Research article

Contrasting Photo-physiological Responses of the Haptophyte Phaeocystis Antarctica and the Diatom Pseudonitzschia sp. in the Ross Sea (Antarctica)

  • Received: 07 February 2017 Accepted: 09 May 2017 Published: 16 May 2017
  • The Antarctic is a unique environment in which substantial variations in irradiance occur over a number of time scales, and as a result phytoplankton need to acclimate and adapt to these changes. We conducted field and laboratory manipulations in the Ross Sea, Antarctica to examine photophysiological differences between Phaeocystis antarctica and Pseudonitzschia sp. a diatom that commonly occurrs in the Ross Sea, since these are the two functional groups that dominate abundance and productivity. Both exhibited reduced quantum yields due to high irradiances. P. antarctica, a haptophyte, displays a distinct photophysiological response to irradiance when compared to diatoms. P. antarctica showed a rapid recovery from high light exposure, as indicated by the rapid return to initial, high quantum yields, in contrast to diatoms, which responded more slowly. Absorption cross sections were high in both forms, but those in P. antarctica were significantly higher. Both organisms recovered within 24 h to initial quantum yields, suggesting that high irradiance exposure does not have a permanent effect on these organisms. Among all micronutrient additions (iron, cobalt, zinc and vitamin B12), only iron additions resulted in rapid impacts on quantum yields. Iron limitation also can result in reduced photosynthetic efficiency. Understanding these photophysiologial responses and the impact of oceanographic conditions provides constraints on modeling efforts of photosynthesis and primary productivity in the Antarctic.

    Citation: Sasha Tozzi, Walker O. Smith. Contrasting Photo-physiological Responses of the Haptophyte Phaeocystis Antarctica and the Diatom Pseudonitzschia sp. in the Ross Sea (Antarctica)[J]. AIMS Geosciences, 2017, 3(2): 142-162. doi: 10.3934/geosci.2017.2.142

    Related Papers:

    [1] Julijana Gjorgjieva, Kelly Smith, Gerardo Chowell, Fabio Sánchez, Jessica Snyder, Carlos Castillo-Chavez . The Role of Vaccination in the Control of SARS. Mathematical Biosciences and Engineering, 2005, 2(4): 753-769. doi: 10.3934/mbe.2005.2.753
    [2] Tingting Xue, Long Zhang, Xiaolin Fan . Dynamic modeling and analysis of Hepatitis B epidemic with general incidence. Mathematical Biosciences and Engineering, 2023, 20(6): 10883-10908. doi: 10.3934/mbe.2023483
    [3] Abdelheq Mezouaghi, Salih Djillali, Anwar Zeb, Kottakkaran Sooppy Nisar . Global proprieties of a delayed epidemic model with partial susceptible protection. Mathematical Biosciences and Engineering, 2022, 19(1): 209-224. doi: 10.3934/mbe.2022011
    [4] Jing-An Cui, Fangyuan Chen . Effects of isolation and slaughter strategies in different species on emerging zoonoses. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1119-1140. doi: 10.3934/mbe.2017058
    [5] Zi Sang, Zhipeng Qiu, Xiefei Yan, Yun Zou . Assessing the effect of non-pharmaceutical interventions on containing an emerging disease. Mathematical Biosciences and Engineering, 2012, 9(1): 147-164. doi: 10.3934/mbe.2012.9.147
    [6] Jiangbo Hao, Lirong Huang, Maoxing Liu, Yangjun Ma . Analysis of the COVID-19 model with self-protection and isolation measures affected by the environment. Mathematical Biosciences and Engineering, 2024, 21(4): 4835-4852. doi: 10.3934/mbe.2024213
    [7] Yicang Zhou, Zhien Ma . Global stability of a class of discrete age-structured SIS models with immigration. Mathematical Biosciences and Engineering, 2009, 6(2): 409-425. doi: 10.3934/mbe.2009.6.409
    [8] Maryam Al-Yahyai, Fatma Al-Musalhi, Ibrahim Elmojtaba, Nasser Al-Salti . Mathematical analysis of a COVID-19 model with different types of quarantine and isolation. Mathematical Biosciences and Engineering, 2023, 20(1): 1344-1375. doi: 10.3934/mbe.2023061
    [9] Kazuo Yamazaki, Xueying Wang . Global stability and uniform persistence of the reaction-convection-diffusion cholera epidemic model. Mathematical Biosciences and Engineering, 2017, 14(2): 559-579. doi: 10.3934/mbe.2017033
    [10] Fernando Saldaña, Hugo Flores-Arguedas, José Ariel Camacho-Gutiérrez, Ignacio Barradas . Modeling the transmission dynamics and the impact of the control interventions for the COVID-19 epidemic outbreak. Mathematical Biosciences and Engineering, 2020, 17(4): 4165-4183. doi: 10.3934/mbe.2020231
  • The Antarctic is a unique environment in which substantial variations in irradiance occur over a number of time scales, and as a result phytoplankton need to acclimate and adapt to these changes. We conducted field and laboratory manipulations in the Ross Sea, Antarctica to examine photophysiological differences between Phaeocystis antarctica and Pseudonitzschia sp. a diatom that commonly occurrs in the Ross Sea, since these are the two functional groups that dominate abundance and productivity. Both exhibited reduced quantum yields due to high irradiances. P. antarctica, a haptophyte, displays a distinct photophysiological response to irradiance when compared to diatoms. P. antarctica showed a rapid recovery from high light exposure, as indicated by the rapid return to initial, high quantum yields, in contrast to diatoms, which responded more slowly. Absorption cross sections were high in both forms, but those in P. antarctica were significantly higher. Both organisms recovered within 24 h to initial quantum yields, suggesting that high irradiance exposure does not have a permanent effect on these organisms. Among all micronutrient additions (iron, cobalt, zinc and vitamin B12), only iron additions resulted in rapid impacts on quantum yields. Iron limitation also can result in reduced photosynthetic efficiency. Understanding these photophysiologial responses and the impact of oceanographic conditions provides constraints on modeling efforts of photosynthesis and primary productivity in the Antarctic.


    Even if the vaccine is successfully developed and widely vaccinated all over the world, COVID-19 [1,2,3,4,5,6,7,8] still rages around the world. During a pandemic, governments take various measures to slow down the spread of the disease, such as keeping social distance, banning large-scale parties, and even imposing curfews. Strictly controlling the flow of people can combat the epidemic, but it can hurt economic development at the same time. So it is very meaningful to find a suitable isolation ratio which can not only ensure the orderly progress of work and life, but also control the wide spread of the virus. In this paper, we construct a model to analyze the relationship between infected population and social isolation by introducing the isolation ratio.

    With the efforts of previous scholars, many typical models have been gradually accumulated, which also reflects that infectious disease model is an important content of research[9,10,11,12,13,14]. System (1.1) first proposed by Kermack and McKendrick pioneered the study of infectious diseases[15].

    {˙S=bNβSIbS,˙I=βSIγI(a+b)I,˙R=γIbR. (1.1)

    S, R, and I respectively represent susceptible population, recovered population and infected population. The total population N satisfies the equation N=S+I+R without considering the migration population. β is the infected rate, and γ is the recovery rate. The elimination rate of the infected people is a+b. SIR model has an assumption that infected patients are immune after rehabilitation. When rehabilitated people are no longer immune to the virus, it means that they are at risk of secondary infection. Then, it becomes SIRS[16,17] model

    {˙S=bNβSIbS+cR,˙I=βSIγI(a+b)I,˙R=γIbR, (1.2)

    where c represents infection rate of convalescent patients. Zhao et al. [17] studied an SIRS model with pulse vaccination and birth pulse. The stability of the infection-free periodic solution and the existence of nontrivial periodic solution which was bifurcated from the infection-free periodic solution were discussed by the Poincaré map and the bifurcation theory. It was obtained when the threshold was reached, a nontrivial periodic solution would appear through supercritical bifurcation.

    Since some infectious diseases such as COVID-19 have latent period, asymptomatic infections must be considered. Then SEIR model [18,19,20,21,22,23,24] is proposed

    {˙S=bNβSIbS,˙E=βSIγEbE,˙I=γE(a+b)I,˙R=γIbR. (1.3)

    Saikia et al. [18] used SEIR model to predict the epidemic trend in India, and found the existence of peak day, meaning a sudden shift in the mode of disease transmission. In [19], based on the propagation characteristics of COVID-19, SEIR model was improved to SEIQR model and its basic reproductive number was derived. The results showed that the improved model had better predictive power and successfully captured the development process of the COVID-19. When we need to consider that the rehabilitated persons have secondary infection, the system (1.3) becomes SEIRS model [25,26,27] as follows

    {˙S=bNβSIbS+cR,˙E=βSIγEbE,˙I=γE(a+b)I,˙R=γIbR. (1.4)

    In [25], Britton et al. considered the impact of an infectious disease with escape ability on population growth. Four possible results were obtained in this paper: (1) disease died out quickly, only infecting few; (2) epidemic took off, but the proportion of infected people was still negligible; (3) infectious disease spread rapidly, and the proportion of infected people had reached a local balance; (4) disease spread widely and rapidly, transforming exponential population growth into exponential decay. Lu et al. [26] proposed a new criterion for determining the global asymptotic stability of nonlinear systems, which was based on the geometric method proposed by Li and Muldowney. Otunuga et al. [27] estimated and analyzed the time-dependent parameters: symptomatic recovery rate, transmission rate, immunity rate and the effective reproduction number for COVID-19 in the United States during the 01/22/2020–02/25/2021 period based on the SEIRS model, where the infected population was classified as the symptomatic infected population and asymptomatic infectious population. It should be noted that the infectious disease model we discussed, whether SEIR model or SEIRS model, does not have the ability of vertical transmission. In other words, the virus cannot be transmitted to the unborn foetus.

    In [28], Abdelaziz et al. investigated a discrete-time SEIR epidemic model with constant vaccination and fractional-order, and got its basic reproduction number. They obtained the local and global stability conditions at equilibriums and discussed two types of codimension one bifurcation which were called Neimark-Sacker bifurcation and flip bifurcation. The criterion used was based on the characteristic coefficient equation instead of the properties of the eigenvalues of the Jacobian matrix. Thirthar et al. [29] established an SI1I2R model with general recovery functions and saturated incidence of the disease I1. The local stability and global stability of disease-free equilibrium and endemic equilibrium were given by the basic reproductive number and Lyapunov function. The system studied had neither Saddle-node bifurcation and Transcritical bifurcation near the disease-free equilibrium point under c2β2S0<μ+ϵ1+ϵ2 and E0=1. Liu et al. [30] studied the existence and uniqueness of the positive solution in the transmission of two diseases between two groups, which could be called S1I1R1S2I2R2 model.

    Most works on infectious disease models mainly study the prediction of future trends based on statistical data from different regions. This paper introduces the isolation ratio and establishes the SIaIsQR model where S,Ia,Is,Q and R respectively represent susceptible, asymptomatic, symptomatic, quarantined and recovery classes. We give the basic reproductive number of the model and its biological significance. The stability conditions of the disease-free and endemic equilibria are obtained by analyzing its distribution of characteristic values. The results show that isolation ratio has an important impact on the basic reproductive number and the stability conditions. As p increases, R0 decreases, and this effect is amplified by square. The simulation results verify the influence of isolation ratio on the system. The rest of this paper is as follows. In Section 2, we establish an SIaIsQR model. In Section 3, we obtain the basic reproductive number and give its biological explanation. In Section 4, the stability conditions of the disease-free and endemic equilibria are discussed. In Section 5, we investigate the influence of several important parameters on epidemic spread from numerical simulations. Finally, in Section 6, we summarize and discuss this paper.

    In recent years, in view of the frequent attacks of infectious diseases on humans, experts and scholars have established various models according to different propagation characteristics [31,32,33,34]. In this paper, we divide the crowd into five storerooms, which are susceptible class (S(t)), asymptomatic infection class (Ia(t)), symptomatic infection class (Is(t)), quarantine class (Q(t)), and recovery class (R(t)), assuming the general population is N(t)=S(t)+Ia(t)+Is(t)+Q(t)+R(t).

    Susceptible class (S(t)): It is assumed that the input of the population is a constant (Λ) and the natural mortality of the population is μ. Both symptomatic patients and asymptomatic patients have the ability to infect, and the transmission ability of symptomatic patients is stronger than that of asymptomatic patients (αa<αs). The proportion of people isolated by the government is p. The contact between susceptible people and infected people is αaS(1p)Ia(1p)+αsS(1p)Is(1p). There is no vertical transmission of the disease. The change rate of susceptible groups is

    ˙S=Λαa(1p)2SIaαs(1p)2SIsμS.

    Remark 1 In the process of disease transmission, the infection process has a linear and proportional relationship with (1p)2, because the isolated objects include infected class and susceptible class. From the perspective of spatial density, if two groups are reduced by the same proportion of p, then the probability of meeting becomes (1p)2 of the original ones. In the predator-prey model, if the proportion of sheltered prey is q, the probability of predator and prey meeting is 1q of the original ones. It is a linear and proportional relation between the predatory process and 1q, because the shelter only acts on the prey[35].

    Asymptomatic class (Ia(t)): It is assumed that all infected persons will experience a incubation period, and the infected persons in the incubation period will be transformed into symptomatic patients in a fixed proportion of β. The change rate of asymptomatic groups is

    ˙Ia=αa(1p)2SIa+αs(1p)2SIsβIaμIa.

    Symptomatic class (Is(t)): Symptomatic infected persons will be detected and admitted to hospitals for isolation in proportion to γ. The recovery and mortality rate of infected patients without treatment are δ1 and μ1+μ. Then, the change rate of symptomatic class is

    ˙Is=βIaγIsδ1Isμ1IsμIs.

    Quarantined class (Q(t)): Because of medical treatment, the cure rate of isolated patients will be higher and the mortality rate will be lower than the symptomatic class δ2>δ1, μ2<μ1. Differential equation on quarantined class (Q(t)) is

    ˙Q=γIsδ2Qμ2QμQ.

    Recovery class (R(t)): The recovery class comes from symptomatic class in the proportion δ1 and quarantined class in proportion δ2. Then we get

    ˙R=δ1Is+δ2QμR.

    Integrating the above five dimensions, we obtain

    {˙S=Λαa(1p)2SIaαs(1p)2SIsμS,˙Ia=αa(1p)2SIa+αs(1p)2SIsβIaμIa,˙Is=βIaγIsδ1Isμ1IsμIs,˙Q=γIsδ2Qμ2QμQ,˙R=δ1Is+δ2QμR. (2.1)

    The initial conditions of system (2.1) are S(t0)=S00,Ia(t0)=I0a0,Is(t0)=I0s0 Q(t0)=Q00,R(t0)=R00. In order to ensure the biological significance of system (2.1), all solutions must be limited to the positive five dimensional Euclidean space region.

    Lemma 2.1 All solutions of system (2.1) with nonnegative initial values keep positive in R5+ for all t>0. All solutions of system (2.1) with nonnegative initial values are uniformly bounded in Ω={(S,Ia,Is,Q,R)R5+:0S,Ia,Is,Q,RΛμ}.

    The basic reproductive number is an important reference index for the study of infectious disease model, which represents the number of people infected by each patient during the disease period. The most commonly used method to calculate the basic reproductive number (R0) is the next-generation matrix [36]. The following is the general process.

    Let

    dxidt=fi(x)=ri(x)hi(x),i=1,2,,m, (3.1)

    where ri(x) is the rate of newly infected individuals in group i, hi(x) is the transfer rate. Denote F=[rixj(x0)], V=[hixj(x0)], where x0={x|xi=0,i=1,2,,m},1i,jm. FV1 is the reproducing matrix. ρ(FV1) is the spectral radius of the reproducing matrix. R0 is equal to ρ(FV1) representing the largest modulus of the eigenvalues of the Jacobian matrixes.

    Rewrite system (2.1) to X=[Ia,Is,Q,S,R]T. The disease-free equilibrium is x0=(Ia(0),Is(0),Q(0),S(0),R(0))=(0,0,0,Λμ,0). According to Eq (3.1),

    ri(x)=[αs(1p)2SIs+αa(1p)2SIa0000],
    hi(x)=[βIa+μIaβIa+γIs+δ1Is+μ1Is+μIsγIs+δ2Q+μ2Q+μQΛ+αs(1p)2SIs+αa(1p)2SIa+μSδ1Isδ2Q+μR],

    i=1,2,3,4,5. Then, we calculate the Jacobian matrix of r(xi) and h(xi) on disease-free equilibrium

    F(x0)=r(xi)xj(x0)=[αa(1p)2Λμαs(1p)2Λμ00000000000000000000000],
    V(x0)=h(xi)xj(x0)=[M10000βM20000γM300αa(1p)2Λμαs(1p)2Λμ0μ00δ1δ20μ],

    where M1=β+μ, M2=γ+δ1+μ1+μ, M3=δ2+μ2+μ, 1i,j5. Denote

    F1=[αa(1p)2Λμαs(1p)2Λμ00],
    V1=[β+μ0βγ+δ1+μ1+μ].

    We obtain

    FV1=F1V11=[A1A200],

    where

    A1=αa(1p)2Λμ1β+μ+αs(1p)2Λμβ(β+μ)(γ+δ1+μ1+μ),
    A2=αs(1p)2Λμ1γ+δ1+μ1+μ.

    Hence, the basic reproductive number is

    R0=ρ(FV1)=Ra0+Rs0,

    where

    Ra0=αa(1p)2Λμ1β+μ,
    Rs0=αs(1p)2Λμββ+μ1γ+δ1+μ1+μ.

    Remark 2 1β+μ and 1γ+δ1+μ1+μ represent the average removal time of the asymptomatic and symptomatic patients respectively. ββ+μ is the ratio of asymptomatic patients to symptomatic patients. Ra0 and Rs0 can be regarded as the number of people infected by each asymptomatic patient and symptomatic patient during the infectious period. As p increases, R0 decreases, and this effect is amplified by square. This shows that isolation is a very good measure to control the spread of disease. We compare the R0 of five different models[31,37,38,39,40]. The same parameters of the different basic reproductive numbers have the same effect in their respective models, such as conversion rate and infection rate.

    In this section, we show the local and global stability of the disease-free and endemic equilibria respectively.

    In this part, we will discuss the local stability of system (2.1) at the equilibrium point x0=(Λμ,0,0,0,0), and we also use Lyapunov function to judge its global stability.

    Theorem 1 When R0<1, the equilibrium point x0 of system (2.1) is locally stable; When R0>1, it is unstable [36].

    Theorem 2 The disease-free equilibrium point is global stable with R0<1.

    Proof. We construct the Lyapunov function

    V(t)=(γ+δ1+μ1+μ)Ia(t)+αs(1p)2ΛμIs(t).

    Obviously, V(t)0.

    By direct calculation

    dV(t)dt=B5[αs(1p)2SIs+αa(1p)2SIaβIaμIa]+αs(1p)2Λμ(βIaγIsδ1Isμ1IsμIs)=[B5αa(1p)2S(β+μ)B5+αs(1p)2Λμβ]Ia+[B5αs(1p)2Sαs(1p)2Λμ(γ+δ1+μ1+μ)]Is.

    As SΛμ,

    dV(t)dt[B5αa(1p)2Λμ(β+μ)B5+αs(1p)2Λμβ]Ia+[B5αs(1p)2Λμαs(1p)2ΛμB5]Is=(R01)B5(β+μ)Ia.

    When R0<1, dVtdt0. According to the second Lyapunov method, the disease-free equilibrium point is globally gradually steady.

    Before discussing the stability of the endemic disease, we consider its existence.

    Theorem 3 The positive equilibrium x=(S,Ia,Is,Q,R) of system (2.1) exists if R0>1 is satisfied. When D1D2>D3>0 is also satisfied, where

    D1=2μ+β+m+αs(1p)2Is+αa(1p)2Iaαa(1p)2S,

    D2=[β+μαa(1p)2S+αs(1p)2Is+αa(1p)2Ia](μ+c)+μcβαs(1p)2S,

    D3=[β+μαa(1p)2S+αs(1p)2Is+αa(1p)2Ia]μcμβαs(1p)2S,

    the positive equilibrium is locally stable.

    Proof. By solving the zero solution of system (2.1), we get x=(S,Ia,Is,Q,R), where

    S=(β+μ)B5αs(1p)2β+αa(1p)2B5,Ia=Λβ+μμB5αs(1p)2β+αa(1p)2B5,Is=βB5Ia,Q=γδ2+μ2+μIs,R=δ1μIs+δ2μQ.

    We just need to judge the negative and positive of Ia. By direct calculation,

    Ia=C1C2(β+μ)(αs(1p)2β+αa(1p)2B5),

    where, C1=Λ(αsβ+αaB5,C2=(β+μ)μB5. Note that, C1C2>0, i.e., C1C21>0. Then,

    C1C2=Λ(αs(1p)2β+αa(1p)2B5(β+μ)μB5=Λαs(1p)2β(β+μ)μB5+Λαa(1p)2(β+μ)μ=R0.

    So when R0>1, Ia>0. The positive equilibrium of system (2.1) exists. The Jacobian matrix of system (2.1) at the positive equilibrium point is

    J(x)=[E1E2E300E4E5E6000βB50000γB6000δ1δ2μ],

    where E1=αs(1p)2Isαa(1p)2Iaμ,E2=αa(1p)2S, E3=αs(1p)2S,E4=αs(1p)2Is+αa(1p)2Ia, E5=αa(1p)2Sβμ,E6=αs(1p)2S.

    Then, we get

    |λEJ(x)|=(λ+μ)(λ+δ2+μ2+μ)(λ3+D1λ2+D2λ+D3),

    where D1=2μ+β+m+αs(1p)2Is+αa(1p)2Iaαa(1p)2S, D2=[β+μαa(1p)2S+αs(1p)2Is+αa(1p)2Ia](μ+c)+μcβαs(1p)2S, D3=[β+μαa(1p)2S+αs(1p)2Is+αa(1p)2Ia]μcμβαs(1p)2S.

    According to the Routh-Hurwitz theorem,

    Δ1=D1>0,Δ2=|D11D3D2|>0,Δ3=|D110D3D2D100D3|>0.

    We obtain D1>0,D1D2>D3>0.

    This section is divided into four parts. Subsection 5.1 mainly discusses the influence of the incubation period from asymptomatic to symptomatic patients. Subsection 5.2 studies the impact of the infectious period on disease dissemination. In Subsection 5.3, we focus on the relationship between isolation ratio and epidemic spread. In this subsection, we obtain the isolation ratio to control the spread of the epidemic. In Subsection 5.4, the simulations show the disease-free and endemic equilibria of system 2.1 are stable with certain conditions.

    This section mainly discusses the influence of the incubation period on disease dissemination. We simulate the outbreak of the COVID-19 in Wuhan [39,43,44,45,46]. To simplify parameter estimation, we make σ=β+μ,τ=γ+δ1+μ1+μ,κ=δ2+μ2+μ. So system (2.1) can be simplified as

    {˙S=Λαa(1p)2SIaαs(1p)2SIsμS,˙Ia=αa(1p)2SIa+αs(1p)2SIsσIa,˙Is=σIaτIs,˙Q=τIsκQ. (5.1)

    Relevant parameters are shown in Table 1. The initial value of system (5.1) is (S(0),Ia(0),Is(0),Q(0))=(1.11×107,105,28,1) [39].

    Table 1.  Parameters estimation of Model (5.1).
    Parameters Definitions Values Source
    μ Mortality 1.6×105day1 [41]
    N Total population 1.11×107 [41]
    αa Transmission rate of asymptomatic infection 2.1×108day1 [39]
    αs Transmission rate of symptomatic infection 1.9×107day1 [39]
    γ Detection rate 0.13day1 [39]
    σ1 Mean latent period 2days [42]
    τ1 Mean infectious period 3days [42]
    κ1 Mean duration from loss infectiousness to death 8days [42]

     | Show Table
    DownLoad: CSV

    As σ is the conversion rate from asymptomatic to symptomatic infections, σ1 can be seen the incubation period from asymptomatic to symptomatic infections. The incubation period of COVID-19 is 2–14 days [47]. We study the spread of COVID-19 by changing the incubation period without considering isolation. When the value range of σ1 is 2 to 14, the reproductive number of system (5.1) is greater than 1, and the COVID-19 will spread. Figure 1a shows the change of S when σ=1/2,1/6,1/10,1/14. It can be seen that all susceptible persons will be infected if nothing is done. We can also see from Figure 1a that the higher the conversion rate, the faster the dissemination speed. Figure 1b shows the change of Ia when σ=1/2,1/6,1/10,1/14. It can be seen that the longer the incubation period, the greater the peak value of asymptomatic infections. When the incubation period is shorter, the peak infection will come faster for asymptomatic infections. Figure 1c shows the change of Is when σ=1/2,1/6,1/10,1/14. Contrary to asymptomatic infections, the shorter the incubation period, the greater the peak value for symptomatic infections. When the incubation period is shorter, the peak infection will also come faster for symptomatic infections. Figure 1d shows the change of Q when σ=1/2,1/6,1/10,1/14. When the incubation period is longer, the peak of infections will be lower and arrive later. To sum up, the longer the incubation period, the slower the disease spreads and the greater the peak. One reason why COVID-19 can spread across the world is largely due to its long incubation period.

    Figure 1.  Sequence diagrams for S,Ia,Is,Q with σ=1/2,1/6,1/10,1/14.

    This section studies the impact of the infectious period on disease dissemination without isolation. τ is the elimination rate of symptomatic population, then τ1 can represent its infectious period. The parameters follow the subsection 5.1. When τ is equal to 1/2, 1/3, 1/4 and 1/5, the reproductive numbers are greater than 1, and the COVID-19 will spread. Figure 2a shows the larger τ is, the slower S decreases. This means that the longer the infectious period, the faster the transmission speed. Figure 2b shows the longer the infectious period, the higher the asymptomatic infections. The peak will come earlier for the smaller elimination rate. The curves of symptomatic infections are similar to that of asymptomatic infections, which is shown in Figure 2c. However, there is little difference in the time of peak for symptomatic infections under different τ. Figure 2d is similar to Figure 2c. Its peak is lower and later. It is concluded that the longer the infectious period, the more infected people, and the faster the dissemination speed.

    Figure 2.  Sequence diagrams for S,Ia,Is,Q with τ=1/2,1/3,1/4,1/5.

    This section studies the impact of different isolation ratios on disease dissemination when the incubation periods from asymptomatic to symptomatic infections are 1/2, 1/7 and 1/14. Except for σ, the values of other parameters are the same as those in Subsection 5.1. Figures 35 are sequence diagrams for S,Ia,Is,Q of σ=1/2,1/7, and 1/14. We study the spread of the COVID-19 by changing the isolation ratio.

    Figure 3.  Sequence diagrams for S,Ia,Is,Q with 2-day incubation period.
    Figure 4.  Sequence diagrams for S,Ia,Is,Q with 7-day incubation period.
    Figure 5.  Sequence diagrams for S,Ia,Is,Q with 14-day incubation period.

    Figure 3 shows the curves of the disease dissemination with the isolation ratio from 0 to 70% during the 2-day incubation period. When p<0.5, the number of infections is millions. When 0.5<p<0.6, the number drops to six figures. When the isolation ratio exceeds 60%, the number of infected people will not exceed tens of thousands. When the isolation ratio exceeds 62%, the epidemic will not spread. Therefore, in view of the outbreak of COVID-19 in Wuhan, theoretically controlling the flow of more than 62% of people can prevent the wide spread.

    Figure 4 shows the curves of the disease dissemination with the isolation ratio from 0 to 70% during the 7-day incubation period. When 0<p<0.5, the number of infections is millions. When 0.55<p<0.62, the number drops to six figures. When the isolation ratio exceeds 62%, the number of infected people will not exceed tens of thousands. When the isolation ratio exceeds 65%, the epidemic will not spread.

    Figure 5 shows the curves of the disease dissemination with the isolation ratio from 0 to 75% during the 14-day incubation period. When 0<p<0.6, the number of infections is millions. When 0.6<p<0.65, the number drops to six figures. When the isolation ratio exceeds 65%, the number of infected people will not exceed tens of thousands. When the isolation ratio exceeds 68%, the epidemic will not spread.

    Through the above analysis, in order to stop the spread of COVID-19 in Wuhan, the isolation ratio should exceed 68%. Comparing Figures 35, we can find when the incubation period increases, the isolation ratio required to control the spread will increase. An important reason why COVID-19 is widely spread around the world is that its incubation period is very long.

    This subsection presents the stability of disease-free and endemic equilibria through simulation. In order to verify that the disease-free equilibrium is globally stable, the parameters of system (2.1) meet R0<1. The initial value is (S(0),Ia(0),Is(0),Q(0),R(0))=(800000,700,400,300,200). Figure 6a proves the number of susceptible population is constant by changing the initial value of S. Figure 6b, c and d show the numbers of asymptomatic, symptomatic and quarantined infections tend to 0 under different initial values. Therefor the disease-free equilibrium of system (2.1) is stable when R0<1.

    Figure 6.  Sequence diagrams with R0=0.76.

    System (2.1) has an endemic equilibrium when the parameters conform to R0>1. Select the initial value as (S(0),Ia(0),Is(0),Q(0),R(0))=(800000,700,400,300,200). When the initial value of S is changed, different curves eventually tend to the same positive value which can be seen in Figure 7a. Applying the same method to Ia,Is,Q, we get Figure 7b, c and d. Therefor system (2.1) has an endemic equilibrium with R0>1, and it is stable.

    Figure 7.  Sequence diagrams with R0=2.28.

    In this paper, we introduce the isolation ratio to quantify the impact of isolation on diseases dissemination. The basic reproductive number solved by the next-generation matrix can be divided into two parts, which are contributed by asymptomatic and symptomatic infections respectively. We obtain the effects of conversion rate from asymptomatic to symptomatic infections, mortality rate of infections and isolation ratio on disease dissemination. Through the stability analysis, we get the local and global stability conditions for the disease-free and endemic equilibria of the system. When R0<1, the disease-free equilibrium is globally asymptotically stable; When R0>1, the disease-free equilibrium is not stable. When R0<1, the positive equilibrium does not exist; When R0>1, the positive equilibrium exists and is stable. Taking the outbreak of COVID-19 in Wuhan as an example, when the proportion of the isolated population exceeds 68%, the epidemic will not be spread. Therefore, we can formulate different proportions of isolated population according to different regions. This not only ensures the epidemic will not spread on a large scale, but also does not stop the economic activities.

    This work was supported by the Science and Technology Research Project of Henan Province (222102240108).

    The authors declare there is no conflicts of interest.

    The datasets analysed during the current study are available from the corresponding author on reasonable request.

    [1] Kirk JTO (1994) Light and photosynthesis in aquatic ecosystems. Cambridge University Press, Cambridge, UK.
    [2] Jones RM and Smith WO Jr (2017) The influence of short-term events on the hydrographic and biological structure of the southwestern Ross Sea. J Mar Systems 166: 184-195. doi: 10.1016/j.jmarsys.2016.09.006
    [3] Smith WO Jr and Nelson DM (1985) Phytoplankton bloom produced by a receding ice edge in the Ross Sea: spatial coherence with the dendity field. Sci 227: 163-166. doi: 10.1126/science.227.4683.163
    [4] Arrigo KR and McClain CR (1994) Spring phytoplankton production in the western Ross Sea. Sci 266: 261-263. doi: 10.1126/science.266.5183.261
    [5] Arrigo KR, Robinson DH, Worthen DL, et al. (1999) Phytoplankton community structure and the drawdown of nutrients and CO2 in the Southern Ocean. Sci 283: 365-367. doi: 10.1126/science.283.5400.365
    [6] Smith WO Jr and Gordon LI (1997) Hyperproductivity of the Ross Sea (Antarctica) polynya during austral spring. Geophys Res Letters 24: 233-236. doi: 10.1029/96GL03926
    [7] Rousseau V, Chrétiennot-Dinet M-J, Jacobsen A, et al. (2007) The life cycle of Phaeocystis: state of knowledge and presumptive role in ecology. Biogeochem 83: 29-47. doi: 10.1007/s10533-007-9085-3
    [8] Smith WO Jr, Ainley DG, Arrigo KR, et al (2014) The oceanography and ecology of the Ross Sea. Annu Rev Mar Sci 6: 469-487. doi: 10.1146/annurev-marine-010213-135114
    [9] Smith WO Jr and Jones RM (2015) Vertical mixing, critical depths, and phytoplankton growth in the Ross Sea. ICES J. Mar. Sci 72: 1952-1960. doi: 10.1093/icesjms/fsu234
    [10] Leventer A and Dunbar RB (1996) Factors influencing the distribution of diatoms and other algae in the Ross Sea. J Geophys Res 101: 18489-18500. doi: 10.1029/96JC00204
    [11] Nuccio C, Innamorati M, Lazzara L, et al. (2000) Spatial and temporal distribution of phytoplankton coenoses in the Ross Sea. In: Faranda FM, Guglielmo L, Ianora A (Eds.), Ross Sea Ecology. Italiantartide Expeditions (1987-1995). Springer Verlag, Berlin, 231-245.
    [12] Fonda Umani S, Accornero A, Budillon G, et al(2002) Particulate matter and plankton dynamics in the Ross Sea Polynya of Terra Nova Bay during the austral aummer 1997/98. J Mar Syst 36: 29-49.
    [13] McGillicuddy DM Jr, Sedwick PN, Dinniman MS, et al. (2015) Iron supply and demand in an Antarctic shelf system. Geophys Res Letters 42:.
    [14] Falkowski PG and Raven JA (1997) Aquatic Photosynthesis. Princeton Univ Press, Princeton, NJ.
    [15] Kropuenske LR, Mills MM, van Dijken GL, et al. (2009) Photophysiology in two major Southern Ocean phytoplankton taxa: Photoprotection in Phaeocystis antarctica and Fragilariopsis cylindrus. Limnol Oceanogr 54: 1176-1196. doi: 10.4319/lo.2009.54.4.1176
    [16] Vass I, Styring S, Hundal T, et al (1992) Reversible and irreversible intermediates during photoinhibition of photosystem II: stable reduced QA species promote chlorophyll triplet formation. Proc Nat Acad Sci US 89 1408-1412.
    [17] Demmig-Adams B and Adams WW (1992) Photoprotection and other responses of plants to high light stress. Annu Rev Plant Physiol Plant Mol Biol 43: 599-626. doi: 10.1146/annurev.pp.43.060192.003123
    [18] Smith WO Jr and Donaldson K (2015) Photosynthesis-irradiance responses in the Ross Sea, Antarctica: a meta-analysis. Biogeosci 12: 1-11.
    [19] Arrigo KR, Mills MM, Kropuenske LR, et al (2010) Photophysiology in two major southern ocean phytoplankton taxa: photosynthesis and growth of Phaeocystis antarctica and Fragilariopsis cylindrus under different irradiance levels. Integr Compar Biol 50: 950–966. doi: 10.1093/icb/icq021
    [20] Smith WO Jr, Tozzi S, Sedwick PW, et al. (2013) Spatial and temporal variations in variable fluorescence in the Ross Sea (Antarctica): environmental and biological correlates. Deep-Sea Res I 79: 141-155. doi: 10.1016/j.dsr.2013.05.002
    [21] Genty B, Briantais JM, Baker NR (1989) The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochim Biophys Acta 990: 87-92. doi: 10.1016/S0304-4165(89)80016-9
    [22] Kolber ZS, Prasil O, Falkowski PG (1998) Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: defining methodology and experimental protocols. Biochim Biophys Acta 1367: 88-106. doi: 10.1016/S0005-2728(98)00135-2
    [23] Keller MD, Selvin RC, Claus W, et al. (1987) Medifor the culture of oceanoic ultraphytoplankton. J Phycol 23: 633-638.
    [24] Sedwick PN, Marsay CM, Aguilar-Islas AM, et al. (2011) Early-season depletion of dissolved iron in the Ross Sea polynya: implications for iron dynamics on the Antarctic continental shelf. J Geophys Res 116.
    [25] Kustka AB, Jones BM, Hatta M, et al. (2015) The influence of iron and siderophores on eukaryotic phytoplankton growth rates and community composition in the Ross Sea. Mar Chem 173: 195-207. doi: 10.1016/j.marchem.2014.12.002
    [26] Ryan-Keogh TJ, Delizo LM, Smith WO Jr, et al. (2016) Temporal progression of photosynthetic strategy by phytoplankton in the Ross Sea, Antarctica. J Mar Systems 166: 87-96.
    [27] Moisan TA, Mitchell BG (1999) Photophysiological acclimation of Phaeocystis antarctica Karsten under light limitation. Limnol Oceanogr 44: 247-258. doi: 10.4319/lo.1999.44.2.0247
    [28] Moisan TA, Olaziola M, Mitchell BG (1998) Xanthophyll cycling in Phaeocystis antarctica: changes in cellular fluorescence. Mar Ecol Prog Ser 169: 113-121. doi: 10.3354/meps169113
    [29] Van Leuwe MA and Stefels J (1998) Effects of iron and light stress on the biochemical composition of Antarctic Phaeocystis sp.(Prymnesiophyceae). II. Pigment composition. J Phycol 34: 496-503.
    [30] Liselotte R and Dale R. (1997) Photoinduction of UV-absorbing compounds in Antarctic diatoms and Phaeocystis antarctica. Mar Ecol Prog Ser 160: 13-25. doi: 10.3354/meps160013
    [31] Tang KW, Smith WO Jr, Shields AR, et al. (2009) Survival and recovery of Phaeocystis antarctica (Prymnesiophyceae) from prolonged darkness and freezing. Phil Trans Roy Soc, serB 276: 81-90.
    [32] Caron DA, Dennett MR, Lonsdale DJ, et al. (2000) Micro-zooplankton herbivory in the Ross Sea, Antarctica. Deep Sea Res II 47: 3249-3272. doi: 10.1016/S0967-0645(00)00067-9
    [33] Bertrand EM, Saito MA, Rose JM, et al. (2007) Vitamin B12 and iron co-limitation of phytoplankton growth in the Ross Sea. Limnol Oceanogr 52: 1079-1093. doi: 10.4319/lo.2007.52.3.1079
    [34] Sedick PN and DiTullio GR (1997) Regulation of algal blooms in Antarctic shelf waters by the release of iron from melting sea ice. Geophys Res Letters 24: 2515-2518. doi: 10.1029/97GL02596
    [35] Feng Y, Hare CE, Rose JM, et al. (2010) Interactive effects of CO2, irradiance and iron on Ross Sea phytoplankton. Deep Sea Res I 57: 368-383. doi: 10.1016/j.dsr.2009.10.013
    [36] Rose JM, Feng Y, DiTullio G, et al. (2009) Synergistic effects of iron and temperature on Antarctic plankton assemblages. Biogeosci 6: 3131–3147. doi: 10.5194/bg-6-3131-2009
    [37] Xu K, Fu F-X, Hutchins DA (2014) Comparative responses of two dominant Antarctic phytoplankton taxa to interactions between ocean acidification, warming, irradiance, and iron availability. Limnol Oceanogr 59: 1919-1931. doi: 10.4319/lo.2014.59.6.1919
  • This article has been cited by:

    1. Maximilian Pawleta, Susanne Kiefer, Edeltraud Gehrig, Visualization of relevant parameter dependencies in a delay SEIQ epidemic model — A live script program for didactic and interactive demonstrations, 2023, 14, 1793-9623, 10.1142/S1793962323500423
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5802) PDF downloads(966) Cited by(3)

Figures and Tables

Figures(7)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog