the media reports and to investigate the effectiveness of media reporting on mitigating the spread of COVID-19. The result suggested that media coverage can be considered as an effective way to mitigate the disease spreading during the initial stage of an outbreak.

    Quarantine is effective for the control of infectious disease. Chinese government advises all the Chinese citizens to isolate themselves at home, and people exposed to the virus have the medical observation for 14 days. In order to get closer to the reality, many scholars have introduced quarantine compartment into epidemic model. Amador and Gomez-Corral[16] studied extreme values in an SIQS model with two different states for quarantine, termed quarantined susceptible and quarantined infective, and limited carrying capacity for the quarantine compartment. Gao and Zhuang proposed a new VEIQS worm propagation model with saturated incidence and strategies of both vaccination and quarantine[17].

    Motivated by the above, we consider a new COVID-19 epidemic model with media coverage and quarantine. The model assumes that the latent stage has certain infectivity. And we also introduce the quarantine compartment into the epidemic model, and the susceptible have consciousness to checking the spread of infectious diseases in the media coverage.

    The organization of this paper is as follows. In the next section, the epidemic model with media coverage and quarantine is formulated. In section 3, the basic reproduction number and the existence of equilibria are investigated. In Section 4, the global stability of the disease free and endemic equilibria are proved. In Section 5, we use the MCMC algorithm to estimate the unknown parameters and initial values of the model. The basic reproduction number R0 of the model and its confidence interval are solved by numerical methods. At the same time, we obtain the sensitivity of the unknown parameters of the model. In the last section, we give some discussions.

    In this section, we introduce a COVID-19 epidemic model with media coverage and quarantine. The total population is partitioned into six compartments: the unconscious susceptible compartment (S1), the conscious susceptible compartment (S2), the latent compartment (E), the infectious compartment (I), the quarantine compartment (Q) and the recovered compartment (R). The total number of population at time t is given by

    N(t)=S1(t)+S2(t)+E(t)+I(t)+Q(t)+R(t).

    The parameters are described in Table 1. The population flow among those compartments is shown in the following diagram (Figure 1).

    Table 1.  Parameters of the model.
    Parameter Description
    Λ The birth rate of the population
    βE Transmission coefficient of the latent compartment
    βI Transmission coefficient of the infectious compartment
    σ The fraction of S2 being infected and entering E
    p The migration rate to S2 from S1, reflecting the impact of media coverage
    r The rate coefficient of transfer from the latent compartment
    q The fraction of the latent compartment E jump into the quarantine compartment Q
    1q The fraction of the latent compartment E jump into the infectious compartment I
    ε The transition rate to I for Q
    ξ The recovering rate of the quarantine compartment
    μ Naturally death rate
    d The disease-related death rate

     | Show Table
    DownLoad: CSV
    Figure 1.  The transfer diagram for the model (2.1).

    The transfer diagram leads to the following system of ordinary differential equations:

    {S1=ΛβES1EβIS1I(p+μ)S1,S2=pS1βEσS2EβIσS2IμS2,E=βEE(S1+σS2)+βII(S1+σS2)(r+μ)E,I=(1q)rE(μ+d+ε)I,Q=qrE+εI(μ+d+ξ)Q,R=ξQμR. (2.1)

    Since the sixth equation in system (2.1) is independent of other equations, system (2.1) may be reduced to the following system:

    {S1=ΛβES1EβIS1I(p+μ)S1,S2=pS1βEσS2EβIσS2IμS2,E=βEE(S1+σS2)+βII(S1+σS2)(r+μ)E,I=(1q)rE(μ+d+ε)I,Q=qrE+εI(μ+d+ξ)Q. (2.2)

    It is important to show positivity for the system (2.1) as they represent populations. We thus state the following lemma.

    Lemma 1. If the initial values S1(0)>0, S2(0)>0, E(0)>0, I(0)>0, Q(0)>0 and R(0)>0, the solutions S1(t), S2(t), E(t), I(t), Q(t) and R(t) of system (2.1) are positive for all t>0.

    Proof. Let W(t)=min{S1(t),S2(t),E(t),I(t),Q(t),R(t)}, for all t>0.

    It is clear that W(0)>0. Assuming that there exists a t1>0 such that W(t1)=0 and W(t)>0, for all t[0,t1).

    If W(t1)=S1(t1), then S2(t)0,E(t)0,I(t)0,Q(t)0,R(t)0 for all t[0,t1]. From the first equation of model (2.1), we can obtain

    S1βES1EβIS1I(p+μ)S1,t[0,t1]

    Thus, we have

    0=S1(t1)S1(0)et10[βEE+βII+(p+μ)]dt>0,

    which leads to a contradiction. Thus, S1(t)>0 for all t0.

    Similarly, we can also prove that S2(t)>0, E(t)>0, I(t)>0, Q(t)>0 and R(t)>0 for all t0.

    Lemma 2. The feasible region Ω defined by

    Ω={(S1(t),S2(t),E(t),I(t),Q(t),R(t))R6+:N(t)Λμ}

    with initial conditions S1(0)0,S2(0)0,E(0)0,I(0)0,Q(0)0,R(0)0 is positively invariant for system (2.1).

    Proof. Adding the equations of system (2.1) we obtain

    dNdt=ΛμNd(I+Q)ΛμN.

    It follows that

    0N(t)Λμ+N(0)eμt,

    where N(0) represents the initial values of the total population. Thus limt+supN(t)Λμ. It implies that the region Ω={(S1(t),S2(t),E(t),I(t),Q(t),R(t))R6+:N(t)Λμ} is a positively invariant set for system (2.1). So we consider dynamics of system (2.1) and (2.2) on the set Ω in this paper.

    The model has a disease free equilibrium (S01,S02,0,0,0), where

    S01=Λp+μ,S02=pΛμ(p+μ).

    In the following, the basic reproduction number of system (2.2) will be obtained by the next generation matrix method formulated in [18].

    Let x=(E,I,Q,S1,S2)T, then system (2.2) can be written as

    dxdt=F(x)V(x),

    where

    F(x)=(βEE(S1+σS2)+βII(S1+σS2)0000),V(x)=((r+μ)E(μ+d+ε)I(1q)rE(μ+d+ξ)QqrEεIβES1E+βIS1I+(p+μ)S1ΛβEσS2E+βIσS2I+μS2pS1). (3.1)

    The Jacobian matrices of F(x) and V(x) at the disease free equilibrium P0 are, respectively,

    DF(P0)=(F3×3000),DV(P0)=(V3×300βES01βIS010p+μ0βEσS02βIσS020pμ), (3.2)

    where

    F=(βE(S01+σS02)βI(S01+σS02)0000000),V=(r+μ00(1q)rμ+d+ε0qrεμ+d+ξ).

    The model reproduction number, denoted by R0 is thus given by

    R0=ρ(FV1)=(S01+σS02)[βE(μ+d+ε)+βI(1q)r](r+μ)(μ+d+ε)=(S01+σS02)(βEA+βIB)(r+μ)A. (3.3)

    where

    A=μ+d+ε,B=(1q)r. (3.4)

    The endemic equilibrium P(S1,S2,E,I,Q) of system (2.2) is determined by equations

    {ΛβES1EβIS1I(p+μ)S1=0,pS1βEσS2EβIσS2IμS2=0,βEE(S1+σS2)+βII(S1+σS2)(r+μ)E=0,(1q)rE(μ+d+ε)I=0,qrE+εI(μ+d+ξ)Q=0. (3.5)

    The first two equations in (3.5) lead to

    S1=ΛβEE+βII+(p+μ),S2=pΛ[βEE+βII+(p+μ)](βEσE+βIσI+μ). (3.6)

    From the fourth equation in (3.5), we have

    E=μ+d+ε(1q)rI=ABI. (3.7)

    Substituting (3.7) into the last equation in (3.5) gives

    Q=q(μ+d+ε)+(1q)ε(1q)(μ+d+ξ)I=Aqr+Bε(μ+d+ξ)BI. (3.8)

    For I0, substituting (3.7) into the third equation in (3.5) gives

    S1+σS2=(r+μ)AβEA+βIB (3.9)

    From (3.6) and (3.7), we have

    S1+σS2=ΛβEE+βII+(p+μ)+σpΛ[βEE+βII+(p+μ)](βEσE+βIσI+μ)=ΛβEE+βII+(p+μ)[1+σpσ(βEE+βII)+μ]=ΛβEE+βII+(p+μ)σ(βEE+βII)+μ+pσσ(βEE+βII)+μ=ΛβEA+βIBBI+(p+μ)σβEA+βIBBI+μ+pσσβEA+βIBBI+μ=BΛ(βEA+βIB)I+(p+μ)Bσ(βEA+βIB)I+(μ+pσ)Bσ(βEA+βIB)I+μB. (3.10)

    Substituting (3.9) into (3.10) yields

    H(I):=B[σ(βEA+βIB)I+(μ+pσ)B][(βEA+βIB)I+(p+μ)B][σ(βEA+βIB)I+μB](r+μ)A(βEA+βIB)Λ=0. (3.11)

    Direct calculation shows

    H(I)=H1(I){[(βEA+βIB)I+(p+μ)B][σ(βEA+βIB)I+μB]}2,

    Denote C=βEA+βIB,

    H1(I)=Bσ(βEA+βIB)[(βEA+βIB)I+(p+μ)B][σ(βEA+βIB)I+μB]B[σ(βEA+βIB)I+(μ+pσ)B](βEA+βIB){[σ(βEA+βIB)I+μB]+σ[(βEA+βIB)I+(p+μ)B]}=σBC[CI+(p+μ)B][σCI+μB]BC[σCI+(μ+pσ)B]{σCI+μB+σ[CI+(p+μ)B]}=BC{σ[CI+(p+μ)B][σCI+μB][σCI+(μ+pσ)B][σCI+μB+σ[CI+(p+μ)B]]}=BC{(σμBμB)(σCI+μB)[σCI+(μ+pσ)B]σ[CI+(p+μ)B]}=BC{μB(σCI+μB)+σ[σC2I2+σpBCI+C(μ+pσ)BI+[(μ+pσ)p+pσμ]B2]}=BC{(σC)2I2+2σBC(μ+σp)I+B2[μ2+μpσ+p(p+μ)σ2]}<0.

    then

    H(I)<0.

    then function H(I) is decreasing for I>0. Since [(βEA+βIB)I+(p+μ)B][σ(βEA+βIB)I+μB]>(βEA+βIB)I[σ(βEA+βIB)I+(μ+pσ)B], then

    H(I)<B(βEA+βIB)I(r+μ)A(βEA+βIB)Λ.

    Thus,

    H(0)=μ+pσμ(p+μ)(r+μ)A(βEA+βIB)Λ=(S01+σS02)Λ(r+μ)A(βEA+βIB)Λ=(βEA+βIB)(S01+σS02)(r+μ)A(βEA+βIB)Λ=(r+μ)AR0(r+μ)A(βEA+βIB)Λ=(r+μ)A(βEA+βIB)Λ(R01),

    and

    H(Λμ)<μB(βEA+βIB)Λ(r+μ)A(βEA+βIB)Λ=μB(r+μ)A(βEA+βIB)Λ=μ(1q)r(r+μ)(μ+d+ε)(βEA+βIB)Λ=(r+μ)(d+ε)+(qr+μ)μ(βEA+βIB)Λ<0.

    Therefore, by the monotonicity of function H(I), for (3.11) there exists a unique positive root in the interval (0,Λμ) when R0>1; there is no positive root in the interval (0,Λμ) when R01. We summarize this result in Theorem 3.1.

    Theorem 3.1. For system (2.2), there is always the disease free equilibrium P0(S01,S02,0,0,0). When R0>1, besides the disease free equilibrium P0, system (2.2) also has a unique endemic equilibrium P(S1,S2,E,I,Q), where

    S1=BΛ(βEA+βIB)I+(p+μ)B,S2=pΛB2[(βEA+βIB)I+(p+μ)B][(βEA+βIB)σI+μB],E=ABI,Q=Aqr+Bε(μ+d+ξ)BI.

    and I is the unique positive root of equation H(I)=0.

    Theorem 4.1. For system (2.2), the disease free equilibrium P0 is globally stable if R01; the endemic equilibrium P is globally stable if R0>1.

    For the disease free equilibrium P0(S01,S02,0,0,0), S01 and S02 satisfies equations

    {ΛβES1EβIS1I(p+μ)S1=0,pS1βEσS2EβIσS2IμS2=0, (4.1)

    then (2.2) can be rewritten as follows:

    {S1=S1[Λ(1S11S01)βEEβII],S2=S2[p(S1S2S01S02)βEσEβIσI],E=(βEE+βII)[(S01+σS02)+(S1S01)+σ(S2S02)](r+μ)E,I=(1q)rE(μ+d+ε)I,Q=qrE+εI(μ+d+ξ)Q (4.2)

    Define the Lyapunov function

    V1=(S1S01S01lnS1S01)+(S2S02S02lnS2S02)+E+(r+μ)βE(S01+σS02)(1q)rI. (4.3)

    The derivative of V1 is given by

    V1=(S1S01)[Λ(1S11S01)βEEβII]+(S2S02)[p(S1S2S01S02)βEσEβIσI]+(βEE+βII)[(S01+σS02)+(S1S01)+σ(S2S02)](r+μ)E+(r+μ)βE(S01+σS02)(1q)r[(1q)rE(μ+d+ε)I]=βI(S01+σS02)I[(r+μ)βE(S01+σS02)](μ+d+ε)(1q)rI+F(S,I)=(r+μ)(μ+d+ε)(1q)r(R01)I+F(S,I),=(r+μ)AB(R01)I+F(S,I), (4.4)

    where

    F(S,I)=Λ(S1S01)(1S11S01)+p(S2S02)(S1S2S01S02).

    Denote x=S1S01,y=S2S02, then

    F(S,I)=Λ(x1)(1x1)+pS01(y1)(xy1)=:¯F(x,y). (4.5)

    Applying (4.1) to function ¯F(x,y) yields

    ¯F(x,y)=Λ(2x1x)+pS01(xyxy+1)=(2Λ+pS01)(ΛpS01)xΛ1xpS01ypS01xy=3pS01+2μS01μS01x(pS01+μS01)1xpS01ypS01xy=pS01(31xyxy)+μS01(2x1x). (4.6)

    We have ¯F(x,y)0 for x,y>0 and ¯F(x,y)=0 if and only if x=y=1. Since R01, then V10. It follows from LaSalle invariance principle [19] that the disease free equilibrium P0 is globally asymptotically stable when R01.

    For the endemic equilibrium P(S1,S2,E,I,Q), S1,S2,E,I, and Q satisfies equations

    {ΛβES1EβIS1I(p+μ)S1=0,pS1βEσS2EβIσS2IμS2=0,βEE(S1+σS2)+βII(S1+σS2)(r+μ)E=0,(1q)rE(μ+d+ε)I=0,qrE+εI(μ+d+ξ)Q=0. (4.7)

    By applying (4.7) and denoting

    S1S1=x,S2S2=y,EE=z,II=u,QQ=v

    we have

    {x=x[ΛS1(1x1)βEE(z1)βII(u1)],y=y[pS1S2(xy1)βEσE(z1)βIσI(u1)],z=z{βE[S1(x1)+σS2(y1)]+βIIE[S1(xuz1)+σS2(yuz1)]},u=u(1q)rEI(zu1),v=v[qrEQ(zv1)+εIQ(uv1)]. (4.8)

    Define the Lyapunov function

    V2=S1(x1lnx)+S2(y1lny)+E(z1lnz)+βI(r+μ)βEA+βIBI(u1lnu). (4.9)

    The derivative of V2 is given by

    V2=S1x1xx+S2y1yy+Ez1zz+βI(r+μ)βEA+βIBIu1uu=(x1)[Λ(1x1)βES1E(z1)βIS1I(u1)]+(y1)[pS1(xy1)βEσS2E(z1)βIσS2I(u1)]+(z1){βEE[S1(x1)+σS2(y1)]+βII[S1(xuz1)+σS2(yuz1)]}+βI(r+μ)βEA+βIB(u1)(1q)rE(zu1)=Λ(x1)(1x1)βIS1I(x1)(u1)+pS1(y1)(xy1)βIσS2I(y1)(u1)+βIS1I(z1)(xuz1)+βIσS2I(z1)(yuz1)+βI(r+μ)βEA+βIB(1q)rE(u1)(zu1)=Λ(2x1x)βIS1I(xuxu1)+pS1(xyxy+1)βIσS2I(yuyu+1)+βIS1I(xuzxuz+1)+βIσS2I(yuzyuz+1)+βI(r+μ)βEA+βIB(1q)rE(zuzu+1)=2Λ+pS1+βI(r+μ)βEA+βIB(1q)rEx(ΛβIS1IpS1)Λ1xy(pS1βIσS2I)pS1xyβIS1IxuzβIσS2IyuzβI(r+μ)βEA+βIB(1q)rEzu=(ΛβIS1IpS1)(2x1x)+(pS1βIσS2I)(31xyxy)+βIS1I(31xxuzzu)+βIσS2I(41xxyyuzzu) (4.10)

    Since the arithmetical mean is greater than, or equal to the geometrical mean, then, 2x1x0 for x>0 and 2x1x=0 if and only if x=1; 31xyxy0 for x,y>0 and 31xyxy=0 if and only if x=y=1; 31xxuzzu0 for x,z,u>0 and 31xxuzzu=0 if and only if x=1,z=u; 41xxyyuzzu0 for x,y,z,u>0 and 41xxyyuzzu=0 if and only if x=y=1,z=u. Therefore, V20 for x,y,z,u>0 and V2=0 if and only if x=y=1,z=u, the maximum invariant set of system (2.2) on the set {(x,y,z,u):V2=0} is the singleton (1,1,1,1). Thus, for system (2.2), the endemic equilibrium P is globally asymptotically stable if R0>1 by LaSalle Invariance Principle [19].

    In this section, we estimate the unknown parameters of model (2.2) on the basis of the total confirmed new cases in the UK from February 1, 2020 to March 23, 2020 by using MCMC algorithm. By estimating the unknown parameters, we estimate the mean and confidence interval of the basic reproduction number R0.

    The total confirmed cases can be expressed as follows

    dCdt=qrE+εI,

    where C(t) indicates the total confirmed cases.

    As for the total confirmed new cases, it can be expressed as following

    NC=C(t)C(t1), (5.1)

    where NC represents the total confirmed new cases.

    We use the MCMC method [20,21,22] for 20000 iterations with a burn-in of 5000 iterations to fit the Eq (5.1) and estimate the parameters and the initial conditions of variables (see Table 2). Figure 2 shows a good fitting between the model solution and real data, well suggesting the epidemic trend in the United Kingdom. According to the estimated parameter values and initial conditions as given in Table 2, we estimate the mean value of the reproduction number R0=4.2816 (95%CI:(3.8882,4.6750)).

    Table 2.  The parameters values and initial values of the model (2.2).
    Parameters Mean value Std 95% CI Reference
    Λ 0 Estimated
    μ 0 Estimated
    d 1/18 [23]
    γ 1/7 [24]
    ξ 1/18 [23]
    βE 5.1561×109 5.1184×1010 [0.4153×108, 0.6159×108] MCMC
    βI 2.3204×108 3.1451×109 [0.1704×107, 0.2937×107] MCMC
    p 0.5961 0.0377 [0.5223, 0.6700] MCMC
    q 0.2250 0.0787 [0.0707, 0.3793] MCMC
    σ 0.3754 0.0135 [0.3490, 0.4018] MCMC
    ε 0.2642 0.0598 [0.1470, 0.3814] MCMC
    S1(0) 3.3928×107 2.9130×106 [2.8219×107, 3.9638×107] Calculated
    S2(0) 3.2645×107 2.9130×106 [2.6936×107, 3.8355×107] MCMC
    E(0) 12.8488 3.2238 [6.5302, 19.1674] MCMC
    I(0) 0.8070 0.7739 [0, 2.3239] MCMC
    Q(0) 2 [25]
    R(0) 0 Estimated
    N(0) 66573504 [26]

     | Show Table
    DownLoad: CSV
    Figure 2.  The fitting results of the total confirmed new cases from February 1, 2020 to March 23, 2020. The blue line is the simulated curve of model (2.2). The red dots represent the actual data. The light blue area is the 95% confidence interval (CI) for all 5000 simulations.

    Applying the estimated parameter values, without the most restrictive measures in UK, we forecast that the peak size is 1.2902×106 (95%CI:(1.1429×106,1.4374×106)), the peak time is June 2 (95%CI:(May23,June13)) (Figure 3a), and the final size is 4.9437×107 (95%CI:(4.7199×107,5.1675×107)) in the UK (Figure 3b).

    Figure 3.  (a) Forecasting trends of total confirmed new cases. (b) Forecasting trends of total confirmed cases. The blue line is the simulated curve of model (2.2). The red dots represent the actual data. The light blue area is the 95% confidence interval (CI) for all 5000 simulations.

    In this section, we do the sensitivity analysis for four vital model parameters σ, p, q and ε, which reflect the intensity of contact, media coverage and isolation, respectively.

    Figure 4 and Table 3 show that reducing the fraction σ of the conscious susceptible S2 contacting with the latent compartment (E) and the infectious compartment (I) delays the peak arrival time, decreases the peak size of confirmed cases and decreases the final size. Reducing the fraction σ is in favor of controlling COVID-19 transmission.

    Figure 4.  (a) Effects of different Parameter σ on The numbers of total confirmed new cases. (b) Effects of different Parameter σ on The numbers of total confirmed cases.
    Table 3.  Epidemic quantities for the UK.
    Parameter Peak size Peak time Final size
    σ=0.3652 1.2056×106 May 23 4.9766×107
    σ=0.32 9.4930×105 June 25 4.5390×107
    σ=0.3 8.1063×105 July 13 4.3121×107
    p=0.5961 1.3056×106 May 23 4.9766×107
    p=1/20 1.3248×106 May 19 4.9864×107
    p=1/40 1.5009×106 May 3 5.0699×107
    q=0.2250 1.3056×106 May 23 4.9766×107
    q=0.3375 1.1379×106 June 6 4.8471×107
    q=0.4500 9.4266×105 June 27 4.6141×107
    ε=0.2642 1.3056×106 May 23 4.9766×107
    ε=0.3170 1.1766×106 June 2 4.8598×107
    ε=0.3963 9.9640×105 June 18 4.6145×107

     | Show Table
    DownLoad: CSV

    Figure 5 and Table 3 show that reducing migration rate p to S2 from S1, reflecting the impact of media coverage advances the peak arrival time, increase the peak size of confirmed cases and the final size.

    Figure 5.  (a) Effects of different Parameter p on The numbers of total confirmed new cases. (b) Effects of different Parameter p on The numbers of total confirmed cases.

    Figures 6, 7 and Table 3 show that, with increase the fraction q (Individuals in the latent compartment E jump into the quarantine compartment Q) and the transition rate ε (the infectious compartment I jump into the quarantine compartment Q), the peak time delays, the the peak size and the final size decrease. This show that Increasing the intensity of detection and isolation may affect the spread of COVID-19.

    Figure 6.  (a) Effects of different Parameter q on The numbers of total confirmed new cases. (b) Effects of different Parameter q on The numbers of total confirmed cases.
    Figure 7.  (a) Effects of different Parameter ε on The numbers of total confirmed new cases. (b) Effects of different Parameter ε on The numbers of total confirmed cases.

    Then the test set data is used to verify the short-term prediction effect of the model (Figure 8). we fit the model with the total confirmed new cases from February 1, 2020 to March 23, 2020, and verify the fitting results with the new cases from March 24, 2020 to April 12, 2020. The model has a good fit to the trajectory of the coronavirus prevalence for a short time in the UK.

    Figure 8.  The test set data is used to verify the short-term prediction effect of the model.

    We have formulated the COVID-19 epidemic model with media coverage and quarantine and investigated their dynamical behaviors. By means of the next generation matrix, we obtained their basic reproduction number, R0, which play a crucial role in controlling the spread of COVID-19. By constructing Lyapunov function, we proved the global stability of their equilibria: when the basic reproduction number is less than or equal to one, all solutions converge to the disease free equilibrium, that is, the disease dies out eventually; when the basic reproduction number exceeds one, the unique endemic equilibrium is globally stable, that is, the disease will persist in the population and the number of infected individuals tends to a positive constant. We use the MCMC algorithm to estimate the unknown parameters and initial values of the model (2.2) on the basis of the total confirmed new cases in the UK. The sensitivity of all parameters are evaluated.

    Through the mean and confidence intervals of the parameters in Table 2, we obtain the basic reproduction number R0=4.2816(95%CI:(3.8882,4.6750)), which means that the novel coronavirus pneumonia is still pandemic in the crowd. The sensitivity of the parameters provides a possible intervention to reduce COVID-19 infection. People should wear masks, avoid contact or reduce their outings, take isolation measure to reduce the spread of virus during COVID-19 outbreaks.

    We are grateful to the anonymous referees and the editors for their valuable comments and suggestions which improved the quality of the paper. This work is supported by the National Natural Science Foundation of China (11861044 and 11661050), and the HongLiu first-class disciplines Development Program of Lanzhou University of Technology.

    The authors declare there is no conflict of interest.



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