Research article Special Issues

Modeling mosquito-borne disease dynamics via stochastic differential equations and generalized tempered stable distribution

  • Received: 06 May 2024 Revised: 05 July 2024 Accepted: 15 July 2024 Published: 18 July 2024
  • MSC : 37A50

  • In this study, we introduce an enhanced stochastic model for mosquito-borne diseases that incorporates quarantine measures and employs Lévy jumps with the generalized tempered stable (GTS) distribution. Our proposed model lacks both endemic and disease-free states, rendering the conventional approach of assessing disease persistence or extinction based on asymptotic behavior inapplicable. Instead, we adopt a novel stochastic analysis approach to demonstrate the potential for disease eradication or continuation. Numerical examples validate the accuracy of our results and compare the outcomes of our model with the GTS distribution against the standard system using basic Lévy jumps. By accounting for the heavy-tailed nature of disease incidence or vector abundance, the GTS distribution enhances the precision of epidemiological models and predictions.

    Citation: Yassine Sabbar, Aeshah A. Raezah. Modeling mosquito-borne disease dynamics via stochastic differential equations and generalized tempered stable distribution[J]. AIMS Mathematics, 2024, 9(8): 22454-22485. doi: 10.3934/math.20241092

    Related Papers:

  • In this study, we introduce an enhanced stochastic model for mosquito-borne diseases that incorporates quarantine measures and employs Lévy jumps with the generalized tempered stable (GTS) distribution. Our proposed model lacks both endemic and disease-free states, rendering the conventional approach of assessing disease persistence or extinction based on asymptotic behavior inapplicable. Instead, we adopt a novel stochastic analysis approach to demonstrate the potential for disease eradication or continuation. Numerical examples validate the accuracy of our results and compare the outcomes of our model with the GTS distribution against the standard system using basic Lévy jumps. By accounting for the heavy-tailed nature of disease incidence or vector abundance, the GTS distribution enhances the precision of epidemiological models and predictions.



    加载中


    [1] J. N. Hays, Epidemics and pandemics: Their impacts on human history, Abc-clio, 2005.
    [2] W. H. Organization, Vector control for malaria and other mosquito-borne diseases: Report of a WHO study group, World Health Organization, 1995.
    [3] C. Yuan, D. Jiang, D. O'Regan, R. P. Agarwal, Stochastically asymptotically stability of the multi-group SEIR and SIR models with random perturbation, Commun. Nonlinear Sci., 17 (2012), 2501–2516. https://doi.org/10.1016/j.cnsns.2011.07.025 doi: 10.1016/j.cnsns.2011.07.025
    [4] M. Mehdaoui, A. L. Alaoui, M. Tilioua, Optimal control for a multi-group reaction-diffusion SIR model with heterogeneous incidence rates, Int. J. Dyn. Control, 11 (2023), 1310–1329. https://doi.org/10.1007/s40435-022-01030-3 doi: 10.1007/s40435-022-01030-3
    [5] A. Rehman, R. Singh, J. Singh, Mathematical analysis of multi-compartmental malaria transmission model with reinfection, Chaos Soliton. Fract., 163 (2022), 112527. https://doi.org/10.1016/j.chaos.2022.112527 doi: 10.1016/j.chaos.2022.112527
    [6] Y. Wang, J. Cao, Global dynamics of multi-group SEI animal disease models with indirect transmission, Chaos Soliton. Fract., 69 (2014), 81–89. https://doi.org/10.1016/j.chaos.2014.09.009 doi: 10.1016/j.chaos.2014.09.009
    [7] T. Kuniya, Y. Muroya, Global stability of a multi-group SIS epidemic model with varying total population size, Appl. Math. Comput., 265 (2015), 785–798. https://doi.org/10.1016/j.amc.2015.05.124 doi: 10.1016/j.amc.2015.05.124
    [8] W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics, Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115 (1927), 700–721. https://doi.org/10.1098/rspa.1927.0118 doi: 10.1098/rspa.1927.0118
    [9] W. O. Kermack, A. G. McKendrick, Contributions to the mathematical theory of epidemics. Ⅱ- The problem of endemicity, Proceedings of the Royal Society of London. Series A, containing papers of a mathematical and physical character, 138 (1932), 55–83. https://doi.org/10.1098/rspa.1932.0171 doi: 10.1098/rspa.1932.0171
    [10] F. Agusto, M. Khan, Optimal control strategies for dengue transmission in pakistan, Math. Biosci., 305 (2018), 102–121. https://doi.org/10.1016/j.mbs.2018.09.007 doi: 10.1016/j.mbs.2018.09.007
    [11] N. Chitnis, J. M. Cushing, J. Hyman, Bifurcation analysis of a mathematical model for malaria transmission, SIAM J. Appl. Math., 67 (2006), 24–45. https://doi.org/10.1137/050638941 doi: 10.1137/050638941
    [12] J. Tumwiine, J. Mugisha, L. S. Luboobi, A mathematical model for the dynamics of malaria in a human host and mosquito vector with temporary immunity, Appl. Math. Comput., 189 (2007), 1953–1965.
    [13] L. Cai, S. Guo, X. Li, M. Ghosh, Global dynamics of a dengue epidemic mathematical model, Chaos Soliton. Fract., 42 (2009), 2297–2304. https://doi.org/10.1016/j.chaos.2009.03.130 doi: 10.1016/j.chaos.2009.03.130
    [14] N. Chitnis, J. M. Hyman, J. M. Cushing, Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model, B. Math. Biol., 70 (2008), 1272–1296. https://doi.org/10.1007/s11538-008-9299-0 doi: 10.1007/s11538-008-9299-0
    [15] A. Abdelrazec, J. Bélair, C. Shan, H. Zhu, Modeling the spread and control of dengue with limited public health resources, Math. Biosci., 271 (2016), 136–145. https://doi.org/10.1016/j.mbs.2015.11.004 doi: 10.1016/j.mbs.2015.11.004
    [16] T. Bakary, S. Boureima, T. Sado, A mathematical model of malaria transmission in a periodic environment, J. Biol. Dyn., 12 (2018), 400–432. https://doi.org/10.1080/17513758.2018.1468935 doi: 10.1080/17513758.2018.1468935
    [17] L. Esteva, C. Vargas, H. M. Yang, A model for yellow fever with migration, Comput. Math. Method., 1 (2019), e1059.
    [18] X. Mao, G. Marion, E. Renshaw, Environmental brownian noise suppresses explosions in population dynamics, Stoch. Proc. Appl., 97 (2002), 95–110. https://doi.org/10.1016/S0304-4149(01)00126-0 doi: 10.1016/S0304-4149(01)00126-0
    [19] G. A. Ngwa, W. S. Shu, A mathematical model for endemic malaria with variable human and mosquito populations, Math. Comput. Model., 32 (2000), 747–763. https://doi.org/10.1016/S0895-7177(00)00169-2 doi: 10.1016/S0895-7177(00)00169-2
    [20] L. Esteva, C. Vargas, Analysis of a dengue disease transmission model, Math. Biosci., 150 (1998), 131–151. https://doi.org/10.1016/S0025-5564(98)10003-2 doi: 10.1016/S0025-5564(98)10003-2
    [21] P. J. Witbooi, G. J. Abiodun, G. J. van Schalkwyk, I. H. Ahmed, Stochastic modeling of a mosquito-borne disease, Adv. Differ. Equ., 2020 (2020), 1–15.
    [22] L. Wang, Z. Teng, C. Ji, X. Feng, K. Wang, Dynamical behaviors of a stochastic malaria model: A case study for yunnan, china, Physica A, 521 (2019), 435–454. https://doi.org/10.1016/j.physa.2018.12.030 doi: 10.1016/j.physa.2018.12.030
    [23] Q. Liu, D. Jiang, T. Hayat, A. Alsaedi, Stationary distribution and extinction of a stochastic dengue epidemic model, J. Franklin I., 355 (2018), 8891–8914. https://doi.org/10.1016/j.jfranklin.2018.10.003 doi: 10.1016/j.jfranklin.2018.10.003
    [24] W. Sun, L. Xue, X. Yan, Stability of a dengue epidemic model with independent stochastic perturbations, J. Math. Anal. Appl., 468 (2018), 998–1017. https://doi.org/10.1016/j.jmaa.2018.08.033 doi: 10.1016/j.jmaa.2018.08.033
    [25] C. Gokila, M. Sambath, The threshold for a stochastic within-host CHIKV virus model with saturated incidence rate, Int. J. Biomath., 14 (2021), 2150042. https://doi.org/10.1142/S179352452150042X doi: 10.1142/S179352452150042X
    [26] A. I. Abushouk, A. Negida, H. Ahmed, An updated review of Zika virus, J. Clin. Virol., 84 (2016), 53–58. https://doi.org/10.1080/00396338.2016.1231529 doi: 10.1080/00396338.2016.1231529
    [27] C. N. Haas, On the quarantine period for Ebola virus, PLoS currents, 6 (2014).
    [28] C. Y. Pan, W. L. Liu, M. P. Su, T. P. Chang, H. P. Ho, P. Y. Shu, et al., Epidemiological analysis of the kaohsiung city strategy for dengue fever quarantine and epidemic prevention, BMC Infect. Dis., 20 (2020), 1–9.
    [29] A. A. Conti, Quarantine through history, Int. Encycl. Public Health, 2008,454. https://doi.org/10.1016/B978-012373960-5.00380-4
    [30] A. Alkhazzan, J. Wang, Y. Nie, H. Khan, J. Alzabut, A stochastic susceptible vaccinees infected recovered epidemic model with three types of noises, Math. Method. Appl. Sci., 2024 (2024), 1.
    [31] A. Alkhazzan, J. Wang, Y. Nie, H. Khan, J. Alzabut, A novel sirs epidemic model for two diseases incorporating treatment functions, media coverage, and three types of noise, Chaos Soliton. Fract., 181 (2024), 114631.
    [32] N. Jafari, A. Shahsanai, M. Memarzadeh, A. Loghmani, Prevention of communicable diseases after disaster: A review, J. Res. Med. Sci., 16 (2011), 956.
    [33] J. Bertoin, Lévy processes, Cambridge university press, Cambridge, 121 (1996).
    [34] D. Kiouach, Y. Sabbar, Threshold analysis of the stochastic hepatitis b epidemic model with successful vaccination and levy jumps, 2019 4th World Conference on Complex Systems (WCCS), 2024 (2019), 1–6.
    [35] Y. Sabbar, M. Yavuz, F. Ozkose, Infection eradication criterion in a general epidemic model with logistic growth, quarantine strategy, media intrusion, and quadratic perturbation, Mathematics, 10 (2022), 4213. https://doi.org/10.3390/math10224213 doi: 10.3390/math10224213
    [36] Y. Sabbar, A. Din, D. Kiouach, Predicting potential scenarios for wastewater treatment under unstable physical and chemical laboratory conditions: A mathematical study, Results Phys., 39 (2022), 105717. https://doi.org/10.1016/j.rinp.2022.105717 doi: 10.1016/j.rinp.2022.105717
    [37] J. Rosinski, Tempering stable processes, Stoch. Proc. Appl., 117 (2007), 677–707. https://doi.org/10.1016/j.spa.2006.10.003
    [38] E. Jouini, J. Cvitanic, M. Musiela, Option pricing, interest rates and risk management, Purely discontinuous asset pricing processes, 2001,105–153. https://doi.org/10.1017/CBO9780511569708
    [39] S. I. Boyarchenko, S. Z. Levendorskii, Option pricing for truncated lévy processes, Int. J. Theor. Appl. Fin., 3 (2000), 549–552.
    [40] I. Koponen, Analytic approach to the problem of convergence of truncated lévy flights towards the gaussian stochastic process, Phys. Rev. E, 52 (1995), 1197–1199. https://doi.org/10.1103/PhysRevE.52.1197 doi: 10.1103/PhysRevE.52.1197
    [41] U. Kuchler, S. Tappe, Bilateral gamma distributions and processes in financial mathematics, Stoch. Proc. Appl., 118 (2008), 261–283.
    [42] 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/10.1002/1521-3870(200201)48:1<29::AID-MALQ29>3.0.CO; 2-N doi: 10.1002/1521-3870(200201)48:1<29::AID-MALQ29>3.0.CO; 2-N
    [43] P. Carr, G. H., D. B. Madan, M. Yor, The fine structure of asset returns: An empirical investigation, J. Bus., 75 (2002), 305–332. https://doi.org/10.1086/338705 doi: 10.1086/338705
    [44] X. Mao, Stochastic differential equations and applications, Elsevier, 2007. https://doi.org/10.1533/9780857099402
    [45] D. Kiouach, Y. Sabbar, S. E. A. El-idrissi, New results on the asymptotic behavior of an SIS epidemiological model with quarantine strategy, stochastic transmission, and Levy disturbance, Math. Method. Appl. Sci., 44 (2021), 13468–13492. https://doi.org/10.1002/mma.7638 doi: 10.1002/mma.7638
    [46] X. Zhang, K. Wang, Stochastic SIR model with jumps, Appl. Math. Lett., 26 (2013), 867–874. https://doi.org/10.1016/j.aml.2013.03.013 doi: 10.1016/j.aml.2013.03.013
    [47] M. Mehdaoui, A. L. Alaoui, M. Tilioua, Dynamical analysis of a stochastic non-autonomous SVIR model with multiple stages of vaccination, J. Appl. Math. Comput., 69 (2023), 2177–2206. https://doi.org/10.1007/s12190-022-01828-6 doi: 10.1007/s12190-022-01828-6
    [48] Y. Sabbar, A. A. Raezah, Threshold analysis of an algae-zooplankton model incorporating general interaction rates and nonlinear independent stochastic components, AIMS Math., 9 (2024), 18211–18235. https://doi.org/10.3934/math.2024889 doi: 10.3934/math.2024889
    [49] Y. Sabbar, Exploring threshold dynamics of a behavioral epidemic model featuring two susceptible classes and second-order jump–diffusion, Chaos Soliton. Fract., 186 (2024), 115216. https://doi.org/10.1016/j.chaos.2024.115216 doi: 10.1016/j.chaos.2024.115216
    [50] S. Marino, I. B. Hogue, C. J. Ray, D. E. Kirschner, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254 (2008), 178–196. https://doi.org/10.1016/j.jtbi.2008.04.011 doi: 10.1016/j.jtbi.2008.04.011
  • Reader Comments
  • © 2024 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(104) PDF downloads(16) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog