Research article Special Issues

A mathematical model to study the 2014–2015 large-scale dengue epidemics in Kaohsiung and Tainan cities in Taiwan, China

  • Received: 18 January 2019 Accepted: 19 April 2019 Published: 29 April 2019
  • Dengue virus (DENV) infection is endemic in many places of the tropical and subtropical regions, which poses serious public health threat globally. We develop and analyze a mathematical model to study the transmission dynamics of the dengue epidemics. Our qualitative analyzes show that the model has two equilibria, namely the disease-free equilibrium (DFE) which is locally asymp- totically stable when the basic reproduction number (R0) is less than one and unstable if R0 > 1, and endemic equilibrium (EE) which is globally asymp-totically stable when R0 > 1. Further analyzes reveals that the model exhibit the phenomena of backward bifurcation (BB) (a situation where a stable DFE co-exists with a stable EE even when the R0 < 1), which makes the disease control more diffi-cult. The model is applied to the real dengue epidemic data in Kaohsiung and Tainan cities in Taiwan, China to evaluate the fitting performance. We propose two reconstruction approaches to estimate the time-dependent R0, and we find a consistent fitting results and equivalent goodness-of-fit. Our findings highlight the similarity of the dengue outbreaks in the two cities. We find that despite the proximity in Kaohsiung and Tainan cities, the estimated transmission rates are neither completely synchronized, nor periodically in-phase perfectly in the two cities. We also show the time lags between the seasonal waves in the two cities likely occurred. It is further shown via sensitivity analysis result that proper sanitation of the mosquito breeding sites and avoiding the mosquito bites are the key control measures to future dengue outbreaks in Taiwan.

    Citation: Salihu Sabiu Musa, Shi Zhao, Hei-Shen Chan, Zhen Jin, Daihai He. A mathematical model to study the 2014–2015 large-scale dengue epidemics in Kaohsiung and Tainan cities in Taiwan, China[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 3841-3863. doi: 10.3934/mbe.2019190

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  • Dengue virus (DENV) infection is endemic in many places of the tropical and subtropical regions, which poses serious public health threat globally. We develop and analyze a mathematical model to study the transmission dynamics of the dengue epidemics. Our qualitative analyzes show that the model has two equilibria, namely the disease-free equilibrium (DFE) which is locally asymp- totically stable when the basic reproduction number (R0) is less than one and unstable if R0 > 1, and endemic equilibrium (EE) which is globally asymp-totically stable when R0 > 1. Further analyzes reveals that the model exhibit the phenomena of backward bifurcation (BB) (a situation where a stable DFE co-exists with a stable EE even when the R0 < 1), which makes the disease control more diffi-cult. The model is applied to the real dengue epidemic data in Kaohsiung and Tainan cities in Taiwan, China to evaluate the fitting performance. We propose two reconstruction approaches to estimate the time-dependent R0, and we find a consistent fitting results and equivalent goodness-of-fit. Our findings highlight the similarity of the dengue outbreaks in the two cities. We find that despite the proximity in Kaohsiung and Tainan cities, the estimated transmission rates are neither completely synchronized, nor periodically in-phase perfectly in the two cities. We also show the time lags between the seasonal waves in the two cities likely occurred. It is further shown via sensitivity analysis result that proper sanitation of the mosquito breeding sites and avoiding the mosquito bites are the key control measures to future dengue outbreaks in Taiwan.


    In 1960, Opial [12] established the following inequality:

    Theorem A Suppose fC1[0,h] satisfies f(0)=f(h)=0 and f(x)>0 for all x(0,h). Then the inequality holds

    h0|f(x)f(x)|dxh4h0(f(x))2dx, (1.1)

    where this constant h/4 is best possible.

    Many generalizations and extensions of Opial's inequality were established [2,4,5,6,7,8,9,10,11,15,16,17,18,19]. For an extensive survey on these inequalities, see [13]. Opial's inequality and its generalizations and extensions play a fundamental role in the ordinary and partial differential equations as well as difference equation [2,3,4,6,7,9,10,11,17]. In particular, Agarwal and Pang [3] proved the following Opial-Wirtinger's type inequalities.

    Theorem B Let λ1 be a given real number, and let p(t) be a nonnegative and continuous function on [0,a]. Further, let x(t) be an absolutely continuous function on [0,a], with x(0)=x(a)=0. Then

    a0p(t)|x(t)|λdt12a0[t(at)](λ1)/2p(t)dta0|x(t)|λdt. (1.2)

    The first aim of the present paper is to establish Opial-Wirtinger's type inequalities involving Katugampola conformable partial derivatives and α-conformable integrals (see Section 2). Our result is given in the following theorem, which is a generalization of (1.2).

    Theorem 1.1 Let λ1 be a real number and α(0,1], and let p(s,t) be a nonnegative and continuous functions on [0,a]×[0,b]. Further, let x(s,t) be an absolutely continuous function and Katugampola partial derivable on [0,a]×[0,b], with x(s,0)=x(0,t)=x(0,0)=0 and x(a,b)=x(a,t)=x(s,b)=0. If p>1, 1p+1q=1 Then

    a0b0p(s,t)|x(s,t)|λdαsdαtp+qpq(1α2)λ1(a0b0Γabpqλα(s,t)p(s,t)dαsdαt)
    ×a0b0|2st(x)α2(s,t)|λdαsdαt, (1.3)

    where

    Γabpqλα(s,t)={(st)1/p[(as)(bt)]1/q}α(λ1).

    Remark 1.1 Let x(s,t) reduce to s(t) and with suitable modifications, and p=q=2 and α=1, (1.3) become (1.2).

    Theorem C Let λ1 be a given real number, and let p(t) be a nonnegative and continuous function on [0,a]. Further, let x(t) be an absolutely continuous function on [0,a], with x(0)=x(a)=0. Then

    a0p(t)|x(t)|λdt12(a2)λ1(a0p(t)dt)a0|x(t)|λdt. (1.4)

    Another aim of this paper is to establish the following inequality involving Katugampola conformable partial derivatives and α-conformable integrals. Our result is given in the following theorem.

    Theorem 1.2 Let j=1,2 and λ1 be a real number, and let pj(s,t) be a nonnegative and continuous functions on [0,a]×[0,b]. Further, let xj(s,t) be an absolutely continuous function and Katugampola partial derivable on [0,a]×[0,b], with xj(s,0)=xj(0,t)=xj(0,0)=0 and xj(a,b)=xj(a,t)=xj(s,b)=0. Then for α(0,1]

    a0b0(p1(s,t)|x1(s,t)|λ+p2(s,t)|x2(s,t)|λ)dαsdαt
    12λ(1α2)λ1[(a0b0(st)α(λ1)p1(s,t)dαsdαt)a0b0|2st(x1)α2(s,t)|λdαsdαt
    +(a0b0(st)α(λ1)p2(s,t)dαsdαt)a0b0|2st(x2)α2(s,t)|λdαsdαt]. (1.5)

    Here, let's recall the well-known Katugampola derivative formulation of conformable derivative of order for α(0,1] and t[0,), given by

    Dα(f)(t)=limε0f(teεtα)f(t)ε, (2.1)

    and

    Dα(f)(0)=limt0Dα(f)(t), (2.2)

    provided the limits exist. If f is fully differentiable at t, then

    Dα(f)(t)=t1αdfdt(t).

    A function f is α-differentiable at a point t0, if the limits in (2.1) and (2.2) exist and are finite. Inspired by this, we propose a new concept of α-conformable partial derivative. In the way of (1.4), α-conformable partial derivative is defined in as follows:

    Definition 2.1 [20] (α-conformable partial derivative) Let α(0,1] and s,t[0,). Suppose f(s,t) is a continuous function and partial derivable, the α-conformable partial derivative at a point s0, denoted by s(f)α(s,t), defined by

    s(f)α(s,t)=limε0f(seεsα,t)f(s,t)ε, (2.3)

    provided the limits exist, and call α-conformable partial derivable.

    Recently, Katugampola conformable partial derivative is defined in as follows:

    Definition 2.2 [20] (Katugampola conformable partial derivatives) Let α(0,1] and s,t[0,). Suppose f(s,t) and s(f)α(s,t) are continuous functions and partial derivable, the Katugampola conformable partial derivative, denoted by 2st(f)α2(s,t), defined by

    2st(f)α2(s,t)=limε0s(f)α(s,teεtα)s(f)α(s,t)ε, (2.4)

    provided the limits exist, and call Katugampola conformable partial derivable.

    Definition 2.3 [20] (α-conformable integral) Let α(0,1], 0a<b and 0c<d. A function f(x,y):[a,b]×[c,d]R is α-conformable integrable, if the integral

    badcf(x,y)dαxdαy:=badc(xy)α1f(x,y)dxdy (2.5)

    exists and is finite.

    Theorem 3.1 Let λ1 be a real number and α(0,1], and let p(s,t) be a nonnegative and continuous functions on [0,a]×[0,b]. Further, let x(s,t) be an absolutely continuous function and Katugampola partial derivable on [0,a]×[0,b], with x(s,0)=x(0,t)=x(0,0)=0 and x(a,b)=x(a,t)=x(s,b)=0. If p>1, 1p+1q=1 Then

    a0b0p(s,t)|x(s,t)|λdαsdαtp+qpq(1α2)λ1(a0b0Γabpqλα(s,t)p(s,t)dαsdαt)
    ×a0b0|2st(x)α2(s,t)|λdαsdαt, (3.1)

    where

    Γabpqλα(s,t)={(st)1/p[(as)(bt)]1/q}α(λ1).

    Proof From (2.4) and (2.5), we have

    x(s,t)=s0t02st(x)α2(s,t)dαsdαt.

    By using Hölder's inequality with indices λ and λ/(λ1), we have

    |x(s,t)|λ/p[(s0t0|2st(x)α2(s,t)|dαsdαt)λ]1/p
    (1α2(st)α)(λ1)/p(s0t0|2st(x)α2(s,t)|λdαsdαt)1/p. (3.2)

    Similarly, from

    x(s,t)=asbt2st(x)α2(s,t)dαsdαt,

    we obtain

    |x(s,t)|λ/q(1α2[(as)(bt)]α)(λ1)/q(asbt|qst(x)α2(s,t)|λdαsdαt)1/q. (3.3)

    Now a multiplication of (3.2) and (3.3), and by using the well-known Young inequality gives

    |x(s,t)|λ(1α2)λ1Γabpqλα(s,t)(s0t0|2st(x)α2(s,t)|λdαsdαt)1/p×(asbt|2st(x)α2(s,t)|λdαsdαt)1/q(1α2)λ1Γabpqλα(s,t)(1ps0t0|2st(x)α2(s,t)|λdαsdαt+1qasbt|2st(x)α2(s,t)|λdαsdαt)
    =p+qpq(1α2)λ1Γabpqλα(s,t)a0b0|2st(x)α2(s,t)|λdαsdαt, (3.4)

    where

    Γabpqλα(s,t)={(st)1/p[(as)(bt)]1/q}α(λ1).

    Multiplying the both sides of (3.4) by p(s,t) and α–conformable integrating both sides over t from 0 to b first and then integrating the resulting inequality over s from 0 to a, we obtain

    a0b0p(s,t)|x(s,t)|λdαsdαt
    p+qpq(1α2)λ1a0b0Γabpqλα(s,t)p(s,t)(a0b0|2st(x)α2(s,t)|λdαsdαt)dαsdαt
    =p+qpq(1α2)λ1(a0b0Γabpqλα(s,t)p(s,t)dαsdαt)a0b0|2st(x)α2(s,t)|λdαsdαt.

    This completes the proof.

    Remark 3.1 Let x(s,t) reduce to s(t) and with suitable modifications, (3.1) becomes the following result.

    a0p(t)|x(t)|λdαtp+qpq(1α2)λ1a0Γapqλα(t)p(t)dαta0|Dα(x)(t)|λdαt, (3.5)

    where Dα(x)(t) is Katugampola derivative (2.1) stated in the introduction, and

    Γapqλα(t)={t1/p(at)1/q}α(λ1).

    Putting p=q=2 and α=1 in (3.5), (3.5) becomes inequality (1.2) established by Agarwal and Pang [3] stated in the introduction.

    Taking for α=1, p=q=2 and p(s,t)=constant in (3.1), we have the following interesting result.

    a0b0|x(s,t)|λdsdt12(ab)λ[B(λ+12,λ+12)]2a0b0|2stx(s,t)|λdsdt,

    where B is the Beta function.

    Theorem 3.2 Let j=1,2 and λ1 be a real number, and let pj(s,t) be a nonnegative and continuous functions on [0,a]×[0,b]. Further, let xj(s,t) be an absolutely continuous function and Katugampola partial derivable on [0,a]×[0,b], with xj(s,0)=xj(0,t)=xj(0,0)=0 and xj(a,b)=xj(a,t)=xj(s,b)=0. Then for α(0,1]

    a0b0(p1(s,t)|x1(s,t)|λ+p2(s,t)|x2(s,t)|λ)dαsdαt
    12λ(1α2)λ1[(a0b0(st)α(λ1)p1(s,t)dαsdαt)a0b0|2st(x1)α2(s,t)|λdαsdαt
    +(a0b0(st)α(λ1)p2(s,t)dαsdαt)a0b0|2st(x2)α2(s,t)|λdαsdαt]. (3.6)

    Proof Because

    x1(s,t)=s0t02st(x1)α2(s,t)dαsdαt=asbt2st(x1)α2(s,t)dαsdαt.

    Hence

    |x1(s,t)|12a0b0|2st(x1)α2(s,t)|dαsdαt.

    By Hölder's inequality with indices λ and λ/(λ1), it follows that

    p1(s,t)|x1(s,t)|λ12λp1(s,t)(a0b0|2st(x1)α2(s,t)|dαsdαt)λ
    12λ(1α2)λ1(st)α(λ1)p1(s,t)a0b0|2st(x1)α2(s,t)|λdαsdαt, (3.7)

    Similarly

    p2(s,t)|x2(s,t)|λ12λ(1α2)λ1(st)α(λ1)p2(s,t)a0b0|2st(x2)α2(s,t)|λdαsdαt, (3.8)

    Taking the sum of (3.7) and (3.8) and α-integrating the resulting inequalities over t from 0 to b first and then over s from 0 to a, we obtain

    a0b0(p1(s,t)|x1(s,t)|λ+p2(s,t)|x2(s,t)|λ)dαsdαt
    12λ(1α2)λ1{a0b0((st)α(λ1)p1(s,t)a0b0|2st(x1)α2(s,t)|λdαsdαt)dαsdαt+a0b0((st)α(λ1)p2(s,t)a0b0|2st(x2)α2(s,t)|λdαsdαt)dαsdαt}=12λ(1α2)λ1[(a0b0(st)α(λ1)p1(s,t)dαsdαt)a0b0|2st(x1)α2(s,t)|λdαsdαt+(a0b0(st)α(λ1)p2(s,t)dαsdαt)a0b0|2st(x2)α2(s,t)|λdαsdαt].

    Remark 3.2 Taking for x1(s,t)=x2(s,t)=x(s,t) and p1(s,t)=p2(s,t)=p(s,t) in (3.6), (3.6) changes to the following inequality.

    a0b0p(s,t)|x(s,t)|λdαsdαt12λ(1α2)λ1
    ×(a0b0(st)α(λ1)p(s,t)dαsdαt)a0b0|2st(x)α2(s,t)|λdαsdαt. (3.9)

    Putting α=1 in (3.9), we have

    a0b0p(s,t)|x(s,t)|λdsdt12λ(a0b0(st)λ1p(s,t)dsdt)a0b0|2stx(s,t)|λdsdt. (3.10)

    Let x(s,t) reduce to s(t) and with suitable modifications, and λ=1, (2.10) becomes the following result.

    a0p(t)|x(t)|dt12(a0p(t)dt)a0|x(t)|dt. (3.11)

    This is just a new inequality established by Agarwal and Pang [4]. For λ=2 the inequality (3.11) has appear in the work of Traple [14], Pachpatte [13] proved it for λ=2m (m1 an integer).

    Remark 3.3 Let xj(s,t) reduce to xj(t) (j=1,2) and pj(s,t) reduce to pj(t) (j=1,2) with suitable modifications, (3.6) becomes the following interesting result.

    a0(p1(t)|x1(t)|λ+p2(t)|x2(t)|λ)dαt12λ(1α2)λ1[(a0tα(λ1)p1(t)dαt)a0|Dα(x1)(t)|λdαt
    +(a0tα(λ1)p2(t)dαt)a0|Dα(x2)(t)|λdαt]. (3.12)

    Putting λ=1 and α=1 in (3.12), we have the following interesting result.

    a0(p1(t)|x1(t)|+p2(t)|x2(t)|)dt12(a0p1(t)dta0|x1(t)|dt+a0p2(t)dta0|x2(t)|dt).

    Finally, we give an example to verify the effectiveness of the new inequalities. Estimate the following double integrals:

    1010[st(s1)(t1)]λdsdt,

    where λ1.

    Let x1(s,t)=x2(s,t)=x(s,t)=st(s1)(t1), p1(s,t)=p2(s,t)=p(s,t)=(st)1α, a=b=1 and 0<α1, and by using Theorem 3.2, we obtain

    1010[st(s1)(t1)]λdsdt
    =1010p(s,t)|x(s,t)|λdαsdαt12λ(1α2)λ1(1010(st)α(λ1)p(s,t)dαsdαt)1010|2st(x)α2(s,t)|λdαsdαt=12λ(1α2)λ1(1α(λ1)+1)21010[(2s1)(2t1)]λ(st)α1dsdt=12λ(1α2)λ1(1α(λ1)+1)2(12α111tλ1(t+1)1αdt)212λ(1α2)λ1(1α(λ1)+1)2(12α12αα)2=22λα2λ(α(λ1)+1)2.

    We have introduced a general version of Opial-Wirtinger's type integral inequality for the Katugampola partial derivatives. The established results are generalization of some existing Opial type integral inequalities in the previous published studies. For further investigations we propose to consider the Opial-Wirtinger's type inequalities for other partial derivatives.

    I would like to thank that research is supported by National Natural Science Foundation of China(11471334, 10971205).

    The author declares no conflicts of interest.



    [1] H. H. G. Silva and I. G. Silva, Influence of eggs quiescence period on the aedes aegypti (Linnaeus, 1762) (diptera, culicidae) life cycle at laboratory conditions, Rev. Soc. Bras. Med. Trop., 32 (1999), 349–355.
    [2] C. J. McMeniman and S. L. O'Neill, A virulent wolbachia infection decreases the viability of the dengue vector aedes aegypti during periods of embryonic quiescence, PLoS Negl. Trop. Dis., 4 (2010), e748.
    [3] H. M. Yang and C. P. Ferreira, Assessing the effects of vector control on dengue transmission,Appl. Math. Comput., 198 (2008), 401–413.
    [4] H. M. Yang, M. L. G. Macoris, K. C. Galvani, et al., Follow up estimation of aedes aegypti entomological parameters and mathematical modellings. Biosyst., 103 (2011), 360–371.
    [5] H. M. Yang, Assessing the influence of quiescence eggs on the dynamics of mosquito aedes ae- gypti, Appl. Math., 5 (2014), 2696–2711.
    [6] S. M. Garba, A. B. Gumel and M. R. A. Bukar, Backward bifurcations in dengue transmission dynamics, Math. Biosci., 215 (2008), 11–25.
    [7] K. S. Vannice, A. Durbin and J. Hombach, Status of vaccine research and development of vaccines for dengue, Vaccine, 34 (2016), 2934–2938.
    [8] World Health Organization, Dengue control, 2017. Available from: http://www.who.int/denguecontrol/human/en/.
    [9] S. Sang, S. Gu, P. Bi, et al., Predicting unprecedented dengue outbreak using imported cases and climatic factors in guangzhou, PLoS Negl. Trop. Dis., 9 (2014), e0003808.
    [10] R. M. Lana, T. G. Carneiro, N. A. Honorio, et al., Seasonal and nonseasonal dynamics of aedes aegypti in Rio de Janeiro, Brazil: fitting mathematical models to trap data, Acta Tropic., 129 (2014), 25–32.
    [11] E. P. Pliego, J. Velazquez-Castro and A. F. Collar, Seasonality on the life cycle of aedes aegypti mosquito and its statistical relation with dengue outbreaks, Appl. Math. Model., 50 (2017), 484–496.
    [12] K. O. Okuneye, J. X. Valesco-Hernandez and A. B. Gumel, The "unholy" chikungunya-dengue- zika trinity: a theoretical analysis, J. Biol. Syst., 25 (2017), 587–603.
    [13] D. Gao, Y. Lou, D. He, et al., Prevention and control of zika as a mosquito-borne and sexually transmitted disease: a mathematical modeling analysis, Sci. Rep., 6 (2016), 28070.
    [14] S. Zhao, L. Stone, D. Gao, et al., Modelling the large-scale yellow fever outbreak in Luanda, Angola, and the impact of vaccination, PLoS Negl. Trop. Dis., 12 (2018), e0006158.
    [15] P. Guo, T. Liu, Q. Zhang, et al., Developing a dengue forecast model using machine learning: a case study in China, PLoS Negl. Trop. Dis., 11 (2017), e0005973.
    [16] T. P. O. Evans and S. R. Bishop, A spatial model with pulsed releases to compare strategies for the sterile insect technique applied to the mosquito Aedes aegypti, Math. Biosci., 254 (2014), 6–27.
    [17] R. R. Mahale, A. Mehta, A. K. Shankar, et al., Delayed subdural hematoma after recovery from dengue shock syndrome, J. Neurosci. Rural Pract., 7 (2016), 323–324.
    [18] D. J. Gubler, E. E. Ooi, S. G. Vasudevan, et al., Dengue and dengue hemorrhagic fever, 2nd ed. Wallingford, UK: CAB International, 2014.
    [19] S. F. Wang, W. H. Wang, K. Chang, et al., Severe dengue fever outbreak in Taiwan, Am. J. Trop. Med. Hyg., 94 (2016), 193–197.
    [20] M. Chan and M. A. Johansson, The incubation periods of dengue viruses, PLoS One, 7 (2012), e50972.
    [21] C. A. Manore, K. S. Hickman, S. Xu, et al., Comparing dengue and chikungunya emergence and endemic transmission in A. egypti and A.albopictus, J. Theor. Bio., 356 (2014), 174–191.
    [22] Sanofi Pasteur, First Dengue Vaccine Approved in More than 10 Countries by Sanofi Pasteur, 2019. Available from: https://www.sanofipasteur.com/en/.
    [23] World Health Organization, Updated Questions and Answers related to information presented in the Sanofi Pasteur press release on 30 November 2017 with regards to the dengue vaccine Deng-vaxia, 2019. Available from: https://www.who.int/immunization/diseases/dengue/q_and_a_dengue_vaccine_dengvaxia/en/.
    [24] H. M. Yang, M. L. G. Macoris, K. C. Galvani, et al., Assessing the effects of temperature on the population of Aedes aegypti, the vector of dengue, Epidemiol. Infect., 137 (2009), 1188–1202.
    [25] H. M. Yang, M. L. G. Macoris, K. C. Galvani, et al., Assessing the effects of temperature on dengue transmission, Epidemiol. Infect., 137 (2009), 1179–1187.
    [26] A. B. Gumel, Causes of backward bifurcations in some epidemiological models, J. Math. Anal. Appl., 395 (2012), 355–365.
    [27] K. Okuneye and A. B. Gumel, Analysis of a temperature- and rainfall-dependent model for malaria transmission dynamics, Mathe. Biosci., 287 (2017), 72–92.
    [28] N. Hussaini, K. Okuneye and A. B. Gumel, Mathematical analysis of a model for zoonotic visceral leishmaniasis, Infect. Dis. Model., 2 (2017), 455–474.
    [29] S.Usaini, U.T.MustaphaandS.M.Sabiu, Modellingscholasticunderachievementasacontagious disease, Math. Meth. Appl. Sci., 41 (2018), 8603–8612.
    [30] P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equi-libria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48.
    [31] T. P. Endy, S. Chunsuttiwat, A. Nisalak, et al., Epidemiology of inapparent and symptomatic acute dengue virus infection: a prospective study of primary school children in Kamphaeng Phet, Thailand, Amer. J. Epi., 156 (2002), 40–51.
    [32] M. J. P. Poirier, D. M. Moss, K. R. Feeser, et al., Measuring Haitian children's exposure to chikun-gunya, dengue and malaria, Bull World Health Organ., 94 (2016), 817–825.
    [33] C. H. Chen, Y. C. Huang and K. C. Kuo, Clinical features and dynamic ordinary laboratory tests differentiating dengue fever from other febrile illnesses in children, J. Microb. Immunol. Infect.,51 (2018), 614–620.
    [34] S. Zhao, Y. Lou, A. P. Chiu, et al., Modelling the skip-and-resurgence of Japanese encephalitis epidemics in Hong Kong, J. Theor. Biol., 454 (2018), 1–10.
    [35] Y. Xiao, S. Tang and J. Wu, Media impact switching surface during an infectious disease outbreak,Scient. Rep., 5 (2015), 7838.
    [36] D. L. Smith, K. E. Battle, S. I. Hay, et al., Ross, Macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens, PLoS Pathog., 8 (2012), e1002588.
    [37] Q. Lin, Z. Lin, A. P. Y. Chiu, et al., Seasonality of influenza A(H7N9) virus in China -fitting simple epidemic models to human cases, PLoS One, 11 (2016), e0151333.
    [38] C. Breto, D. He, E. L. Ionides , et al., Time series analysis via mechanistic models, Ann. Appl. Stat., 3 (2009), 319–348.
    [39] The website of R package "pomp": statistical inference for partially-observed Markov processes, 2018. Available from: https://kingaa.github.io/pomp/.
    [40] D. He, R. Lui, L. Wang, et al., Global Spatio-temporal Patterns of influenza in the post-pandemic era, Sci Rep., 5 (2015), 11013.
    [41] E. L. Ionides, C. Breto and A. A. King, Inference for nonlinear dynamical systems, Proc. Natl. Acad. Sci., 103 (2006), 18438–18443.
    [42] E. L. Ionides, A. Bhadra, Y. Atchade, et al., Iterated filtering, Ann. Stat., 39 (2011), 1776–1802.
    [43] D. J. Earn, D. He, M. B. Loeb, et al., Effects of school closure on incidence of pandemic influenza in Alberta, Canada, Ann. Intern. Med., 156 (2012), 173–181.
    [44] A. Camacho, S. Ballesteros, A. L. Graham, et al., Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study, Proc. Biol. Sci., 278 (2011), 3635–3643.
    [45] D. He, J. Dushoff, T. Day, et al., Mechanistic modelling of the three waves of the 1918 influenza pandemic, Theory Ecol., 4 (2011), 283–288.
    [46] D. He, E. L. Ionides and A. A. King, Plug-and-play inference for disease dynamics: measles in large and small populations as a case study, J. R. Soc. Interf., 7 (2010), 271–283.
    [47] D. He, D. Gao, Y. Lou, et al., A comparison study of zika virus outbreaks in French Polynesia, Colombia and the state of Bahia in Brazil, Sci. Rep., 7 (2017), 273.
    [48] S. Zhao, S. S. Musa, J. Qin , et al., Phase-shifting of the transmissibility of macrolide-sensitive and resistant Mycoplasma pneumoniae epidemics in Hong Kong, from 2015 to 2018, Int. J. Infect. Dis., 81 (2019), 251–253.
    [49] Taiwan National Infectious Disease Statistics System, Dengue, 2018. Available from: https://nidss.cdc.gov.tw/en/Default.aspx?op=4.
    [50] C. Yang, X. Wang, D. Gao , et al., Impact of awareness programs on cholera dynamics: two modeling approaches, Bull. Math. Biol., 79 (2017), 2109–2131.
    [51] G. Sun, J. Xie, S. Huang, et al., Transmission dynamics of cholera: mathematical modeling and control strategies, Commun. Non. Sci. Numer. Simulat., 45 (2017), 235–244.
    [52] J. P. LaSalle, The stability of dynamical systems, Regional Conference Series in Applied Mathe-matics, Soceity for industrial and Applied Mathematics, Philadelphia, 1976.
    [53] D. S. Shepard, Y. A. Halasa, B. K. Tyagi, et al., Economic and disease burden of dengue illness in India, Am. J. Trop. Med. Hyg., 91 (2014), 1235–1242.
    [54] N. T. Toan, S. Rossi, G. Prisco, et al., Dengue epidemiology in selected endemic countries: factors influencing expansion factors as estimates of underreporting, Trop. Med. Int. Health., 20 (2015), 840–863.
    [55] E. Sarti, M. L'Azou, M. Mercado, et al., A comparative study on active and passive epidemio-logical surveillance for dengue in five countries of Latin America, Int. J. Infect. Dis., 44 (2016), 44–49.
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