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Research article Special Issues

Exploring the mechanisms behind the country-specific time of Zika virusimportation

  • Received: 06 December 2018 Accepted: 03 April 2019 Published: 16 April 2019
  • The international spread of Zika virus (ZIKV) began in Brazil in 2015. To estimate the risk of observing imported ZIKV cases, we calculated effective distance, typically an excellent predictor of arrival time, from airline network data. However, we eventually concluded that, for ZIKV, effective distance alone is not an adequate predictor of arrival time, which we partly attributed to the difficulty of diagnosing and ascertaining ZIKV infections. Herein, we explored the mechanisms behind the observed time delay of ZIKV importation by country, statistically decomposing the delay into two parts: the actual time to importation from Brazil and the reporting delay. The latter was modeled as a function of the gross domestic product (GDP) and other variables that influence underlying diagnostic capacity in a given country. We showed that a high GDP per capita is a good predictor of short reporting delay. ZIKV infection is generally mild and, without substantial laboratory capacity, cases can be underestimated. This study successfully demonstrates this phenomenon and emphasizes the importance of accounting for reporting delays as part of the data generating process for estimating time to importation.

    Citation: Nao Yamamoto, Hyojung Lee, Hiroshi Nishiura. Exploring the mechanisms behind the country-specific time of Zika virusimportation[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 3272-3284. doi: 10.3934/mbe.2019163

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  • The international spread of Zika virus (ZIKV) began in Brazil in 2015. To estimate the risk of observing imported ZIKV cases, we calculated effective distance, typically an excellent predictor of arrival time, from airline network data. However, we eventually concluded that, for ZIKV, effective distance alone is not an adequate predictor of arrival time, which we partly attributed to the difficulty of diagnosing and ascertaining ZIKV infections. Herein, we explored the mechanisms behind the observed time delay of ZIKV importation by country, statistically decomposing the delay into two parts: the actual time to importation from Brazil and the reporting delay. The latter was modeled as a function of the gross domestic product (GDP) and other variables that influence underlying diagnostic capacity in a given country. We showed that a high GDP per capita is a good predictor of short reporting delay. ZIKV infection is generally mild and, without substantial laboratory capacity, cases can be underestimated. This study successfully demonstrates this phenomenon and emphasizes the importance of accounting for reporting delays as part of the data generating process for estimating time to importation.


    Throughout history, epidemics have been a serious threat to human survival and development, and human has been committed to find effective ways to prevent and control epidemics. Mathematical modeling provides a useful way to understand the dynamics of transmission of an infectious disease, and in the process, it provides effective guides and strategies for the control of diseases. One of the earliest mathematical models in epidemiology was introduced by Kermack and McKendrick [1]. The Kermack-McKendrick model is a compartmental model based on relatively simple assumptions on the rates of flow between different classes of members of the population [2], and it plays an important role in characterizing the transmission dynamics of disease within a short outbreak period. Following the work of Kermack and McKendrick, a large number of researchers pay attention to the study of epidemic models (see, e.g., [2,3,4,5]).

    In the earlier epidemic models, the attributes of individuals such as contact patterns, models of transmission and geographic distributions are assumed to be homogeneous. In population biology, however, the life cycle of many populations go through different stages and the habits are different at different stages (see, e.g., [5,6]). During the last decades, quite a few stage-structured models have been used to characterize the population dynamics, see, e.g., [2,5,7]. In epidemiology, empirical evidence indicates that developmental stages of population have a profound impact on the transmission dynamics of some diseases, and the individuals in some stages don't participate in the infection cycle and don't have the reproductive capacity. For example, TB is highly age-dependent (see [8]), and the population who is likely to be infected is the adult individuals since newborns are vaccinated in many countries, but the immunity has decreased with increasing age. Moreover, the biological cycle of some species goes through some separate and distinct stages, and the biological and epidemiological properties of different stages are quite different. For example, the biological cycle of some salmon species goes through three stages: egg, juvenile and adult fish, and juveniles mature in the fresh water stream [9], so that the individuals in egg and juvenile stages do not participate in the infection cycle of some diseases for mature individuals. It is therefore necessary to incorporate the stage structure into the epidemic models. When the stage structure is introduced, there will be a time-delay term in the model, see, e.g., [5].

    In epidemiology, another time-delay factor which should be considered is the latency. Empirical observation shows that many diseases have incubation period which differs from disease to disease, and the habit at latent stage is different at infectious stage (see, e.g., [4]). Therefore, it is significant that brings incubation period into epidemic models. During the past decades, a lot of models with latency have been studied and used to characterize the disease transmission. An SEIRS epidemic model with latent and immune periods was formulated and analyzed by Cooke and van den Driessche [10]. Li and Zou [11,12] generalized SIR model to patchy environments with incubation period. In [11], they found that the disease exists multiple outbreaks before it goes to extinction, which is in sharp contrast to the dynamics of classic Kermack-McKendrick SIR model.

    Note that the aforementioned models are all given by autonomous systems of differential equations. Autonomous systems of differential equations provide an appropriate characterization for the spread of epidemics. However, certain diseases admit seasonal fluctuation and it is now well known that seasonal changes are ubiquitous and can exert strong influence on the spread of infectious diseases. Recently, the interaction of time delay and seasonality in epidemic models has attracted much attention. Zhao [13] established the theory of basic reproduction ratio for periodic and time-delayed compartmental population models, and considered a periodic SEIR model with an incubation period. More and more attentions pay to the study of periodic epidemic models with latency since then, see e.g., [14,15,16,17].

    In reality, due to the stochastic effects and the complexity of external environments, fluctuations in nature may not exactly periodic, and hence, the parameters in an epidemic model are not necessary to be periodic. Even if they are periodic, they are also not always share a common period. Though one can obtain the exactly periodic parameters in controlled laboratory experiments, as noted in [18,19], environmental changes in nature are hardly periodic. In an almost periodic epidemic model, the time almost periodic dependence reflects the influence of certain "seasonal" fluctuations which are roughly but not exactly periodic. Therefore, almost periodic epidemic models are significant in the study of dynamics of infectious diseases. Up to now, there have been a few works concerning the global dynamics for almost periodic epidemic models with stage structure and latency, in which the maturation and latency periods are assumed to be constants (see, e.g., [20,21]). However, for the time-dependent case, the global dynamics of epidemic models dose not have an adequate characterization.

    It is worth noting that for some populations, the maturation period and latency of some diseases also depend on environment factors. For example, fish egg development time to hatching is temperature dependent, see, e.g., [22,23], and the lengths of the latent periods of some diseases for fish are also temperature dependent, such as furunculosis [24,25]. Based on this observation, population and epidemiology models concerning time-dependent delay were developed in quite a few works, see, e.g., [26,27,28]. In particular, Li and Zhao [29] derived and studied an SEIRS epidemic model with a time-dependent latent period in a periodic environment. To our best knowledge, however, there is no work at present of considering the interaction of time-dependent maturation and incubation periods in the directly transmitted diseases models. The purpose of this paper is to study the global dynamics of an SEIR models with maturation and incubation delays in an almost periodic environment, in which the time delays are time-dependent.

    In this paper, we employ an almost periodic time-delayed system framework to evaluate the effects of seasonal fluctuations, stage structure and latent period on the spread of infectious disease. The remaining parts are organized as follows. In section 2, we formulate an almost periodic SEIR model with time-dependent time delays, and give the underlying assumptions. In section 3, we introduce the basic reproduction ratios ˆR0 and R0 for population and disease, respectively, and establish threshold-type results on the global dynamics in terms of basic reproduction ratios. In section 4, we present some numerical simulations to interpret the obtained theoretical results, and reveal the effect of parameters on R0.

    Let (X,d) be a metric space. A function fC(R,X) is said to be almost periodic if for any ϵ>0, the set

    T(f,ϵ):={sR:d(f(t+s)f(t))<ϵ,tR}

    is a relatively dense subset of R, that is, there exists a positive number h>0 such that [c,c+h]T(f,ϵ), cR. Let AP(R,X):={fC(R,X): f is an almost periodic function}. Then AP(R,X) is a Banach space equipped with the supremum norm . A function fC(D×R,X) is said to be uniformly almost periodic in t if f(x,) is almost periodic for each xD, and for any compact set ED, f is uniformly continuous on E×R ([30,31]).

    We consider a disease for some population with immature and mature stages, where the immature individuals don't have reproductive capacity, and use G(t) and N(t) to denote the total numbers of immature and mature individuals. Let α(t) represent the maturation delay. Mathematically, we assume that α(t) is continuously differential in R. We use b(t,N) to denote the per-capita birth rate of the population, and let d1(t) and μ(t) be the natural death rates of immature and mature individuals, respectively. In the absence of disease, it then follows from [32] that the change of N(t) is governed by equation

    dN(t)dt=(1α(t))Λ(tα(t),N(tα(t)))ettα(t)d1(r)drμ(t)N(t), (2.1)

    where Λ(t,N)=b(t,N)N. In order to determine the sign of 1α(t), we introduce the development rate of immature stage at time t, denoted by k(t)ε>0 (see, e.g., [28]). It then follows that the following relationship holds,

    ttα(t)k(s)ds=1. (2.2)

    Differentiating equation (2.2) with respect to time t, we get

    1α(t)=k(t)k(tα(t)).

    It then follows that there exists a ρ>0 such that ρ<1α(t)<1ρ.

    We assume that only mature individuals participate in the infection cycle, and let S(t), E(t), I(t) and R(t) represent the total numbers of the susceptible, exposed, infective and recovered populations at time t, respectively. Moreover, we assume that there is no vertical infection. Base on these assumptions, the schematic diagram of SEIR model is given in Figure 1. We adopt the mass action infection mechanism that the lost of susceptible individuals by infection is at a rate proportional to the number of infectious individuals. Then we get the following system

    dS(t)dt=(1α(t))Λ(tα(t),N(tα(t))))ettα(t)d1(r)drβ(t)S(t)I(t)μ(t)S(t),dE(t)dt=β(t)S(t)I(t)μ(t)E(t)C(t),dI(t)dt=C(t)(μ(t)+d(t)+γ(t))I(t),dR(t)dt=γ(t)I(t)μ(t)R(t). (2.3)
    Figure 1.  Compartmental model for the disease.

    Here, β(t) is the infection rate, d(t) represents the disease-induced death rate, γ(t) is the recovery rate, and C(t) denotes the number of newly occurred infectious population per unit time at time t. Let τ(t) represent the time-dependent latent period. Mathematically, we also assume that τ(t) is continuously differential in R. In consideration of biological meanings, the following compatibility condition should be imposed:

    E(0)=0τ(0)e0sμ(r)drβ(s)S(s)I(s)ds.

    Moreover, we have

    E(t)=ttτ(t)etsμ(r)drβ(s)S(s)I(s)ds.

    It is then necessary to determine C(t). By the results in [29,section 2], it follows that

    C(t)=(1τ(t))ettτ(t)μ(r)drβ(tτ(t))S(tτ(t))I(tτ(t)).

    Thus, the SEIR model is governed by the following system,

    dS(t)dt=(1α(t))Λ(tα(t),N(tα(t)))ettα(t)d1(r)drβ(t)S(t)I(t)μ(t)S(t),dE(t)dt=β(t)S(t)I(t)(1τ(t))ettτ(t)μ(r)drβ(tτ(t))S(tτ(t))I(tτ(t))μ(t)E(t),dI(t)dt=(1τ(t))ettτ(t)μ(r)drβ(tτ(t))S(tτ(t))I(tτ(t))(μ(t)+d(t)+γ(t))I(t),dR(t)dt=γ(t)I(t)μ(t)R(t). (2.4)

    By an argument similar to that of α(t), we get that there exists an η>0 such that η<1τ(t)<1η. Considering the effect of environmental factors on disease transmission, we assume that b(t,N) is uniformly almost periodic and the time-dependent coefficients of system (2.4) are all almost periodic in t. By [32,Lemma 2.2], it follows that ettα(t)d1(r)dr and ettτ(t)μ(r)dr are almost periodic.

    For the sake of convenience, we let

    p(t):=(1α(t))ettα(t)d1(r)dr  and  q(t):=(1τ(t))ettτ(t)μ(r)dr.

    Note that we can drop the E and R equations in model (2.4), and the system (2.4) can be rewritten as

    dN(t)dt=p(t)Λ(tα(t),N(tα(t)))μ(t)N(t)d(t)I(t),dS(t)dt=p(t)Λ(tα(t),N(tα(t))))β(t)S(t)I(t)μ(t)S(t),dI(t)dt=q(t)β(tτ(t))S(tτ(t))I(tτ(t))(μ(t)+d(t)+γ(t))I(t). (2.5)

    In order to investigate the dynamics of system (2.5), we make the following assumptions (see, e.g., [5,32]).

    (A1) β(t), μ(t), d(t) and γ(t) are nonnegative and continuous functions, and inftRμ(t)>0.

    (A2) b(t,N) is continuous, and satisfies inftRb(t,0)>0 and b(t,N)N<0, N0, tR.

    (A3) There exists a constant K>0 such that μ(t)>p(t)b(tα(t),K) holds for all tR when KK.

    Let α=suptRα(t), τ=suptRτ(t), and L=max{α,τ}. We define CL:=C([L,0],R3). The norm of CL is defined by φ=maxθ[L,0]φ(θ)R3. Define positive cone C+L:=C([L,0],R3+). It is clear that the interior of C+L, Int(C+L)=C([L,0],Int(R3+)), is nonempty. Then the ordering on CL generated by C+L is defined as follows,

    φψφi(θ)ψi(θ),θ[L,0], 1i3,φ<ψφi(θ)ψi(θ), φψ,θ[L,0], 1i3,φψφi(θ)<ψi(θ),θ[L,0], 1i3.

    For a continuous function fC([L,T),R3) (T>0), define ftCL by ft(θ)=f(t+θ), θ[L,0], t[0,T).

    Lemma 2.1. Let (A1)-(A3) hold. Then system (2.5) admits a unique nonnegative and bounded solution on [0,) with the initial datum ϕC+L.

    Proof. For any ϕ=(ϕ1,ϕ2,ϕ3)C+L, we define G(t,ϕ)=(G1(t,ϕ),G2(t,ϕ),G3(t,ϕ)) as follows,

    G(t,ϕ)=(p(t)Λ(tα(t),ϕ1(α(t)))μ(t)ϕ1(0)d(t)ϕ3(0)p(t)Λ(tα(t),ϕ1(α(t))))β(t)ϕ2(0)ϕ3(0)μ(t)ϕ2(0)q(t)β(tτ(t))ϕ2(τ(t))ϕ3(τ(t))(μ(t)+d(t)+γ(t))ϕ3(0)).

    Note that G(t,ϕ) is continuous and Lipschitzian with respect to ϕ in each compact subset of C+L. It then follows from [33,Theorems 2.2.1 and 2.2.3] that system (2.5) admits a unique solution w(t,ϕ) on its maximal interval of existence with initial condition w0=ϕC+L. Moreover, Gi(t,ϕ)0 whenever ϕ=(ϕ1,ϕ2,ϕ3)0 and ϕi(0)=0, 1i3. It then follows from [34,Theorem 5.2.1] that the solution w(t,ϕ) of system (2.5) with w0=ϕC+L is nonnegative for all t0 in its maximal interval of existence.

    Define

    Y={φC([L,0],R4+):φ2(0)=0τe0sμ(r)drβ(s)φ1(s)φ3(s)ds}.

    By an argument similar to that in [13], we obtain that for any φY, system (2.4) admits a unique nonnegative solution u(t,φ)=(S(t),E(t),I(t),R(t)) satisfying initial condition u0=φ. Recall that N(t)=S(t)+E(t)+I(t)+R(t), and it satisfies

    dN(t)dtp(t)Λ(tα(t),N(tα(t)))μ(t)N(t). (2.6)

    Assumptions (A3) together with comparison principle indicate that C([L,0],[0,K]) is positively invariant for equation (2.6), and hence, C([L,0],[0,K]3) is positively invariant for system (2.5). Thus, the solutions of system (2.5) with initial data in C+L exist globally on [0,), and are also ultimately bounded.

    In this section, we establish threshold-type results on the global dynamics of system (2.5) or (2.4) in terms of basic reproduction ratios.

    We first establish the basic reproduction ratio ˆR0 for population equation (2.1) in the absence of disease. Let Cα:=C([α,0],R) and C+α:=C([α,0],R+). Note that equation (2.1) has a trivial zero solution. Linearizing equation (2.1) at the zero solution, we obtain the following linearized equation,

    dN(t)dt=r(t)N(tα(t))μ(t)N(t), (3.1)

    where r(t):=p(t)b(tα(t),0). By the general theory of linear functional differential equations in [33,Section 8.1], it follows that for any φCα, system (3.1) has a unique solution N(t,s,φ) (ts) with initial condition Ns=φ.

    Let F1(t)φ:=r(t)φ(α(t)), φCα, and V1(t):=μ(t). Then linear equation (3.1) can be written as

    dN(t)dt=F1(t)NtV1(t)N(t),

    where Nt(θ)=N(t+θ), θ[α,0]. Refer to [32], we define the next generation operator ˆL as

     [ˆLϕ](t)=0ettsV1(σ)dσF1(ts)ϕ(ts+)ds.

    Motivated by [20,13], the basic reproduction ratio ˆR0 is defined as ˆR0:=r(ˆL), the spectral radius of ˆL. Let N(t,ϕ) denote the solution of equation (2.1) with initial condition N0=ϕC+α, it then follows from [32,Theorem 3.5] that the following threshold-type result holds.

    Lemma 3.1. Let (A1)-(A3) hold, then the following statements are valid:

    (i) If ˆR0<1, then limtN(t,ϕ)=0, ϕC+α;

    (ii) If ˆR0>1, then there exists an ϵ>0 such that lim inftN(t,ϕ)ϵ holds for all ϕC+α with ϕ(0)>0.

    Lemma 3.1 shows that if ˆR0<1, then the population will die out whether the disease breaks out or not. In the following, we investigate the threshold dynamics for system (2.5) in the case where ˆR0>1. We first define the basic reproduction ratio R0 for system (2.5). In order to ensure the existence of the disease-free almost periodic solution, we further make the following assumption.

    (A4) Λ(t,N)N>0 for all N0 and t0.

    It then follows from [32,Theorem 3.7] that equation (2.1) admits a unique positive almost periodic solution N(t), and limt|N(t,φ)N(t)|=0 for all φC+α with φ(0)>0, and hence, system (2.5) admits a unique nontrivial positive disease-free almost periodic solution E(t)=(N(t),N(t),0). Assumption (A4) implies that the birth rate function is monotone. For a non-monotone case Λ(t,N)=a(t)eb(t)NN, the condition that (2.1) exists a globally stable almost periodic solution can be found in [32].

    Linearizing system (2.5) at E(t), we then have the equation for infectious class as follows,

    dI(t)dt=k(t)I(tτ(t))h(t)I(t), (3.2)

    where

    k(t)=q(t)β(tτ(t))N(tτ(t)),h(t)=(μ(t)+d(t)+γ(t)).

    Let Cτ:=C([τ,0],R) and C+τ:=C([τ,0],R+). By the general theory of linear functional differential equations in [33,Section 8.1], it follows that for any φCτ, system (3.2) admits a unique solution I(t,s,φ) (ts) with Is=φ. We define the evolution family U(t,s) on Cτ of equation (3.2) as

    U(t,s)φ=It(s,φ), φCτ, ts, sR,

    where It(s,φ)(θ)=I(t+θ,s,φ), θ[τ,0]. Let ω(U) be the exponential growth bound of the evolution family U(t,s), that is,

    ω(U)=inf{ωR:K01 such that U(t+s,s)K0eωt, sR, t0}.

    Lemma 3.2. [32,Theorem 3.2] The following statements are valid:

    (i) There exists an almost periodic function a(t) such that et0a(s)ds is a solution of (3.2).

    (ii) ω(U)=limt1tt0a(s)ds.

    (iii) Let u(t,ϕ) be the unique solution of (3.2) with initial condition u0=ϕ. Then for any ϕInt(C+τ), there holds ω(U)=limtlnu(t,ϕ)t.

    Let ˆU(t,s) denote the evolution family on CL:=C([L,0],R) of equation (3.2) and ω(ˆU) be its exponential growth bound. We have the following observation.

    Proposition 3.3. ω(ˆU)=ω(U).

    Proof. Note that Lτ, if L=τ, then the conclusion is natural, and we just need to prove the case that L>τ. By the definition of ω(ˆU), for any δ>0, there exists Kδ>1 such that

    ˆU(t+s,s)ϕCLKδe(ω(ˆU)+δ)tϕCL,t0, sR, ϕCL.

    For any φCτ, we can find a ϕCL such that φCτ=ϕCL and φ(θ)=ϕ(θ), θ[τ,0]. Thus, we have

    U(t+s,s)φCτˆU(t+s,s)ϕCLKδe(ω(ˆU)+δ)tϕCL=Kδe(ω(ˆU)+δ)tφCτ,t0, sR.

    By the definition of ω(U), we get ω(U)ω(ˆU)+δ.

    On the other hand, the definition of ω(U) shows that for any δ>0, there exists Lδ>1 such that

    I(t+s,s,φ)U(t+s,s)φCτLδe(ω(U)+δ)tφCτ,t0, sR, φCτ.

    For any ϕCL, there exists φCτ such that

    I(t+s,s,ϕ)=I(t+s,s,φ)Lδe(ω(U)+δ)tφCτLδe(ω(U)+δ)tϕCL,

    and hence,

    ˆU(t+s,s)ϕCL=It+s(s,ϕ)CLLδHe(ω(U)+δ)tϕCL,

    where H=max0θL{Lδe(ω(U)+δ)θ}. Thus, ˆU(t+s,s)LδHe(ω(U)+δ)t. By the definition of ω(ˆU), we get ω(ˆU)ω(U)+δ.

    In above proof, letting δ0+, we obtain that ω(U)ω(ˆU) and ω(ˆU)ω(U), and hence, ω(˜U)=ω(U).

    Let V2(t)=h(t) and F2 be a map defined as follows,

    F2(t)ϕ=k(t)ϕ(τ(t)), ϕCτ.

    It then follows that equation (3.2) can be written as

    dI(t)dt=F2(t)ItV2(t)I(t).

    Let ΨV2(t,s), ts, be the evolution family of linear equation

    dI(t)dt=V2(t)I(t).

    A simple computation shows that

    ΨV2(t,s)=etsh(r)dr,ts, sR.

    Let AP(R,R) be the order Banach space of all continuous almost periodic functions from R to R equipped with the supremum norm and the positive cone AP(R,R+). Following [20], let ϕAP(R,R+) denote the initial distribution of infectious individuals. For any given s0, F2(ts)ϕts, ts, represents the distribution of newly infectious individuals at time ts, which is produced by the infectious individuals at time tsτ(t) and still in the infectious compartment at time ts. Then ΨV2(t,ts)F2(ts)ϕts denotes the distribution of those infectious individuals who were newly reproduced at time tsτ(t) and remain in the infectious compartment at time t. Thus, 0ΨV2(t,ts)F2(ts)ϕtsds represents the distribution of accumulative new infectious individuals at time t produced by all those infectious individuals introduced at all previous time to t and still in the infectious compartment at time t. Define the next generation operator L as

     [Lϕ](t)=0ettsV2(σ)dσF2(ts)ϕ(ts+)ds,ϕAP(R,R), tR.

    Motivated by [13,20,29], the basic reproduction ratio R0 is defined as R0:=r(L), the spectral radius of L.

    Consider the following linear almost periodic equation with a parameter ρ>0:

    {dI(t)dt=1ρF2(t)ItV2(t)I(t),ts,Is=ϕC+τ. (3.3)

    Let U(t,s,ρ) be the evolution family of equation (3.3) and ω(U(ρ)) be its exponential growth bound. Then we have the following result, which comes from [20,Theorems 3.8 and 3.10].

    Lemma 3.4. R01 has the same sign as ω(U). Furthermore, if R0>0, then ρ=R0 is the unique solution of ω(U(ρ))=0.

    We now establish a threshold-type result on the global dynamics of system (2.5) in terms of R0. Let X0={ψ=(ψ1,ψ2,ψ3)C+L:ψ3(0)>0}. We first show the global extinction.

    Theorem 3.5. Let (A1)-(A4) hold. In the case where ˆR0>1, if R0<1, then the disease-free almost periodic solution (N(t),N(t),0) of system (2.5) is globally attractive in X0.

    Proof. Let (N(t,ϕ),S(t,ϕ),I(t,ϕ)) be the solution of system (2.5) with initial datum ϕX0. The global stability of N(t) for (2.1) indicates that for any δ>0, there exists t0>0 such that

    N(t,ϕ)<N(t)+δ,tt0.

    Let ˆUδ(t,s) denote the evolution family on C([L,0],R+) associated with the following perturbed linear almost periodic equation:

    dI(t)dt=q(t)β(tτ(t))(N(tτ(t))+δ)I(tτ(t))h(t)I(t). (3.4)

    Since R0<1, Lemma 3.4 together with Proposition 3.3 yield ω(U)=ω(ˆU). Thus, we can restrict δ small enough such that ω(ˆUδ)<0. Again by Lemma 3.2, there exists an almost periodic function aδAP(R,R) such that y(t)=et0aδ(μ)dμ is a solution of (3.4), and ω(ˆUδ)=limtt0aδ(μ)dμt<0. By the third equation of system (2.5), we have that I(t,ϕ) satisfies

    dI(t)dtq(t)β(tτ(t))(N(tτ(t))+δ)I(tτ(t))h(t)I(t)

    for all tt0. Choose a positive constant M such that I(t,ϕ)My(t), t[t0L,t0]. By the comparison theorem for delay differential equations ([34,Theorem 5.1.1]), we obtain

    I(t,ϕ)My(t)=Met0aδ(r)dr,tt0.

    Note that

    limtet0aδ(r)dr=limt(e1tt0aδ(r)dr)t=0.

    Hence, we deduce that I(t,ϕ)0 as t. Using the chain transitive arguments similar to those in the proof of [21,Theorem 4.2], we further obtain limt(N(t)N(t))=0 and limt(S(t)S(t))=0.

    Theorem 3.6. Let (A1)-(A4) hold. In the case where ˆR0>1, if R0>1, then there exists ϵ>0 such that the solution (N(t,ϕ),S(t,ϕ),I(t,ϕ)) of system (2.5) with initial datum ϕX0 satisfies

    lim inft(N(t,ϕ),S(t,ϕ),I(t,ϕ))(ϵ,ϵ,ϵ).

    Proof. We use the skew-product semiflows approach to prove the desired uniform persistence. Let

    Γ(t)=(p(t),Λ(t),α(t),μ(t),β(t),q(t),τ(t),d(t),γ(t))

    and H(Γ) be the closure of {Γs:sR} under the compact open topology, where Γs is defined by Γs(t)=Γ(t+s), tR. It then follows from [31,Theorem 1.6] that H(Γ) is compact. Define ζt(Θ)=Θt, for all ΘH(Γ) and tR. Then ζt:H(Γ)H(Γ) is a compact, almost periodic minimal and distal flow (see [35,Section VI.C]). Consider a family of almost periodic systems,

    dN(t)dt=ˉp(t)ˉΛ(tˉα(t),N(tˉα(t)))ˉμ(t)N(t)ˉd(t)I(t),dS(t)dt=ˉp(t)ˉΛ(tˉα(t),N(tˉα(t))))ˉβ(t)S(t)I(t)ˉμ(t)S(t),dI(t)dt=ˉq(t)ˉβ(tˉτ(t))S(tˉτ(t))I(tˉτ(t))(ˉμ(t)+ˉd(t)+ˉγ(t))I(t), (3.5)

    where (ˉp,ˉΛ,ˉα,ˉμ,ˉβ,ˉq,ˉτ,ˉd,ˉγ)=ΘH(Γ). Let

    X0:=XX0, Z:=X×H(Γ), Z0:=X0×H(Γ), Z0:=ZZ0.

    Then Z0 and Z0 are relatively open and closed in Z, respectively. For any given (ϕ,Θ)Z, let x(t,ϕ,Θ)=(N(t,Θ),S(t,Θ),I(t,Θ)) be the unique solution of system (3.5) with initial condition x0(ϕ,Θ)=ϕ. It is easy to see that for any given ΘH(Γ), both X and X0 are positively invariant for solutions of (3.5). Let xt(ϕ,Θ)(θ):=x(t+θ,ϕ,Θ), θ[L,0], we define a skew-product semiflow

    Π:R+×ZZ,(t,ϕ,Θ)(xt(ϕ,Θ),ζt(Θ)).

    We use the notation Πt(ϕ,Θ)=Π(t,ϕ,Θ). Obviously, ΠtZ0Z0, t0. Recall that the solutions of (2.5) are ultimately bounded in X, and hence, so does (3.5), which implies that Πt is point dissipative on Z. By [36,Theorem 3.4.8], it follows that Πt:ZZ admits a global compact attractor ˆV.

    Define

    M:={(ϕ,Θ)Z0:Πt(ϕ,Θ)Z0,t0}.

    We now show that

    M={(N,S,0,Θ)Z:N0,S0,ΘH(Γ)}. (3.6)

    For any (ˆϕ,ˆΘ){(N,S,0,Θ)Z:N0,S0,ΘH(Γ)}, the solution

    x(t,ˆϕ,ˆΘ)=(N(t,ˆΘ),S(t,ˆΘ),I(t,ˆΘ))

    satisfies I(t,ˆΘ)=0 for all t0 and ˆΘH(Γ), and hence, (ˆϕ,ˆΘ)M. This indicates that {(N,S,0,Θ)Z:N0,S0,ΘH(Γ)}M. For any given (˜ϕ,˜Θ)M, xt(˜ϕ,˜Θ)X0 holds for all t0. We further show that I(t,˜Θ)=0 for all t0. Assume, for the sake of contradiction, that there exists t10 such that I(t1,˜Θ)>0. It then follows from the second equation of (3.5) that I(t,˜Θ)>0 for all tt1, which contradicts the fact that (N(t,˜Θ),S(t,˜Θ),I(t,˜Θ))X0. Since I(t,˜Θ)=0 for all t0, we have

    (˜ϕ,˜Θ){(N,S,0,Θ)Z:N0,S0,ΘH(Γ)},

    and hence, M{(N,S,0,Θ)Z:N0,S0,ΘH(Γ)}. This proves (3.6).

    Note that for each ΘH(Γ), in the case where ˆR0>1, the following equation,

    dN(t)dt=ˉp(t)ˉΛ(tˉα(t),N(tˉα(t)))ˉμ(t)N(t), (3.7)

    admits a positive almost periodic solution N(t,Θ), which is uniformly asymptotically stable. Let M={(N0(Θ),N0(Θ),0,Θ):ΘH(Ω)}, where N0(Θ)(θ)=N(θ,Θ), θ[L,0]. By the uniqueness and continuity of solutions, it follows that Nt(Θ)=N0(ζt(Θ)). Hence, M is a compact and invariant set for Πt:ZZ.

    For any given (ϕ,Θ)M, let ω(ϕ,Θ) be the omega limit set of (ϕ,Θ) for Πt. Let (ˇϕ,ˇΘ)ω(ϕ,Θ) be given. Then there exists a sequence tn such that limnΠtn(ϕ,Θ)=(ˇϕ,ˇΘ). Note that Πtn(ϕ,Θ)=(xtn(ϕ,Θ),ζtn(Θ)) and limnxtn(ϕ,Θ)(Ntn(Θ),Ntn(Θ),0)=0. Since N0(Θ) is continuous in Θ, and H(Γ) is compact, it follows that N0(Θ) is uniformly continuous in ΘH(Γ). This indicates that Ntn(Θ)=N0(ζtn(Θ))N0(ˇΘ) as n. Hence, (ˇϕ,ˇΘ)=(N0(ˇΘ),N0(ˇΘ),0,ˇΘ)M. Thus, ω(ϕ,Θ)M. It then follows that M is a compact and isolated invariant set for Πt in Z0, (ϕ,Σ)Mω(ϕ,Θ)M, and no subset of M forms a cycle for Πt in Z0.

    Since R0>1, Lemma 3.4 shows that ω(U)>0. We let ω(ˉU) be the exponential growth bound of the evolution family of system (3.2) with (k,τ,h) replaced by (ˉk,ˉτ,ˉh) on C([L,0],R), where

    ˉk(t)=ˉq(t)ˉβ(tˉτ(t))N(tˉτ(t),Θ),ˉh(t)=(ˉμ(t)+ˉd(t)+ˉγ(t)).

    It then follows from Proposition 3.3 that ω(ˉU)=ω(ˆU)=ω(U)>0. We use ω(ˆUϑ) to denote the exponential growth bound associated with the following linear equation on C([L,0],R),

    dI(t)dt=ˉkρ(t)I(tˉτ(t))ˉh(t)I(t),

    where

    ˉkρ(t)=ˉq(t)ˉβ(tˉτ(t))(N(tˉτ(t),Θ)ρ) and ρ<inftRN(t).

    Hence, we can choose a sufficiently small constant ρ>0 such that ω(ˆUρ)>0.

    Next, we claim that

    lim suptd(Πt(ϕ,Θ),M)ρ,(ϕ,Θ)Z0.

    Assume, by contradiction, that for some (ˉϕ,ˉΘ)Z0, ˉΘ=(ˉp1,ˉΛ1,ˉα1,ˉμ1,ˉβ1,ˉq1,ˉτ1,ˉd1,ˉγ1), there holds

    lim suptd(Πt(ˉϕ,ˉΘ),M)<ρ.

    It then follows that there exists t2>0 such that S(t,ˉϕ,ˉΘ)N(t,ˉΘ)ρ, tt2. Thus, I(t,ˉϕ,ˉΘ) satisfies

    dI(t)dtˉk1ρ(t)I(tˉτ1(t))ˉh1(t)I(t),tt2, (3.8)

    where

    ˉk1(t)=ˉq1(t)ˉβ1(tˉτ1(t))(N(tˉτ1(t),ˉΘ)ρ),ˉh1(t)=(ˉμ1(t)+ˉd1(t)+ˉγ1(t)).

    By virtue of Lemma 3.2, there exists an almost periodic function a(t,ˉΘ) such that ˆI(t,ˉΘ)=et0a(r,ˉΘ)dr is a solution of

    dI(t)dt=ˉk1ρ(t)I(tˉτ1(t))ˉh1(t)I(t), (3.9)

    and

    ω(˜Uρ)=limt1tt0a(r,ˉΘ)dr>0,

    where ˜Uρ(t,s) (ts) denotes the evolution family of equation (3.9) on C([L,0],R). Since (ˉϕ,ˉΘ)Z0 indicates that there exists a t3>t2+L such that I(t3+θ,ˉϕ,ˉΘ)Int(R+), θ[L,0], we can take ξ>0 small enough such that I(t3+θ,ˉϕ,ˉΘ)ξˆI(t3+θ,ˉΘ), θ[L,0]. By the comparison principle, as applied to system (3.8), it then follows that

    I(t,ˉϕ,ˉΘ)ξˆI(t,ˉΘ)=ξet0a(r,ˉΘ)dr,tt3.

    Moreover, since

    limtet0a(r,ˉΘ)dr=limt(e1tt0a(r,ˉΘ)dr)t=,

    we get limtI(t,ˉϕ,ˉΘ)=, a contradiction.

    Recall that M is an isolated invariant set for Πt in Z0, the claim above shows that M is also an isolated invariant set for Πt in Z. The above claim also indicates that Ws(M)Z0=, where the set

    Ws(M):={(ϕ,Θ)Z:ω(ϕ,Θ),ω(ϕ,Θ)M}

    represents the stable set of M for Πt. By the continuous-time version of [37,Theorem 1.3.1 and Remark 1.3.1], the skew-product semiflow Πt:ZZ is uniformly persistent with respect to Z0. That is, there exists ε>0 such that for any (ϕ,Θ)Z0, lim inftd(Πt(ϕ,Θ),Z0)ε. Since Πt is compact for any t>L, it follows that Πt is asymptotically smooth. By [38,Theorem 3.7 and Remark 3.10], we get that Πt:Z0Z0 admits a global attractor ˆV0.

    It remains to prove the practical uniform persistence. Since ˆV0Z0 and ΠtˆV0=ˆV0, it follows that ϕ2(0)>0 for all (ϕ1,ϕ2,ϕ3,Θ)ˆV0. From the invariance of ˆV0, we get that ϕi(0)>0 for i=1,3. Obviously, limtd(Πt(ϕ,Θ),ˆV0)=0 for all (ϕ,Θ)Z0. Define a continuous function g:Z[0,) by

    g(ϕ,Θ)=mini=1,2,3{ϕi(0)},(ϕ,Θ)=(ϕ1,ϕ2,ϕ3,Θ)Z.

    It is easy to see that g(ϕ,Θ)>0 for all (ϕ,Θ)V0. The compactness of V0 implies that inf(ϕ,Θ)V0g(ϕ,Θ)=min(ϕ,Θ)V0g(ϕ,Θ)>0. Consequently, we conclude that there exists an ϵ>0 such that lim inft(N(t,Θ),S(t,Θ),I(t,Θ))(ϵ,ϵ,ϵ) for any (ϕ,Θ)Z0.

    In this section, we carry out some numerical simulations to illustrate the theoretical results obtained in previous sections, and numerically analyze the influence of the almost periodic time delays on the disease transmission. The numerical computation of ˆR0 and R0 is based on Lemmas 3.2 and 3.4, see the numerical simulations in [32] for detail.

    For the sake of simplicity, we first suppose that all the parameters except τ(t) are independent of time t. In our numerical simulations, we take b(t,N)=cz+N Day1, where c,z>0, which is a classical birth rate function. Other examples of birth rate functions Λ(t,N) in the biological literature can be found in, e.g., [5]. In this case, the population change in the absence of disease is governed by

    dN(t)dt=cz+N(tα)ed1αN(tα)μ(t)N(t). (4.1)

    Corresponding to equation (3.1), the linearized equation of (2.1) at trivial solution is

    dN(t)dt=czed1αN(tα)μN(t).

    For ϕAP(R,R+), the next generation operator ˆL for population model in the absence of disease is defined as

     [ˆLϕ](t)=0czeμsed1(s+α)ϕ(tsα)ds.

    We take z=20, α=365 Day, d1=1365×20 Day1 and μ=1365×5 Day1. Thanks to Lemma 3.4, it easily follows that ˆR0 is in scale with c, that is, ˆR10c1=ˆR20c2, where ci>0 (i=1,2), ˆR10 and ˆR20 are the basic reproduction ratios corresponding to c=c1 and c=c2, respectively. The graph of ˆR0 versus c is presented in Figure 2. We choose c=0.02, in this case we have ˆR0=1.736>1, and Figure 3 shows that the population is uniformly persistent in the absence of disease. A direct computation, furthermore, shows that the equilibrium solution of equation (4.1) is ced1αμz, and hence, the disease-free equilibrium is (ced1αμz,ced1αμz,0). Thus, the linearized equation of infectious class equation at disease-free equilibrium reads

    dI(t)dt=(1τ(t))eμτ(t)β(ced1αμz)I(tτ(t))(μ+d+γ)I(t). (4.2)
    Figure 2.  The graph of ˆR0 versus r.
    Figure 3.  The population change when ˆR0>1.

    Let β=0.002 Day1, d=0.001 Day1, γ=0.01 Day1 and τ(t)=30+12cos(2πt365)+sin(2t365) Day. By numerical computation, we obtain R0=2.509>1. The numerical simulations further show that the disease is uniformly persistent, see Figure 4. Similar to the relationship between ˆR0 and c, it is easy to see that R0 is in scale with β. We choose β=0.0007 Day1, it follows that R0=0.878<1, and the numerical simulations indicate that the disease will vanish, see Figure 5. Clearly, the numerical simulations are consistent with the threshold results obtained in section 3.

    Figure 4.  Long-term behavior of the solution of system (2.5) when R0=2.509>1.
    Figure 5.  Long-term behavior of the solution of system (2.5) when R0=0.878<1.

    By Lemma 3.4, we can easily obtain the effect of parameters μ, β, c, d1, α, z, d and γ on R0, see Table 1. If τ is a positive constant, moreover, then the numerical simulations in [20] shows that R0 is decreasing with respect to τ. We now are interested in the sensitivity of R0 on the fluctuation of latent period. Let τ(t)=30+a(12cos(2πt365)+sin(2t365)) Day, a[0,4]. It is clear that if a=0, then latent period is a constant delay, and the amplitude of latent period is increasing with respect to a. Let other parameters remain unchanged, the relationship between R0 and a is presented in Figure 6. It indicates that increasing the amplitude of latent period has a positive effect on disease transmission, but the effect is negligible. Note that μ=1365×5 Day 1, it causes that the amplitude of eμτ(t) is very small. Choose μ=0.02 Day 1, c=2, and other parameters remain unchanged. By numerical simulations, in this case, we obtain that the effect of a on R0 should not be ignored, see Figure 7.

    Table 1.  The effect of parameters on R0 ("+": R0 is increasing with respect to the parameter; "-": R0 is decreasing with respect to the parameter).
    Parameter the effect on R0
    μ -
    β +
    c +
    d1 -
    α -
    z -
    d -
    γ -

     | Show Table
    DownLoad: CSV
    Figure 6.  The graph of R0versus a(case 1).
    Figure 7.  The graph of R0versus a(case 2).

    In order to consider the effect of the fluctuation of maturation delay α(t) on R0, we let d1=0.02 Day1, c=0.05, τ(t)=τ=30 Day, α(t)=30+b(12cos(2πt365)+sin(2t365)) Day, b[0,4], and other parameters remain unchanged. Figure 8 shows the relationship between R0 and b. It indicates that the amplitude of maturation period has a little effect on the disease transmission, and R0 is increasing with respect to the amplitude of maturation period.

    Figure 8.  The graph of R0 versus b.

    In our numerical simulations, we numerically analyze the effects of the fluctuations of maturation and incubation periods on disease transmission. It is shown that both the amplitudes of maturation and latent periods have a little effect on the disease transmission, and R0 is increasing with respect to them. But if the death rates μ and d1 are very small, then the effect can be negligible. Furthermore, by numerical computations, we obtain that the mean values of eμτ(t) and ed1α(t) are increasing with respect to a and b, respectively, which may cause that R0 is increasing with respect to a and b.

    It is well-known in epidemiology that seasonal changes have profound effects on disease transmission. With the combination of environmental factors, stage structure and incubation period of disease, we formulate and study an almost periodic epidemic model with time-dependent delays. The almost periodicity reflects the influence of certain seasonal variations which are approximatively but not exactly periodic, and allows one to consider general seasonal fluctuations. For this mathematical model, we first introduce the basic reproduction ratio ˆR0 for population, it can be regarded as an index of reproductive ability of population. By the theory developed in [32], we present the threshold dynamics for population in the absence of disease. Furthermore, in the case where ˆR0>1, we introduce the basic reproduction ratio R0 for disease, it provides an index of transmission intensity. With the recent theory developed in [20], we can characterize the basic reproduction ratio R0 by the exponential growth bound associated with a linear functional differential equation (3.2). By the skew-product semiflow, comparison arguments and persistence theory, we show that the sign of R01 completely determines the extinction and persistence of the disease. More precisely, the disease will be eliminated if R0<1, while the disease persists in the population if R0>1.

    In the final section, we carry out some numerical simulations to illustrate the theoretical results obtained in previous sections, and numerically analyze the influence of the parameters of model on the disease transmission. Note that, in particular, the maturation delay and incubation period are time-dependent, we are interested in the dependence between R0 and the amplitudes of maturation and latent periods. Numerical simulations indicate that both the amplitudes of maturation and latent periods have a little effect on the disease transmission, and R0 is increasing with respect to them. But if the death rates μ and d1 are very small, then the effect can be neglected.

    The authors are very grateful to the editors and the referees for careful reading and valuable comments which led to important improvements of the original manuscript. This work is supported by NNSF of China (12071193).

    All authors declare no conflicts of interest in this paper.



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