American Institute of Mathematical Sciences

Advanced
May  2020, 25(5): 1907-1933. 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  May 2020 Early access  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, 2020, 25 (5) : 1907-1933. doi: 10.3934/dcdsb.2020009
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

[2]

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

[3]

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

[5]

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

[6]

N. Chitnis, J. M. Cushing and J. M. Hyman, Bifurcation analysis of a mathematical model for malaria transmission, SIAM Journal on Applied Mathematics, 67 (2006), 24-45.  doi: 10.1137/050638941.  Google Scholar

[7]

H. Delatte, G. Gimonneau, A. Triboire and D. Fontenille, Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of Aedes albopictus, vector of chikungunya and dengue in the Indian Ocean, Journal of Medical Entomology, 46 (2009), 33–41, https://doi.org/10.1603/033.046.0105. Google Scholar

[8]

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

[9]

Y. Dumont, F. Chiroleu and C. Domerg, On a temporal model for the chikungunya disease: Modeling theory and numerics, Mathematical Bioscience, 213 (2008), 80-91.  doi: 10.1016/j.mbs.2008.02.008.  Google Scholar

[10]

Y. Dumont and J. M. Tchuenche, Mathematical studies on the strile insect technique for the Chikungunya disease and Aedes albopictus, Journal of Mathematical Biology, 65 (2012), 809-854.  doi: 10.1007/s00285-011-0477-6.  Google Scholar

[11]

V. Duong, L. Lambrechts, R. E. Paul, S. Ly, R. S. Lay, K. C. Long, R. Huy, A. Tarantola, T. W. Scott, A. Sakuntabhai and P. Buchy, Asymptomatic humans transmit dengue virus to mosquitoes, PNAS, 112 (2015), 14688-14693.  doi: 10.1073/pnas.1508114112.  Google Scholar

[12]

First Dengue Vaccine Approved in More than 10 Countries by Sanofi Pasteur 2019., Available from: https://www.sanofipasteur.com/en/. Google Scholar

[13]

L. Furuya-Kanamori, S. H. Lian, G. Milinovic, R. J. S. Magalhaes, A. C. A. Clements, W. B. Hu, P. Brasil, F. D. Frentiu, R. Dunning and L. Yakob, Co-distribution and co-infection of chikungunya and dengue viruses, BMC Infectious Diseases, 16 (2016), 11 pp. doi: 10.1186/s12879-016-1417-2.  Google Scholar

[14]

B. S. Gandhi, K. Kulkarni, M. Gobele, S. S. Dole, S. Kapur and P. Satpathy, Dengue and chikungunya co-infection associated with more severe clinical disease than mono-infection, International J. of Healthcare and Biomedical Research, 3 (2015), 117-123.   Google Scholar

[15]

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

[16]

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

[17]

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

[18]

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

[25]

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

[32]

S. Nimmannitya, S. B. Halstead, S. N. Cohen and M. R. Margiotta, Dengue and chikungunya virus infection in Man in Thailand 1962–1964. I. Observations on hospitalized patients with hemorrhagic fever, The American Journal of Tropical Medicine and Hygiene, 18 (1969), 954–971, https://doi.org/10.4269/ajtmh.1969.18.954. Google Scholar

[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]

K. O. Okuneye, J. X. Valesco-Hernandez and A. B. Gumel, The "unholy" chikungunya-dengue-zika trinity: A theoretical analysis, Journal of Biological Systems, 25 (2017), 545-585.  doi: 10.1142/S0218339017400046.  Google Scholar

[36]

K. Pesko, C. Westbrook, C. Mores, L. Lounibos and M. Reiskind, Effects of infectious virus dose and blood meal delivery method on susceptibility of Aedes aegypti and Aedes albopictus to chikungunya virus, Journal of Medical Entomology, 46 (2009), 395–399, https://doi.org/10.1603/033.046.0228. Google Scholar

[37]

E. Pliego Pliego, J. Velazquez-Castro and A. Fraguela Collar, Seasonality on the life cycle of Aedes aegypti mosquito and its statistical relation with dengue outbreaks, Applied Mathematical Modelling, 50 (2017), 484-496.  doi: 10.1016/j.apm.2017.06.003.  Google Scholar

[38]

S. Sang, S. Gu, P. Bi, W. Yang, Z. Yang, L. Xu and et al., Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, PLoS Neglected Tropical Diseases, 9 (2014), e0003808, https://doi.org/10.1371/journal.pntd.0003808. Google Scholar

[39]

T. Saswat, A. Kumar, S. Kumar, P. Mamidi, S. Muduli and N. K. Debata, High rates of co-infection of dengue and Chikungunya virus in Odisha and Maharashtra, India during 2013, Infectious, Genetics and Evolution, 35 (2015), 134-141.  doi: 10.1016/j.meegid.2015.08.006.  Google Scholar

[40]

The World Bank Data, Life Expectancy at Birth website 2019., Available from: https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=IN. Google Scholar

[41]

The World Bank Data, Population website 2019. Available from: https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG-IN&name_desc=true. Google Scholar

[42]

M. Turell, J. Beaman and R. Tammariello, Susceptibility of selected strains of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) to chikungunya virus, Journal of Medical Entomology, 29 (1992), 49–53, https://doi.org/10.1093/jmedent/29.1.49. Google Scholar

[43]

S. Usaini, U. T. Mustapha and S. S. Musa, Modelling scholastic underachievement as a contagious disease, Mathematical Methods in the Applied Sciences, 41 (2018), 8603-8612.  doi: 10.1002/mma.4924.  Google Scholar

[44]

P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180 (2002), 29-48.  doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[45]

K. S. Vannice, A. Durbin and J. Hombach, Status of vaccine research and development of vaccines for dengue, Vaccine, 34 (2016), 2934-2938.  doi: 10.1016/j.vaccine.2015.12.073.  Google Scholar

[46]

S. F. Wang, W. H. Wang, K. Chang, Y. H. Chen, S. P. Tseng, C. H. Yen, D.C. Wu and Y. M. Chen, Severe dengue fever outbreak in Taiwan, American Journal of Tropical Medicine and Hygiene, 94 (2016), 193-197.  doi: 10.4269/ajtmh.15-0422.  Google Scholar

[47]

World Heath Organization (WHO), the Chikungunya factsheet 2016. Available from: http://www.who.int/mediacentre/factsheets/fs327/en/. Google Scholar

[48]

World Heath Organization, Dengue fact sheet 2017. Available from: http://www.who.int/mediacentre/factsheets/fs117/en/. Google Scholar

[49]

L. Yakob and A. C. A. Clements, A Mathematical model of chikungunya dynamics and control: The major epidemic on re'union Island, PLoS ONE, 8 (2013), e57448. doi: 10.1371/journal.pone.0057448.  Google Scholar

[50]

H. M. Yang, M. L. G. Macoris, K. C. Galvani, M. T. M. Andrighetti and D. M. V. Wanderley, Assesseing the effectcts of temperature on the population of Aedes aegypti, the vector of dengue, Epidemiology and Infection, 137 (2009), 1188–1202, https://doi.org/10.1017/S0950268809002040. Google Scholar

[51]

L. Zou, J. Chen, X. M. Feng and S. G. Ruan, Analysis of a dengue model with vertical transmission and application to the 2014 dengue outbreak in Guangdong Province, China. Bulletin of Mathematical Biology, 80 (2018), 2633-2651.  doi: 10.1007/s11538-018-0480-9.  Google Scholar

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

[2]

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

[3]

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

[5]

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

[6]

N. Chitnis, J. M. Cushing and J. M. Hyman, Bifurcation analysis of a mathematical model for malaria transmission, SIAM Journal on Applied Mathematics, 67 (2006), 24-45.  doi: 10.1137/050638941.  Google Scholar

[7]

H. Delatte, G. Gimonneau, A. Triboire and D. Fontenille, Influence of temperature on immature development, survival, longevity, fecundity, and gonotrophic cycles of Aedes albopictus, vector of chikungunya and dengue in the Indian Ocean, Journal of Medical Entomology, 46 (2009), 33–41, https://doi.org/10.1603/033.046.0105. Google Scholar

[8]

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

[9]

Y. Dumont, F. Chiroleu and C. Domerg, On a temporal model for the chikungunya disease: Modeling theory and numerics, Mathematical Bioscience, 213 (2008), 80-91.  doi: 10.1016/j.mbs.2008.02.008.  Google Scholar

[10]

Y. Dumont and J. M. Tchuenche, Mathematical studies on the strile insect technique for the Chikungunya disease and Aedes albopictus, Journal of Mathematical Biology, 65 (2012), 809-854.  doi: 10.1007/s00285-011-0477-6.  Google Scholar

[11]

V. Duong, L. Lambrechts, R. E. Paul, S. Ly, R. S. Lay, K. C. Long, R. Huy, A. Tarantola, T. W. Scott, A. Sakuntabhai and P. Buchy, Asymptomatic humans transmit dengue virus to mosquitoes, PNAS, 112 (2015), 14688-14693.  doi: 10.1073/pnas.1508114112.  Google Scholar

[12]

First Dengue Vaccine Approved in More than 10 Countries by Sanofi Pasteur 2019., Available from: https://www.sanofipasteur.com/en/. Google Scholar

[13]

L. Furuya-Kanamori, S. H. Lian, G. Milinovic, R. J. S. Magalhaes, A. C. A. Clements, W. B. Hu, P. Brasil, F. D. Frentiu, R. Dunning and L. Yakob, Co-distribution and co-infection of chikungunya and dengue viruses, BMC Infectious Diseases, 16 (2016), 11 pp. doi: 10.1186/s12879-016-1417-2.  Google Scholar

[14]

B. S. Gandhi, K. Kulkarni, M. Gobele, S. S. Dole, S. Kapur and P. Satpathy, Dengue and chikungunya co-infection associated with more severe clinical disease than mono-infection, International J. of Healthcare and Biomedical Research, 3 (2015), 117-123.   Google Scholar

[15]

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

[16]

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

[17]

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

[18]

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

[25]

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

[32]

S. Nimmannitya, S. B. Halstead, S. N. Cohen and M. R. Margiotta, Dengue and chikungunya virus infection in Man in Thailand 1962–1964. I. Observations on hospitalized patients with hemorrhagic fever, The American Journal of Tropical Medicine and Hygiene, 18 (1969), 954–971, https://doi.org/10.4269/ajtmh.1969.18.954. Google Scholar

[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]

K. O. Okuneye, J. X. Valesco-Hernandez and A. B. Gumel, The "unholy" chikungunya-dengue-zika trinity: A theoretical analysis, Journal of Biological Systems, 25 (2017), 545-585.  doi: 10.1142/S0218339017400046.  Google Scholar

[36]

K. Pesko, C. Westbrook, C. Mores, L. Lounibos and M. Reiskind, Effects of infectious virus dose and blood meal delivery method on susceptibility of Aedes aegypti and Aedes albopictus to chikungunya virus, Journal of Medical Entomology, 46 (2009), 395–399, https://doi.org/10.1603/033.046.0228. Google Scholar

[37]

E. Pliego Pliego, J. Velazquez-Castro and A. Fraguela Collar, Seasonality on the life cycle of Aedes aegypti mosquito and its statistical relation with dengue outbreaks, Applied Mathematical Modelling, 50 (2017), 484-496.  doi: 10.1016/j.apm.2017.06.003.  Google Scholar

[38]

S. Sang, S. Gu, P. Bi, W. Yang, Z. Yang, L. Xu and et al., Predicting unprecedented dengue outbreak using imported cases and climatic factors in Guangzhou, PLoS Neglected Tropical Diseases, 9 (2014), e0003808, https://doi.org/10.1371/journal.pntd.0003808. Google Scholar

[39]

T. Saswat, A. Kumar, S. Kumar, P. Mamidi, S. Muduli and N. K. Debata, High rates of co-infection of dengue and Chikungunya virus in Odisha and Maharashtra, India during 2013, Infectious, Genetics and Evolution, 35 (2015), 134-141.  doi: 10.1016/j.meegid.2015.08.006.  Google Scholar

[40]

The World Bank Data, Life Expectancy at Birth website 2019., Available from: https://data.worldbank.org/indicator/SP.DYN.LE00.IN?locations=IN. Google Scholar

[41]

The World Bank Data, Population website 2019. Available from: https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NG-IN&name_desc=true. Google Scholar

[42]

M. Turell, J. Beaman and R. Tammariello, Susceptibility of selected strains of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) to chikungunya virus, Journal of Medical Entomology, 29 (1992), 49–53, https://doi.org/10.1093/jmedent/29.1.49. Google Scholar

[43]

S. Usaini, U. T. Mustapha and S. S. Musa, Modelling scholastic underachievement as a contagious disease, Mathematical Methods in the Applied Sciences, 41 (2018), 8603-8612.  doi: 10.1002/mma.4924.  Google Scholar

[44]

P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Mathematical Biosciences, 180 (2002), 29-48.  doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[45]

K. S. Vannice, A. Durbin and J. Hombach, Status of vaccine research and development of vaccines for dengue, Vaccine, 34 (2016), 2934-2938.  doi: 10.1016/j.vaccine.2015.12.073.  Google Scholar

[46]

S. F. Wang, W. H. Wang, K. Chang, Y. H. Chen, S. P. Tseng, C. H. Yen, D.C. Wu and Y. M. Chen, Severe dengue fever outbreak in Taiwan, American Journal of Tropical Medicine and Hygiene, 94 (2016), 193-197.  doi: 10.4269/ajtmh.15-0422.  Google Scholar

[47]

World Heath Organization (WHO), the Chikungunya factsheet 2016. Available from: http://www.who.int/mediacentre/factsheets/fs327/en/. Google Scholar

[48]

World Heath Organization, Dengue fact sheet 2017. Available from: http://www.who.int/mediacentre/factsheets/fs117/en/. Google Scholar

[49]

L. Yakob and A. C. A. Clements, A Mathematical model of chikungunya dynamics and control: The major epidemic on re'union Island, PLoS ONE, 8 (2013), e57448. doi: 10.1371/journal.pone.0057448.  Google Scholar

[50]

H. M. Yang, M. L. G. Macoris, K. C. Galvani, M. T. M. Andrighetti and D. M. V. Wanderley, Assesseing the effectcts of temperature on the population of Aedes aegypti, the vector of dengue, Epidemiology and Infection, 137 (2009), 1188–1202, https://doi.org/10.1017/S0950268809002040. Google Scholar

[51]

L. Zou, J. Chen, X. M. Feng and S. G. Ruan, Analysis of a dengue model with vertical transmission and application to the 2014 dengue outbreak in Guangdong Province, China. Bulletin of Mathematical Biology, 80 (2018), 2633-2651.  doi: 10.1007/s11538-018-0480-9.  Google Scholar

Table 2">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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Table 2">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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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">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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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">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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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">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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Table 3">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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Table 3">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 $
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