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doi: 10.3934/dcdsb.2020009

Dynamical analysis of chikungunya and dengue co-infection model

Salihu Sabiu Musa 1,2, , Nafiu Hussaini 3,, , Shi Zhao 1,4, and  He Daihai 1,,

1. 

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
2. 

Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria
3. 

Department of Mathematical Sciences, Bayero University Kano, Nigeria
4. 

School of Nursing, Hong Kong Polytechnic University, Hong Kong, China

* Corresponding authors: nhussaini.mth@buk.edu.ng; and daihai.he@polyu.edu.hk

Received  February 2019 Revised  July 2019 Published  December 2019

Fund Project: The authors acknowledge the helpful comments by the editor and the reviewers

The aim of this paper is to design and analyze a nonlinear mechanistic model for chikungunya (CHIKV) and dengue (DENV) co-endemicity. The model can assess the epidemiological consequences of the spread of each disease on the co-infection transmission dynamics. Although the two diseases are different, they exhibit similar dynamical features which show that to combat/control CHIKV virus (or co-infection with DENV virus) we can employ DENV control strategies and vice versa. Our analytical results show that each sub-model and the full model have two disease-free equilibria (i.e., trivial disease-free equilibrium (TDFE) and non-trivial disease-free equilibrium (NTDFE)). Further, qualitative analyses reveal that each of the sub-models exhibits the phenomenon of backward bifurcation (where a stable NTDFE co-exits with a stable endemic equilibrium (EE)). Epidemiologically, this implies that, in each case (CHIKV or DENV), the basic requirement of making the associated reproduction number to be less-than unity is no longer sufficient for the disease eradication. We further highlight that the full model, consisting of twenty-six (26) mutually exclusive compartments representing the human and mosquito dynamics, also exhibits the phenomenon of backward bifurcation. We fit the full model and its sub-models using realistic data from India. Sensitivity analysis using the partial rank correlation coefficient (PRCC) is used for ranking the importance of each parameter-output. The results suggested that the mosquito removal rates, the transmission rates, and the mosquito maturation rate are the top control parameters for combating CHIKV, DENV and CHIKV-DENV co-infection outbreaks.

Keywords: Co-infection, chikungunya, dengue, stability, sensitivity analysis.
Mathematics Subject Classification: Primary: 34K18, 34K25; Secondary: 92D30.
Citation: Salihu Sabiu Musa, Nafiu Hussaini, Shi Zhao, He Daihai. Dynamical analysis of chikungunya and dengue co-infection model. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020009
References:
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show all references

References:
[1]

F. B. Agusto, S. Easley, K. Freeman and M. Thomas, Mathematical model of three age-structured transmission dynamics of Chikungunya virus, Journal of Computational and Mathematical Methods in Medicine, (2016), Art. ID 4320514, 31 pp. doi: 10.1155/2016/4320514.  Google Scholar

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J. A. Ayukekbong, Dengue virus in nigeria: Current status and future perspective, British Journal of Virology, 1 (2014), 106-111.   Google Scholar

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J. Carr, Application of Centre Manifold Theory, Applied Mathematical Sciences, 35. Springer-Verlag, New York-Berlin, 1981.  Google Scholar

[4]

C. Castillo-Chavez and B. J. Song, Dynamical model of tuberclosis and their applications, Mathematical Bioscience Engineering, 1 (2004), 361-404.  doi: 10.3934/mbe.2004.1.361.  Google Scholar

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D. Cecilia, Current status of dengue and chikungunya in India, WHO South-East Asia Journal of Public Health, 3 (2014), 22-26.  doi: 10.4103/2224-3151.206879.  Google Scholar

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[7]

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Y. Dumont and F. Chiroleu, Vector control for the Chikungunya disease, Mathematical Bioscience Engineering, 7 (2010), 315-348.  doi: 10.3934/mbe.2010.7.313.  Google Scholar

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[11]

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D. Gao, Y. Lou, D. He, T. C. Porco, Y. Kuang, G. Chowell and S. G. Ruan, Prevention and control of zika as a mosquito-borne and sexually transmitted disease: A mathematical modeling analysis, Scientific Report, 6 (2016), 28070. doi: 10.1038/srep28070.  Google Scholar

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S. M. Garba, A. B. Gumel and M. R. Abu Bukar, Backward bifurcations in dengue transmission dynamics, Mathematical Biosciences, 215 (2008), 11-25.  doi: 10.1016/j.mbs.2008.05.002.  Google Scholar

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D. J. Gubler, E. E. Ooi, S. G. Vasudevan and J. Farrar, Dengue and dengue Hemorrhagic Fever, 2nd ed. Wallingford, UK: CAB International, 2014. Google Scholar

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D. J. Gubler, Dengue and dengue hemorrhagic fever, Clinical Microbiology Reviews, 11 (1998), 480–496, https://doi.org/10.1128/CMR.11.3.480. Google Scholar

[19]

A. B. Gumel, Causes of backward bifurcations in some epidemiological models, Journal of Mathematical Analysis and Applications, 395 (2012), 355-365.  doi: 10.1016/j.jmaa.2012.04.077.  Google Scholar

[20]

N. Hussaini, J.M.-S Lubuma, K. Barley and A. B. Gumel, Mathematical analysis of a model for AVL-HIV co-endemicity, Mathematical Biosciences, 271 (2016), 80-95.  doi: 10.1016/j.mbs.2015.10.008.  Google Scholar

[21]

N. Hussaini, K. Okuneye and A. B. Gumel, Mathematical analysis of a model for zoonotic visceral leishmaniasis, Infectious Disease Modelling, 2 (2017), 455-474.  doi: 10.1016/j.idm.2017.12.002.  Google Scholar

[22]

J. Jain, R. B. S. Kushwaha, S. S. Singha, A. Sharma, A. Adakb, O. P. Singh, R. K. Bhatnagar, S. K. Snbbarao and S. Sunil, Evidence for natural vertical transmission of chikungunya viruses in field populations of Aedes aegypti in Delhi and Haryana states in India—A preliminary report, Acta. Tropica., 162 (2016), 46-55.  doi: 10.1016/j.actatropica.2016.06.004.  Google Scholar

[23]

R. M. Lana, T. G. S. Carneiro, N. A. Hono'rio and C. T. Code, Seasonal and nonseasonal dynamics of Aedes aegypti in Rio de Janeiro, Brazil: Fitting mathematical models to trap data, Acta Tropica, 129 (2014), 25–32, https://doi.org/10.1016/j.actatropica.2013.07.025. Google Scholar

[24]

J. P. LaSalle, The Stability of Dynamical Systems, Regional Conference Series in Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, Pa., 1976.  Google Scholar

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R. R. Mahale, A. Mehta, A. K. Shankar and R. Srinivasa, Delayed subdural hematoma after recovery from dengue shock syndrome, Journal of Neurosciences in Rural Practice, 7 (2016), 323-324.  doi: 10.4103/0976-3147.178655.  Google Scholar

[26]

C. A. Manore, K. S. Hickman, S. Xu, H. J. Wearing and J. M. Hyman, Comparing dengue and chikungunya emergence and endemic transmission in A. aegypti and A. albopictus, Journal of Theoretical Biology, 356 (2014), 174-191.  doi: 10.1016/j.jtbi.2014.04.033.  Google Scholar

[27]

E. Massad, S. Ma, M. N. Burattini, Y. Tun, F. A. B. Coutinho and L. W. Ang, The risk of chikungunya fever in a dengue-endemic area, Journal of Travel Medicine, 15 (2008), 147-155.  doi: 10.1111/j.1708-8305.2008.00186.x.  Google Scholar

[28]

D. Moulay, M. A. Aziz-Alaoui and M. Cadivel, The chikungunya disease: Modeling, vector and transmission global dynamics, Mathematical Biosciences, 229 (2011), 50-63.  doi: 10.1016/j.mbs.2010.10.008.  Google Scholar

[29]

S. S. Musa, S. Zhao, H. S. chan, Z. Jin and D. He, A mathematical model to study the 2014-2015 large-scale dengue epidemics in Kaohsiung and Tainan cities in Taiwan, China, Mathematical Biosciences Engineering, 16 (2019), 3841–3863, http://dx.doi.org/10.3934/mbe.2019190. Google Scholar

[30]

S. Naowarat, W. Tawarat and I. M. Tang, Control of the transmission of chikungunya fever epidemic through the use of adulticide, Americal Journal of Applied Sciences, 8 (2011), 558–565, http://repository.li.mahidol.ac.th/dspace/handle/123456789/12916. doi: 10.3844/ajassp.2011.558.565.  Google Scholar

[31]

National Vector Borne Disease Control Programme, 22, Shamnath Marg, Delhi 110054, 2018, http://nvbdcp.gov.in/index4.php?lang=1&level=0&linkid=486&lid=3765 and http://nvbdcp.gov.in/index4.php?lang=1&level=0&linkid=431&lid=3715. Google Scholar

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[33]

N. Nuraini, E. Soewono and K. A. Sidarto, Mathematical model of dengue disease transmission with severe DHF compartment, BULLETIN of the Malaysian Mathematical Sciences Society, 30 (2007), 143–157, http://math.usm.my/bulletin.  Google Scholar

[34]

K. Okuneye and A. B. Gumel, Analysis of a temperature- and rainfall-dependent model for malaria transmission dynamics, Mathematical Biosciences, 287 (2017), 72-92.  doi: 10.1016/j.mbs.2016.03.013.  Google Scholar

[35]

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Figure 1.  Schematic diagram of the model Eqn (1). The yellow line represent infection of CHIKV from the DENV recovered class, while the orange line represent infection of DENV from the CHIKV recovered class. The green and the blue lines represent CHIKV and DENV new infection as well as the recovery of individuals, all the parameters are defined in Table 2
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Figure 2.  Schematic diagram of the model Eqns(2). The yellow line represent the vertical transmission of CHIKV virus from aquatic stage. The green and blue lines represent new infection of CHIKV and DENV, all the parameters are defined in Table 2
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Figure 3.  Backward bifurcation diagram of the model (9)
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Figure 4.  Fitting result of the CHIKV only sub-model (6). We used the parameter values from Table 3 and the following initial conditions: $ S_H(0) = 1.3\times 10^9 $, $ E_C(0) = 1.2\times 10^5 $, $ I_C(0) = 48176 $, $ R_C(0) = 4000 $, $ A(0) = 8\times 10^7 $, $ S_v(0) = 5\times 10^7 $, $ E_{vC}(0) = 2\times 10^5 $ and $ I_{vC}(0) = 10^4 $. The vertical axes indicate the cumulative number of CHIKV cases in India from 2010 to 2017
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Figure 6.  Fitting result of the full model of Eqns (1)-(2). We used the parameter values from Table 3 and the following initial conditions: $ S_H(0) = 1179681900 $, $ E_C(0) = 1200 $, $ E_D(0) = 1200 $, $ E_{CD}(0) = 10 $, $ I_C(0) = 8 $, $ I_D(0) = 8 $, $ I_{CE}(0) = 8 $, $ I_{DE}(0) = 8 $, $ I_{CD}(0) = 6 $, $ E_{CT}(0) = 12 $, $ E_{DT}(0) = 12 $, $ I_{CT}(0) = 8 $, $ I_{DT}(0) = 8 $, $ R_{C}(0) = 8 $, $ R_{D}(0) = 8 $, $ T(0) = 3 $, $ A(0) = 8\times 10^{10} $, $ S_{v}(0) = 5\times 10^{10} $, $ E_{vC}(0) = 2\times 10^5 $, $ E_{vD}(0) = 2\times 10^5 $, $ E_{M}(0) = 2\times 10^5 $, $ I_{vC}(0) = 10^4 $, $ I_{vD}(0) = 10^4 $, $ I_{vCE}(0) = 100 $, $ I_{vDE}(0) = 10^4 $, and $ I_{vM}(0) = 10^4 $. The vertical axes indicate the cumulative number of DENV cases in India since 2007 to 2012
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Figure 5.  Fitting result of the DENV only sub-model(9). We used the parameter values from Table 3 and the following initial conditions: $ S_H(0) = 1.3\times 10^9 $, $ E_D(0) = 1.1\times 10^5 $, $ I_D(0) = 28292 $, $ R_D(0) = 3000 $, $ A(0) = 8\times 10^6 $, $ S_v(0) = 5\times 10^6 $, $ E_{vD}(0) = 2\times 10^5 $ and $ I_{vD}(0) = 10^5 $. The vertical axes indicate the cumulative number of DENV cases in India from 2010 to 2017
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Figure 7.  The PRCCs (of the CHIKV only sub-model(6)) of basic reproduction number (panel (a)) and infection attack rate (panel (b)) with respect to the model parameters. $ m_1 $ denotes the mosquito to human ratio. The blue dots are the estimated correlations and the bars represent the 95% CIs. The ranges of parameters are given in Table 3
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Figure 8.  The PRCCs (of the DENV only sub-model(9)) of basic reproduction number (panel (a)) and infection attack rate (panel (b)) with respect to the model parameters. $ m_2 $ denotes the mosquito to human ratio. The blue dots are the estimated correlations and the bars represent the 95% CIs. The ranges of parameters are given in Table 3
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Table 1.  Interpretation of the compartmental variables of the model Eqns (1)-(2)
Variable Interpretation/Description
$ N_{H} $ Total population of humans
$ S_{H} $ Population of susceptible humans
$ E_{C} $ Population of asymptomatic CHIKV individuals
$ E_{D} $ Population of asymptomatic DENV individuals
$ E_{CD} $ Population of humans exposed to both CHIKV and DENV parasite
$ I_{C} $ Population of CHIKV-infected (only) humans with clinical symptoms of CHIKV
$ I_{D} $ Population of DENV-infected (only) humans with clinical symptoms of DENV
$ I_{CD} $ Population of dually-infected humans with symptoms of both CHIKV and DENV
$ I_{CE} $ Population of CHIKV-infected humans with clinical symptoms of CHIKV but exposed to DENV
$ I_{DE} $ Population of DENV-infected humans with clinical symptoms of DENV but exposed to CHIKV
$ R_{C} $ Recovered CHIKV-infected humans
$ R_{D} $ Recovered DENV-infected humans
$ E_{CT} $ Population of individuals exposed to CHIKV but recovered from DENV with permanent immunity
$ E_{DT} $ Population of individuals exposed to DENV but recovered from CHIKV with permanent immunity
$ I_{CT} $ Population of CHIKV-infected individuals with clinical symptoms of CHIKV but recovered from DENV with permanent immunity
$ I_{DT} $ Population of DENV-infected individuals with clinical symptoms of DENV but recovered from CHIKV with permanent immunity
$ T $ Population of individuals who recovered from both CHIKV and DENV with permanent immunity
$ N_{V} $ Total population of mosquitoes
$ A $ population of immature mosquitoes (egg, lava and pupa stages)
$ N_{I} $ Total population of adult mosquitoes
$ S_{v} $ population of adult mosquitoes susceptible to both CHIKV and DENV
$ E_{vC} $ population of adult mosquitoes exposed to CHIKV
$ E_{vD} $ population of adult mosquitoes exposed to DENV
$ E_{M} $ population of adult mosquitoes exposed to both CHIKV and DENV viruses
$ I_{vC} $ Population of CHIKV-infected (only) adult mosquitoes
$ I_{vD} $ Population of DENV-infected (only) adult mosquitoes
$ I_{vCE} $ Population of CHIKV-infected adult mosquitoes that are exposed to DENV
$ I_{vDE} $ Population of DENV-infected adult mosquitoes that are exposed to CHIKV
$ I_{vM} $ Population of adult mosquitoes infected to both CHIKV and DENV
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Variable Interpretation/Description
$ N_{H} $ Total population of humans
$ S_{H} $ Population of susceptible humans
$ E_{C} $ Population of asymptomatic CHIKV individuals
$ E_{D} $ Population of asymptomatic DENV individuals
$ E_{CD} $ Population of humans exposed to both CHIKV and DENV parasite
$ I_{C} $ Population of CHIKV-infected (only) humans with clinical symptoms of CHIKV
$ I_{D} $ Population of DENV-infected (only) humans with clinical symptoms of DENV
$ I_{CD} $ Population of dually-infected humans with symptoms of both CHIKV and DENV
$ I_{CE} $ Population of CHIKV-infected humans with clinical symptoms of CHIKV but exposed to DENV
$ I_{DE} $ Population of DENV-infected humans with clinical symptoms of DENV but exposed to CHIKV
$ R_{C} $ Recovered CHIKV-infected humans
$ R_{D} $ Recovered DENV-infected humans
$ E_{CT} $ Population of individuals exposed to CHIKV but recovered from DENV with permanent immunity
$ E_{DT} $ Population of individuals exposed to DENV but recovered from CHIKV with permanent immunity
$ I_{CT} $ Population of CHIKV-infected individuals with clinical symptoms of CHIKV but recovered from DENV with permanent immunity
$ I_{DT} $ Population of DENV-infected individuals with clinical symptoms of DENV but recovered from CHIKV with permanent immunity
$ T $ Population of individuals who recovered from both CHIKV and DENV with permanent immunity
$ N_{V} $ Total population of mosquitoes
$ A $ population of immature mosquitoes (egg, lava and pupa stages)
$ N_{I} $ Total population of adult mosquitoes
$ S_{v} $ population of adult mosquitoes susceptible to both CHIKV and DENV
$ E_{vC} $ population of adult mosquitoes exposed to CHIKV
$ E_{vD} $ population of adult mosquitoes exposed to DENV
$ E_{M} $ population of adult mosquitoes exposed to both CHIKV and DENV viruses
$ I_{vC} $ Population of CHIKV-infected (only) adult mosquitoes
$ I_{vD} $ Population of DENV-infected (only) adult mosquitoes
$ I_{vCE} $ Population of CHIKV-infected adult mosquitoes that are exposed to DENV
$ I_{vDE} $ Population of DENV-infected adult mosquitoes that are exposed to CHIKV
$ I_{vM} $ Population of adult mosquitoes infected to both CHIKV and DENV
Table 2.  Interpretation of the parameters of the model Eqns (1)-(2)
Parameter Interpretation/Description
$ \Pi_H,\Pi_V $ Recruitment rate of humans and mosquitoes, respectively
$ \mu_{H} $ Natural death rate of humans
$ \mu_A $ Death rate of immature mosquitoes
$ \mu_v $ Death rate of adult mosquitoes
$ \lambda_{C} $ Rates of CHIKV force of infection in humans
$ \lambda_{D} $ Rates of DENV force of infection in humans
$ \lambda_{vC} $ Rates of CHIKV force of infection in mosquitoes
$ \lambda_{vD} $ Rates of DENV force of infection in mosquitoes
$ \beta_{C} $ Transmission probability for CHIKV to humans
$ \beta_{D} $ Transmission probability for DENV to humans
$ \beta_{v} $ Transmission probability from an infectious human to a susceptible adult mosquitoes
$ b_{1} $ Number of bites per human per unit time
$ b_{2} $ Number of bites per mosquitoes per unit time
$ \xi $ Fraction of immature mosquitoes becoming susceptible adult
$ \alpha_1 $ Modification parameter for the heterogeneity of DENV infection between susceptible humans and humans exposed to CHIKV
$ \alpha_2 $ Modification parameter for the heterogeneity of CHIKV infection between susceptible humans and humans exposed to DENV
$ \omega_1 $ Modification parameter for the heterogeneity of DENV infection between susceptible adult mosquitoes and those exposed to CHIKV
$ \omega_2 $ Modification parameter for the heterogeneity of CHIKV infection between susceptible adult mosquitoes and those exposed to DENV
$ \sigma_{m} $ Number of times a mosquito bites humans per unit time
$ \sigma_{H} $ Maximum number of mosquito bites a human can receive per unit time
$ \sigma_{C} $ Progression rate of humans from exposed state of CHIKV to the infectious state of CHIKV
$ \sigma_{D} $ Progression rate of humans from exposed state of DENV to the infectious state of DENV
$ \sigma_{vC} $ Progression rate of adult mosquitoes from exposed state of CHIKV to the infectious state of CHIKV
$ \sigma_{vD} $ Progression rate of adult mosquitoes from exposed state of DEN to the infectious state of DENV
$ \gamma_i(i=1,4) $ Progression rates of humans to active CHIKV classes
$ \gamma_j(j=2,3) $ Progression rates of humans to active DENV classes
$ \theta_1,\rho_2 $ Progression rates of adult mosquitoes to active CHIKV classes
$ \theta_2,\rho_1 $ Progression rates of adult mosquitoes to active DENV classes
$ \tau_{C} $ Recovery rate of humans from infectious state of CHIKV to the recovered state of CHIKV
$ \tau_{D} $ Recovery rate of humans from infectious state of DENV to the recovered state of DENV
$ \eta $ Modification parameters for the increase in infectiousness of dually-infected humans in comparison to mono-infected humans
$ \eta_{C},\eta_{D},\eta_{CD},\eta_{vC},\eta_{vD},\eta_{M} $ Modification parameters for the increase in infectiousness for the exposed classes in humans and mosquitoes, respectively
$ \delta_{C},\delta_{D},\delta_{CE},\delta_{DE},\delta_{CD},\delta_{CT},\delta_{DT} $ Disease-induced death rates for humans
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Parameter Interpretation/Description
$ \Pi_H,\Pi_V $ Recruitment rate of humans and mosquitoes, respectively
$ \mu_{H} $ Natural death rate of humans
$ \mu_A $ Death rate of immature mosquitoes
$ \mu_v $ Death rate of adult mosquitoes
$ \lambda_{C} $ Rates of CHIKV force of infection in humans
$ \lambda_{D} $ Rates of DENV force of infection in humans
$ \lambda_{vC} $ Rates of CHIKV force of infection in mosquitoes
$ \lambda_{vD} $ Rates of DENV force of infection in mosquitoes
$ \beta_{C} $ Transmission probability for CHIKV to humans
$ \beta_{D} $ Transmission probability for DENV to humans
$ \beta_{v} $ Transmission probability from an infectious human to a susceptible adult mosquitoes
$ b_{1} $ Number of bites per human per unit time
$ b_{2} $ Number of bites per mosquitoes per unit time
$ \xi $ Fraction of immature mosquitoes becoming susceptible adult
$ \alpha_1 $ Modification parameter for the heterogeneity of DENV infection between susceptible humans and humans exposed to CHIKV
$ \alpha_2 $ Modification parameter for the heterogeneity of CHIKV infection between susceptible humans and humans exposed to DENV
$ \omega_1 $ Modification parameter for the heterogeneity of DENV infection between susceptible adult mosquitoes and those exposed to CHIKV
$ \omega_2 $ Modification parameter for the heterogeneity of CHIKV infection between susceptible adult mosquitoes and those exposed to DENV
$ \sigma_{m} $ Number of times a mosquito bites humans per unit time
$ \sigma_{H} $ Maximum number of mosquito bites a human can receive per unit time
$ \sigma_{C} $ Progression rate of humans from exposed state of CHIKV to the infectious state of CHIKV
$ \sigma_{D} $ Progression rate of humans from exposed state of DENV to the infectious state of DENV
$ \sigma_{vC} $ Progression rate of adult mosquitoes from exposed state of CHIKV to the infectious state of CHIKV
$ \sigma_{vD} $ Progression rate of adult mosquitoes from exposed state of DEN to the infectious state of DENV
$ \gamma_i(i=1,4) $ Progression rates of humans to active CHIKV classes
$ \gamma_j(j=2,3) $ Progression rates of humans to active DENV classes
$ \theta_1,\rho_2 $ Progression rates of adult mosquitoes to active CHIKV classes
$ \theta_2,\rho_1 $ Progression rates of adult mosquitoes to active DENV classes
$ \tau_{C} $ Recovery rate of humans from infectious state of CHIKV to the recovered state of CHIKV
$ \tau_{D} $ Recovery rate of humans from infectious state of DENV to the recovered state of DENV
$ \eta $ Modification parameters for the increase in infectiousness of dually-infected humans in comparison to mono-infected humans
$ \eta_{C},\eta_{D},\eta_{CD},\eta_{vC},\eta_{vD},\eta_{M} $ Modification parameters for the increase in infectiousness for the exposed classes in humans and mosquitoes, respectively
$ \delta_{C},\delta_{D},\delta_{CE},\delta_{DE},\delta_{CD},\delta_{CT},\delta_{DT} $ Disease-induced death rates for humans
Table 3.  Values and ranges of the parameters of the model Eqns (1)-(2)
Parameter Baseline; (Range) Unit Source(s)
[0.5ex] $ \mu_H $ $ 3.9\times10^{-5};\; (3.6, 4.0)\times10^{-5} $ $ \text{day}^{-1} $ [1,34]
$ \mu_v $ $ 0.05714;\; (0.01, 0.1) $ $ \text{day}^{-1} $ [37]
$ \mu_A $ $ 0.174;\; (0.0143, 0.33) $ $ \text{day}^{-1} $ [23,50]
$ \beta_C $ $ 0.375;\; (0.001, 0.54) $ $ \text{day}^{-1} $ [8,42]
$ \beta_D $ $ 0.75;\; (0.1, 0.95) $ $ \text{day}^{-1} $ [26]
$ \beta_v $ $ 0.375;\; (0.1, 0.5) $ $ \text{day}^{-1} $ [8,9,27,36]
$ \sigma_m $ $ 0.5;\; (0.33, 1) $ $ \text{day}^{-1} $ [7,26]
$ \sigma_H $ $ 1;\; (0.1, 10) $ $ \text{day}^{-1} $ [16]
$ \sigma_C $ $ 0.35;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [1,16]
$ \sigma_D $ $ 0.5;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \sigma_{vC} $ $ 0.25;\; (0.1, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \sigma_{vD}, \sigma_{CT}, \sigma_{DT} $ $ 0.2;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \tau_C $ 0.2(0.1429, 0.3333) $ \text{day}^{-1} $ Estimated [15]
$ \tau_D $ $ 0.25;\; (0.01, 0.3) $ $ \text{day}^{-1} $ [15]
$ \Pi_H $ $ 2.5;\; (1, 5) $ $ \text{day}^{-1} $ [16]
$ \Pi_v $ $ 5000;\; (2500, 6000) $ $ \text{day}^{-1} $ [16]
$ \gamma_1 $ $ 0.23;\; (0, 1) $ Dimensionless Assumed
$ \gamma_2 $ $ 0.25;\; (0, 1) $ Dimensionless Assumed
$ \gamma_3 $ $ 0.3;\; (0, 1) $ Dimensionless Assumed
$ \gamma_4 $ $ 0.23;\; (0, 1) $ Dimensionless Assumed
$ \delta_C $, $ \delta_D $ $ 1 \times 10^{-3};\; (0.0005, 0.0015) $ $ \text{day}^{-1} $ [16]
$ \delta_{CE} $, $ \delta_{DE} $ $ 1.5 \times 10^{-3};\; (0.00051, 0.0015) $ $ \text{day}^{-1} $ Assumed
$ \delta_{CD} $, $ \delta_{CT} $, $ \delta_{DT} $ $ 1.2 \times 10^{-3};\; (0.0005, 0.002) $ $ \text{day}^{-1} $ Assumed
$ \eta_C $, $ \eta_M $ $ 0.1;\; (0, 1) $ Dimensionless Estimated [16]
$ \eta_{CD} $, $ \eta_{vC} $, $ \eta_{vD} $ $ 0.1;\; (0, 1) $ Dimensionless Estimated [16]
$ \eta $, $ \eta_D $ $ 0.12;\; (0, 1) $ Dimensionless Assumed
$ \xi $ $ 0.01;\; (0.001, 0.021) $ Dimensionless Estimated [8]
$ \alpha_1 $ $ 0.03;\; (0.15, 0.99) $ Dimensionless Estimated
$ \alpha_2 $ $ 0.018;\; (0.10, 1) $ Dimensionless Estimated
$ \omega_1 $ $ 0.015;\; (0, 1) $ Dimensionless Assumed
$ \omega_2 $ $ 0.013;\; (0, 0.9) $ Dimensionless Assumed
$ \theta_1 $ $ 0.01;\; (0.005, 0.016) $ Dimensionless Assumed
$ \theta_2 $, $ \rho_1 $ $ 0.01;\; (0.005, 0.018) $ Dimensionless Assumed
$ \rho_2 $ $ 0.01;\; (0.0051, 0.01) $ Dimensionless Assumed
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Parameter Baseline; (Range) Unit Source(s)
[0.5ex] $ \mu_H $ $ 3.9\times10^{-5};\; (3.6, 4.0)\times10^{-5} $ $ \text{day}^{-1} $ [1,34]
$ \mu_v $ $ 0.05714;\; (0.01, 0.1) $ $ \text{day}^{-1} $ [37]
$ \mu_A $ $ 0.174;\; (0.0143, 0.33) $ $ \text{day}^{-1} $ [23,50]
$ \beta_C $ $ 0.375;\; (0.001, 0.54) $ $ \text{day}^{-1} $ [8,42]
$ \beta_D $ $ 0.75;\; (0.1, 0.95) $ $ \text{day}^{-1} $ [26]
$ \beta_v $ $ 0.375;\; (0.1, 0.5) $ $ \text{day}^{-1} $ [8,9,27,36]
$ \sigma_m $ $ 0.5;\; (0.33, 1) $ $ \text{day}^{-1} $ [7,26]
$ \sigma_H $ $ 1;\; (0.1, 10) $ $ \text{day}^{-1} $ [16]
$ \sigma_C $ $ 0.35;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [1,16]
$ \sigma_D $ $ 0.5;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \sigma_{vC} $ $ 0.25;\; (0.1, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \sigma_{vD}, \sigma_{CT}, \sigma_{DT} $ $ 0.2;\; (0, 1) $ $ \text{day}^{-1} $ Estimated [16]
$ \tau_C $ 0.2(0.1429, 0.3333) $ \text{day}^{-1} $ Estimated [15]
$ \tau_D $ $ 0.25;\; (0.01, 0.3) $ $ \text{day}^{-1} $ [15]
$ \Pi_H $ $ 2.5;\; (1, 5) $ $ \text{day}^{-1} $ [16]
$ \Pi_v $ $ 5000;\; (2500, 6000) $ $ \text{day}^{-1} $ [16]
$ \gamma_1 $ $ 0.23;\; (0, 1) $ Dimensionless Assumed
$ \gamma_2 $ $ 0.25;\; (0, 1) $ Dimensionless Assumed
$ \gamma_3 $ $ 0.3;\; (0, 1) $ Dimensionless Assumed
$ \gamma_4 $ $ 0.23;\; (0, 1) $ Dimensionless Assumed
$ \delta_C $, $ \delta_D $ $ 1 \times 10^{-3};\; (0.0005, 0.0015) $ $ \text{day}^{-1} $ [16]
$ \delta_{CE} $, $ \delta_{DE} $ $ 1.5 \times 10^{-3};\; (0.00051, 0.0015) $ $ \text{day}^{-1} $ Assumed
$ \delta_{CD} $, $ \delta_{CT} $, $ \delta_{DT} $ $ 1.2 \times 10^{-3};\; (0.0005, 0.002) $ $ \text{day}^{-1} $ Assumed
$ \eta_C $, $ \eta_M $ $ 0.1;\; (0, 1) $ Dimensionless Estimated [16]
$ \eta_{CD} $, $ \eta_{vC} $, $ \eta_{vD} $ $ 0.1;\; (0, 1) $ Dimensionless Estimated [16]
$ \eta $, $ \eta_D $ $ 0.12;\; (0, 1) $ Dimensionless Assumed
$ \xi $ $ 0.01;\; (0.001, 0.021) $ Dimensionless Estimated [8]
$ \alpha_1 $ $ 0.03;\; (0.15, 0.99) $ Dimensionless Estimated
$ \alpha_2 $ $ 0.018;\; (0.10, 1) $ Dimensionless Estimated
$ \omega_1 $ $ 0.015;\; (0, 1) $ Dimensionless Assumed
$ \omega_2 $ $ 0.013;\; (0, 0.9) $ Dimensionless Assumed
$ \theta_1 $ $ 0.01;\; (0.005, 0.016) $ Dimensionless Assumed
$ \theta_2 $, $ \rho_1 $ $ 0.01;\; (0.005, 0.018) $ Dimensionless Assumed
$ \rho_2 $ $ 0.01;\; (0.0051, 0.01) $ Dimensionless Assumed
Table A1.  Human reported CHIKV and DENV cases in India [31]
Year CHIKV DENV
$ 2010 $ $ 48176 $ $ 28292 $
$ 2011 $ $ 20402 $ $ 18860 $
$ 2012 $ $ 15977 $ $ 50222 $
$ 2013 $ $ 18840 $ $ 75808 $
$ 2014 $ $ 16049 $ $ 40571 $
$ 2015 $ $ 27553 $ $ 99913 $
$ 2016 $ $ 64057 $ $ 129166 $
$ 2017 $ $ 62268 $ $ 157220 $
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Year CHIKV DENV
$ 2010 $ $ 48176 $ $ 28292 $
$ 2011 $ $ 20402 $ $ 18860 $
$ 2012 $ $ 15977 $ $ 50222 $
$ 2013 $ $ 18840 $ $ 75808 $
$ 2014 $ $ 16049 $ $ 40571 $
$ 2015 $ $ 27553 $ $ 99913 $
$ 2016 $ $ 64057 $ $ 129166 $
$ 2017 $ $ 62268 $ $ 157220 $
Table A2.  Human reported CHIKV-DENV co-infection cases in India [13]
Year No. of cases
$ 2007 $ $ 8 $
$ 2008 $ $ 1 $
$ 2009 $ $ 8 $
$ 2010 $ $ 5 $
$ 2011 $ $ 9 $
$ 2012 $ $ 1 $
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Year No. of cases
$ 2007 $ $ 8 $
$ 2008 $ $ 1 $
$ 2009 $ $ 8 $
$ 2010 $ $ 5 $
$ 2011 $ $ 9 $
$ 2012 $ $ 1 $
[1]

Zindoga Mukandavire, Abba B. Gumel, Winston Garira, Jean Michel Tchuenche. Mathematical analysis of a model for HIV-malaria co-infection. Mathematical Biosciences & Engineering, 2009, 6 (2) : 333-362. doi: 10.3934/mbe.2009.6.333
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Kazeem Oare Okosun, Robert Smith?. Optimal control analysis of malaria-schistosomiasis co-infection dynamics. Mathematical Biosciences & Engineering, 2017, 14 (2) : 377-405. doi: 10.3934/mbe.2017024
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Yijun Lou, Li Liu, Daozhou Gao. Modeling co-infection of Ixodes tick-borne pathogens. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1301-1316. doi: 10.3934/mbe.2017067
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Georgi Kapitanov. A double age-structured model of the co-infection of tuberculosis and HIV. Mathematical Biosciences & Engineering, 2015, 12 (1) : 23-40. doi: 10.3934/mbe.2015.12.23
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Jinping Fang, Guang Lin, Hui Wan. Analysis of a stage-structured dengue model. Discrete & Continuous Dynamical Systems - B, 2018, 23 (9) : 4045-4061. doi: 10.3934/dcdsb.2018125
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Jing-Jing Xiang, Juan Wang, Li-Ming Cai. Global stability of the dengue disease transmission models. Discrete & Continuous Dynamical Systems - B, 2015, 20 (7) : 2217-2232. doi: 10.3934/dcdsb.2015.20.2217
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Expeditho Mtisi, Herieth Rwezaura, Jean Michel Tchuenche. A mathematical analysis of malaria and tuberculosis co-dynamics. Discrete & Continuous Dynamical Systems - B, 2009, 12 (4) : 827-864. doi: 10.3934/dcdsb.2009.12.827
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Folashade B. Agusto. Optimal control and cost-effectiveness analysis of a three age-structured transmission dynamics of chikungunya virus. Discrete & Continuous Dynamical Systems - B, 2017, 22 (3) : 687-715. doi: 10.3934/dcdsb.2017034
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Laura Fumanelli, Pierre Magal, Dongmei Xiao, Xiao Yu. Qualitative analysis of a model for co-culture of bacteria and amoebae. Mathematical Biosciences & Engineering, 2012, 9 (2) : 259-279. doi: 10.3934/mbe.2012.9.259
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Jaroslaw Smieja, Malgorzata Kardynska, Arkadiusz Jamroz. The meaning of sensitivity functions in signaling pathways analysis. Discrete & Continuous Dynamical Systems - B, 2014, 19 (8) : 2697-2707. doi: 10.3934/dcdsb.2014.19.2697
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Kazimierz Malanowski, Helmut Maurer. Sensitivity analysis for state constrained optimal control problems. Discrete & Continuous Dynamical Systems - A, 1998, 4 (2) : 241-272. doi: 10.3934/dcds.1998.4.241
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Ruotian Gao, Wenxun Xing. Robust sensitivity analysis for linear programming with ellipsoidal perturbation. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-16. doi: 10.3934/jimo.2019041
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S.Durga Bhavani, K. Viswanath. A general approach to stability and sensitivity in dynamical systems. Discrete & Continuous Dynamical Systems - A, 1998, 4 (1) : 131-140. doi: 10.3934/dcds.1998.4.131
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Jinliang Wang, Xiu Dong. Analysis of an HIV infection model incorporating latency age and infection age. Mathematical Biosciences & Engineering, 2018, 15 (3) : 569-594. doi: 10.3934/mbe.2018026
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Yuri Nechepurenko, Michael Khristichenko, Dmitry Grebennikov, Gennady Bocharov. Bistability analysis of virus infection models with time delays. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020166
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Yves Dumont, Frederic Chiroleu. Vector control for the Chikungunya disease. Mathematical Biosciences & Engineering, 2010, 7 (2) : 313-345. doi: 10.3934/mbe.2010.7.313
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Yuming Chen, Junyuan Yang, Fengqin Zhang. The global stability of an SIRS model with infection age. Mathematical Biosciences & Engineering, 2014, 11 (3) : 449-469. doi: 10.3934/mbe.2014.11.449
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Seung-Yeal Ha, Shi Jin. Local sensitivity analysis for the Cucker-Smale model with random inputs. Kinetic & Related Models, 2018, 11 (4) : 859-889. doi: 10.3934/krm.2018034
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Lorena Bociu, Jean-Paul Zolésio. Sensitivity analysis for a free boundary fluid-elasticity interaction. Evolution Equations & Control Theory, 2013, 2 (1) : 55-79. doi: 10.3934/eect.2013.2.55
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Hamed Azizollahi, Marion Darbas, Mohamadou M. Diallo, Abdellatif El Badia, Stephanie Lohrengel. EEG in neonates: Forward modeling and sensitivity analysis with respect to variations of the conductivity. Mathematical Biosciences & Engineering, 2018, 15 (4) : 905-932. doi: 10.3934/mbe.2018041

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