Research article

Sensitivity analysis of cassava mosaic disease with saturation incidence rate model

  • Received: 08 October 2022 Revised: 26 November 2022 Accepted: 13 December 2022 Published: 03 January 2023
  • MSC : 34D20, 34D23

  • Cassava mosaic disease (CMD) is caused by a virus transmitted by the whitefly. This disease can destroy cassava at any stage of its growth and it resulted in lower cassava yields. In this paper, we developed a mathematical model for the epidemic of cassava mosaic disease with a deterministic model which has saturation incidence rates. This model aims to explain the effect of vectors on cassava disease outbreaks. First, this model was analyzed using standard dynamic methods to determine the behavior of the solution. We found the existence and condition of disease-free and endemic steady state. The basic reproductive number ($ R_0 $) is obtained by using the next-generation method which $ R_0 $ helps assess the ability to spread infectious diseases. Second, the stability of the steady state was analyzed, then we obtain the condition of existence of local stability and global stability at each steady state of this model. Third, analysis of the sensitivity indices in the threshold number to determine the effect of the various parameters. Finally, the results of the theoretical model were validated by numerical simulations. It is represented by various graphs converging at a steady state and stable.

    Citation: Sireepatch Sangsawang, Usa Wannasingha Humphries, Amir Khan, Puntani Pongsumpun. Sensitivity analysis of cassava mosaic disease with saturation incidence rate model[J]. AIMS Mathematics, 2023, 8(3): 6233-6254. doi: 10.3934/math.2023315

    Related Papers:

  • Cassava mosaic disease (CMD) is caused by a virus transmitted by the whitefly. This disease can destroy cassava at any stage of its growth and it resulted in lower cassava yields. In this paper, we developed a mathematical model for the epidemic of cassava mosaic disease with a deterministic model which has saturation incidence rates. This model aims to explain the effect of vectors on cassava disease outbreaks. First, this model was analyzed using standard dynamic methods to determine the behavior of the solution. We found the existence and condition of disease-free and endemic steady state. The basic reproductive number ($ R_0 $) is obtained by using the next-generation method which $ R_0 $ helps assess the ability to spread infectious diseases. Second, the stability of the steady state was analyzed, then we obtain the condition of existence of local stability and global stability at each steady state of this model. Third, analysis of the sensitivity indices in the threshold number to determine the effect of the various parameters. Finally, the results of the theoretical model were validated by numerical simulations. It is represented by various graphs converging at a steady state and stable.



    加载中


    [1] R. H. Howeler, Sustainable soil and crop management of cassava in Asia: A reference manual, Centro Internacional de Agricultura Tropical (CIAT), Tokyo, 2001, 1–280.
    [2] I. C. Onwueme, Cassava in Asia and the Pacific, in Cassava: Biology, production and utilization, CAB International, 2001, 55–65. https://doi.org/10.1079/9780851995243.0055
    [3] FAO, Save and grow: Cassava: A guide to sustainable production intensification, Agriculture Organization of the United Nations, Rome, 2013, 1–129.
    [4] D. Fargette, M. Jeger, C. Fauquet, L. D. C. Fishpool, Analysis of temporal disease progress of African cassava mosaic virus, Phytopathology, 84 (1994), 91–98. https://doi.org/10.1094/Phyto-84-91 doi: 10.1094/Phyto-84-91
    [5] C. Fauquet, D. Fargette, African cassava mosaic virus: Etiology, epidemiology and control, Plant Dis., 74 (1990), 404–411. https://doi.org/10.1094/PD-74-0404 doi: 10.1094/PD-74-0404
    [6] H. H. Storey, R. F. W. Nichols, Studies of the mosaic diseases of cassava, Ann. Appl. Bio., 25 (1938), 790–806. https://doi.org/10.1111/j.1744-7348.1938.tb02354.x doi: 10.1111/j.1744-7348.1938.tb02354.x
    [7] H. L. Wang, X. Y. Cui, X. W. Wang, S. S. Liu, Z. H. Zhang, X. P. Zhou, First report of Sri Lankan cassava mosaic virus infecting cassava in Cambodia, Phytopathological, 100 (2016). https://doi.org/10.1094/PDIS-10-15-1228-PDN
    [8] A. C. Bellotti, Arthropod pests, in Cassava: Biology, production and utilization, CABI Publishing, 2002,209–235. https://doi.org/10.1079/9780851995243.0209
    [9] F. Zhou, H. Yao, Global dynamics of a host-vector-predator mathematical model, J. Appl. Math., 2014 (2014), 1–10. https://doi.org/10.1155/2014/245650 doi: 10.1155/2014/245650
    [10] P. Kumar, V. S. Erturk, V. Govindaraj, S. Kumar, A fractional mathematical modeling of protectant and curative fungicide application, Chaos Soliton. Fract., 8 (2022), 1–12, https://doi.org/10.1016/j.csfx.2022.100071 doi: 10.1016/j.csfx.2022.100071
    [11] M. Shahzad, A. H. Abdel-Aty, R. A. Attia, S. H. Khoshnaw, D. Aldila, M. Ali, et al., Dynamics models for identifying the key transmission parameters of the COVID-19 disease, Alex. Eng. J., 60 (2021), 757–765. https://doi.org/10.1016/j.aej.2020.10.006 doi: 10.1016/j.aej.2020.10.006
    [12] Abdullah, S. Ahmada, S. Owyedb, A. H. Abdel-Aty, E. E. Mahmoud, K. Shah, et al., Mathematical analysis of COVID-19 via new mathematical model, Chaos Soliton. Fract., 143 (2021), 1–9. https://doi.org/10.1016/j.chaos.2020.110585 doi: 10.1016/j.chaos.2020.110585
    [13] P. Kumar, V. S. Erturk, M. Vellappandi, H. Trinh, V. Govindaraj, A study on the maize streak virus epidemic model by using optimized linearization-based predictor-corrector method in Caputo sense, Chaos Soliton. Fract., 158 (2022), 198–207. https://doi.org/10.1016/j.chaos.2022.112067 doi: 10.1016/j.chaos.2022.112067
    [14] M. J. Jeger, L. V. Madden, F. Van den Bosch, The effect of transmission route on plant virus epidemic development and disease control, J. Theor. Biol., 258 (2009), 198–207. https://doi.org/10.1016/j.jtbi.2009.01.012 doi: 10.1016/j.jtbi.2009.01.012
    [15] R. Shi, H. Zhao, S. Tang, Global dynamic analysis of a vector-borne plant disease model, Adv. Differ. Equ., 59 (2014), 1–16. https://doi.org/10.1186/1687-1847-2014-59 doi: 10.1186/1687-1847-2014-59
    [16] T. Kinene, L. S. Luboobi, B. Nannyonga, G. G. A. Mwanga, A mathematical model for the dynamics and cost effectiveness of the current controls of cassava brown streak disease in Uganda, J. Math. Comput. Sci, 5 (2015), 567–600.
    [17] F. D. Magoyo, J. I. Irunde, D. Kuznetsov, Modeling the dynamics and transmission of cassava mosaic disease in Tanzania, Commun. Math. Biol. Neurosci., 2019 (2019), 1–21. https://doi.org/10.28919/cmbn/3819 doi: 10.28919/cmbn/3819
    [18] B. Erickab, M. Mayengo, Modelling the dynamics of cassava mosaic disease with non-cassava host plants, Transbound. Emerg. Dis., 33 (2022), 1–9. https://doi.org/10.1016/j.imu.2022.101086 doi: 10.1016/j.imu.2022.101086
    [19] F. Al Basir, Y. N. Kyrychko, K. B. Blyuss, S. Ray, Effects of vector maturation time on the dynamics of cassava mosaic disease, Bull. Math. Biol., 83 (2021), 83–87. https://doi.org/10.1007/s11538-021-00921-4 doi: 10.1007/s11538-021-00921-4
    [20] A. Tompros, A. D. Dean, A. Fenton, M. Q. Wilber, E. D. Carter, M. J. Gray, Frequency-dependent transmission of Batrachochytrium salamandrivorans in eastern newts, Transbound. Emerg. Dis., 69 (2022), 731–741. https://doi.org/ 10.1111/tbed.14043 doi: 10.1111/tbed.14043
    [21] L. Esteva, M. Matias, A model for vector transmitted diseases with saturation incidence, J. Biol. Syst., 9 (2001), 235–245. https://doi.org/10.1142/S0218339001000414 doi: 10.1142/S0218339001000414
    [22] V. Capasso, G. Serio, A generalization of the Kermack-McKendrick deterministic epidemic model, Math. Biosci., 42 (1978), 43–61. https://doi.org/10.1016/0025-5564(78)90006-8 doi: 10.1016/0025-5564(78)90006-8
    [23] H. McCallum, N. Barlow, J. Hone, How should pathogen transmission be modelled? Trends Ecol. Evol., 16 (2001), 295–300. https://doi.org/10.1016/s0169-5347(01)02144-9
    [24] K. S. Mathur, P. Narayan, Dynamics of an SVEIRS epidemic model with vaccination and saturated incidence rate, Int. J. Appl. Comput. Math., 4 (2018), 1–22. https://doi.org/10.1007/s40819-018-0548-0 doi: 10.1007/s40819-018-0548-0
    [25] J. Zhang, J. Jia, X. Song, Analysis of an SEIR epidemic model with saturated incidence and saturated treatment function, The Scientific World J., 2014 (2014), 1–11. https://doi.org/10.1155/2014/910421. doi: 10.1155/2014/910421
    [26] H. Laarabi, E. H. Labriji, M. Rachik, A. Kaddar, Optimal control of an epidemic model with a saturated incidence rate, Nonlinear Anal.-Model., 17 (2012), 448–459. https://doi.org/10.15388/NA.17.4.14050 doi: 10.15388/NA.17.4.14050
    [27] P. Van den Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/s0025-5564(02)00108-6 doi: 10.1016/s0025-5564(02)00108-6
    [28] A. Omame, M. E. Isah, M. Abbas, A. H. A. Aty, C. P. Onyenegecha, A fractional order model for Dual Variants of COVID-19 and HIV co-infection via Atangana-Baleanu derivative, Alex. Eng. J., 61 (2022), 9715–9731. https://doi.org/10.1016/j.aej.2022.03.013 doi: 10.1016/j.aej.2022.03.013
    [29] C. C. Chavez, Z. Feng, W. Huang, On the computation of $R_0$ and its role on global stability, mathematical approaches for emerging and re-emerging infection diseases: An introduction, 125 (2002), 229–250.
    [30] M. Y. Li, J. S. Muldowney, A geometric approach to global stability problems, SIAM J. Math. Anal., 27 (1996), 1070–1083. https://doi.org/10.1137/S0036141094266449 doi: 10.1137/S0036141094266449
    [31] J. Holt, M. J. Jeger, J. M. Thresh, G. W. Otim-Nape, An epidemiological model incorporating vector population dynamics applied to African cassava mosaic virus disease, J. Appl. Ecol., 34 (1997), 793–806. https://doi.org/10.2307/2404924 doi: 10.2307/2404924
    [32] I. R. Stella, M. Ghosh, Modeling and analysis of plant disease with delay and logistic growth of insect vector, Commun. Math. Biol. Neurosci., 19 (2018), 1–18. https://doi.org/10.28919/cmbn/3751 doi: 10.28919/cmbn/3751
    [33] H. S. Rodrigues, M. T. T. Monteiro, D. F. Torres, Seasonality effects on dengue: Basic reproduction number, sensitivity analysis and optimal control, Math. Meth. Appl. Sci., 39 (2014), 4671–4679. https://doi.org/10.1002/mma.3319 doi: 10.1002/mma.3319
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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

Metrics

Article views(1558) PDF downloads(115) Cited by(0)

Article outline

Figures and Tables

Figures(3)  /  Tables(3)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog