Research article Special Issues

A computational study of a stochastic fractal-fractional hepatitis B virus infection incorporating delayed immune reactions via the exponential decay

  • Received: 20 July 2022 Revised: 31 July 2022 Accepted: 02 August 2022 Published: 05 September 2022
  • Recently, researchers have become interested in modelling, monitoring, and treatment of hepatitis B virus infection. Understanding the various connections between pathogens, immune systems, and general liver function is crucial. In this study, we propose a higher-order stochastically modified delay differential model for the evolution of hepatitis B virus transmission involving defensive cells. Taking into account environmental stimuli and ambiguities, we presented numerical solutions of the fractal-fractional hepatitis B virus model based on the exponential decay kernel that reviewed the hepatitis B virus immune system involving cytotoxic T lymphocyte immunological mechanisms. Furthermore, qualitative aspects of the system are analyzed such as the existence-uniqueness of the non-negative solution, where the infection endures stochastically as a result of the solution evolving within the predetermined system's equilibrium state. In certain settings, infection-free can be determined, where the illness settles down tremendously with unit probability. To predict the viability of the fractal-fractional derivative outcomes, a novel numerical approach is used, resulting in several remarkable modelling results, including a change in fractional-order $ \delta $ with constant fractal-dimension $ \varpi $, $ \delta $ with changing $ \varpi $, and $ \delta $ with changing both $ \delta $ and $ \varpi $. White noise concentration has a significant impact on how bacterial infections are treated.

    Citation: Maysaa Al Qurashi, Saima Rashid, Fahd Jarad. A computational study of a stochastic fractal-fractional hepatitis B virus infection incorporating delayed immune reactions via the exponential decay[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12950-12980. doi: 10.3934/mbe.2022605

    Related Papers:

  • Recently, researchers have become interested in modelling, monitoring, and treatment of hepatitis B virus infection. Understanding the various connections between pathogens, immune systems, and general liver function is crucial. In this study, we propose a higher-order stochastically modified delay differential model for the evolution of hepatitis B virus transmission involving defensive cells. Taking into account environmental stimuli and ambiguities, we presented numerical solutions of the fractal-fractional hepatitis B virus model based on the exponential decay kernel that reviewed the hepatitis B virus immune system involving cytotoxic T lymphocyte immunological mechanisms. Furthermore, qualitative aspects of the system are analyzed such as the existence-uniqueness of the non-negative solution, where the infection endures stochastically as a result of the solution evolving within the predetermined system's equilibrium state. In certain settings, infection-free can be determined, where the illness settles down tremendously with unit probability. To predict the viability of the fractal-fractional derivative outcomes, a novel numerical approach is used, resulting in several remarkable modelling results, including a change in fractional-order $ \delta $ with constant fractal-dimension $ \varpi $, $ \delta $ with changing $ \varpi $, and $ \delta $ with changing both $ \delta $ and $ \varpi $. White noise concentration has a significant impact on how bacterial infections are treated.



    加载中


    [1] S. M. Ciupe, R. M. Ribeiro, P. W. Nelson, A. S. Perelson, Modeling the mechanisms of acute hepatitis B virus infection, J. Theor. Biol., 247 (2007), 23–35. https://doi.org/10.1016/j.jtbi.2007.02.017 doi: 10.1016/j.jtbi.2007.02.017
    [2] R. M. Ribeiro, A. Lo, A. S. Perelson, Dynamics of hepatitis B virus infection, Microb. Infect., 4 (2002), 829–835. https://doi.org/10.1016/S1286-4579(02)01603-9 doi: 10.1016/S1286-4579(02)01603-9
    [3] D. H. Kim, H. S. Kang, K.-H. Kim, Roles of hepatocyte nuclear factors in hepatitis B virus infection, World J Gastroenterol., 22 (2016), 7017–7029. https://doi.org/10.3748/wjg.v22.i31.7017 doi: 10.3748/wjg.v22.i31.7017
    [4] I. S. Oh, S. H. Park, Immune-mediated liver injury in hepatitis B virus infection, Immun. Netw, 15 (2015), 191. https://doi.org/10.4110/in.2015.15.4.191 doi: 10.4110/in.2015.15.4.191
    [5] C. A. Janeway, J. P. Travers, M. Walport, M. J. Sholmchik, Immunobiology: The Immune System in Health and Disease 5th edition, New York, Garland Science, 2001.
    [6] R. Kapoor, S. Kottilil, Strategies to eliminate HBV infection, Future Virol., 9 (2014). https://doi.org/10.2217/fvl.14.36 doi: 10.2217/fvl.14.36
    [7] K. Hattaf, N. Yousfi, A generalized HBV model with diffusion and two delays, Comput. Math. Appl. 69 (2015), 31–40. https://doi.org/10.1016/j.camwa.2014.11.010 doi: 10.1016/j.camwa.2014.11.010
    [8] K. Manna, S. P. Chakrabarty, Global stability of one and two discrete delay models for chronic hepatitis B infection with HBV DNA-containing capsids, Comput. Appl. Math, 36 (2017), 525–536. https://doi.org/10.1007/s40314-015-0242-3 doi: 10.1007/s40314-015-0242-3
    [9] T. Luzyanina, G. Bocharov, Stochastic modeling of the impact of random forcing on persistent hepatitis B virus infection, Math. Comput. Simul., 96 (2014), 54–65. https://doi.org/10.3934/mbe.2021034 doi: 10.3934/mbe.2021034
    [10] X. Wang, Y. Tan, Y. Cai, K. Wang, W. Wang, Dynamics of a stochastic HBV infection model with cell-to-cell transmission and immune response, Math. Biosci. Eng., 18 (2021), 616–642. https://doi.org/10.3934/mbe.2021034 doi: 10.3934/mbe.2021034
    [11] C. Ji, The stationary distribution of hepatitis B virus with stochastic perturbation, Appl. Math. Lett., 100 (2020), 106017. https://doi.org/10.1016/j.aml.2019.106017 doi: 10.1016/j.aml.2019.106017
    [12] D. Kiouach, Y. Sabbar, Ergodic stationary distribution of a stochastic hepatitis B epidemic model with interval-valued parameters and compensated poisson process, Comput. Math. Meth. Med., 2020 (2020). https://doi.org/10.1155/2020/9676501 doi: 10.1155/2020/9676501
    [13] H. Hui, L. F. Nie, Analysis of a stochastic HBV infection model with nonlinear incidence rate, J. Bio. Syst., 27 (2019), 399–421. https://doi.org/10.1142/S0218339019500177 doi: 10.1142/S0218339019500177
    [14] C. Ji, The stationary distribution of hepatitis B virus with stochastic perturbation, Appl. Math. Lett., 100 (2020), 106017. https://doi.org/10.1016/j.aml.2019.106017 doi: 10.1016/j.aml.2019.106017
    [15] Y. Wang, K. Qi, D. Jiang, An HIV latent infection model with cell-to-cell transmission and stochastic perturbation, Chaos Soliton. Fract., 151 (2021), 111215. https://doi.org/10.1016/j.chaos.2021.111215 doi: 10.1016/j.chaos.2021.111215
    [16] A. Din, Y. Li, A. Yusuf, Delayed hepatitis B epidemic model with stochastic analysis, Chaos Soliton. Fract., 146 (2021), 110839. https://doi.org/10.1016/j.chaos.2021.110839 doi: 10.1016/j.chaos.2021.110839
    [17] J.Sun, M. Gao, D. Jiang, Threshold dynamics of a Non-linear stochastic viral model with Time Delay and CTL responsiveness, Life, 11 (2021), 766. https://doi.org/10.3390/life11080766 doi: 10.3390/life11080766
    [18] F. A. Rihan, H. J. Alsakaji, Analysis of a stochastic HBV infection model with delayed immune response, Math. Biosci. Eng, 18 (2021), 5194–5220. https://doi.org/10.3934/mbe.2021264 doi: 10.3934/mbe.2021264
    [19] T.-H. Zhao, O. Castillo, H. Jahanshahi, A. Yusuf, M. O. Alassafi, F. E. Alsaadi, Y.-M. Chu, A fuzzy-based strategy to suppress the novel coronavirus (2019-NCOV) massive outbreak, Appl. Comput. Math, 20 (2021), 160–176.
    [20] S.-W Yao, M. Farman, M. Amin, M. Inc, A. Akgül, A. Ahmad, Fractional order COVID-19 model with transmission rout infected through environment, AIMS Math., 7 (2022), 5156–5174. https://doi.org/10.3934/math.2022288 doi: 10.3934/math.2022288
    [21] Z. Ul. A. Zafar, H. Rezazadeh, M. Inc, K. S. Nisar, T. A. Sulaiman, et al., Fractional order heroin epidemic dynamics, Alexandria Eng. J., 60 (2021), 5157–5165. https://doi.org/10.1016/j.aej.2021.04.039 doi: 10.1016/j.aej.2021.04.039
    [22] I. Podlubny, Fractional differential equations, San Diego: Academic Press, (1999).
    [23] T.-H. Zhao, M. Ijaz Khan, Y.-M. Chu, Artificial neural networking (ANN) analysis for heat and entropy generation in flow of non-Newtonian fluid between two rotating disks, Math. Methods Appl. Sci., (2021). https://doi.org/10.1002/mma.7310 doi: 10.1002/mma.7310
    [24] K. Karthikeyan, P. Karthikeyan, H. M. Baskonus, K. Venkatachalam, Y.-M. Chu, Almost sectorial operators on $\Psi$-Hilfer derivative fractional impulsive integro-differential equations, Math. Methods Appl. Sci, (2021). https://doi.org/10.1002/mma.7954 doi: 10.1002/mma.7954
    [25] S. Rashid, S. Sultana, Y. Karaca, A. Khalid, Y.-M. Chu, Some further extensions considering discrete proportional fractional operators, Fractals, 30 (2022), Article ID 2240026. https://doi.org/10.1142/S0218348X22400266 doi: 10.1142/S0218348X22400266
    [26] S. N. Hajiseyedazizi, M. E. Samei, J. Alzabut, Y.-M. Chu, On multi-step methods for singular fractional $q$-integro-differential equations, Open Math., 19 (2021), 1378–1405. https://doi.org/10.1515/math-2021-0093 doi: 10.1515/math-2021-0093
    [27] M. Caputo, M. Fabrizio, A new definition of fractional derivative without singular kernel. Prog. Fract. Differ. Appl., 2 (2015), 73–85.
    [28] A. Atangana, Fractal-fractional differentiation and integration: Connecting fractal calculus and fractional calculus to predict complex system, Chaos Soliton. Fract., 396 (2017), 102. https://doi.org/10.1016/j.chaos.2017.04.027 doi: 10.1016/j.chaos.2017.04.027
    [29] A. Atangana, S. Jain, A new numerical approximation of the fractal ordinary differential equation, Eur. Phys. J. Plus., 133 (2018), 37. https://doi.org/10.1140/epjp/i2018-11895-1 doi: 10.1140/epjp/i2018-11895-1
    [30] F. Jin, Z.-S. Qian, Y.-M. Chu, M. ur Rahman, On nonlinear evolution model for drinking behavior under Caputo-Fabrizio derivative, J. Appl. Anal. Comput., 12 (2022), 790–806. https://doi.org/10.11948/20210357 doi: 10.11948/20210357
    [31] S. Rashid, E. I. Abouelmagd, A. Khalid, F. B. Farooq, Y.-M. Chu, Some recent developments on dynamical $\hbar$-discrete fractional type inequalities in the frame of nonsingular and nonlocal kernels, Fractals, 30 (2022), Article ID 2240110. https://doi.org/10.1142/S0218348X22401107 doi: 10.1142/S0218348X22401107
    [32] F.-Z. Wang, M. N. Khan, I. Ahmad, H. Ahmad, H. Abu-Zinadah, Y.-M. Chu, Numerical solution of traveling waves in chemical kinetics: time-fractional fishers equations, Fractals, 30 (2022), Article ID 2240051. https://doi.org/10.1142/S0218348X22400515 doi: 10.1142/S0218348X22400515
    [33] S. Rashid, R. Ashraf, F. Jarad, Strong interaction of Jafari decomposition method with nonlinear fractional-order partial differential equations arising in plasma via the singular and nonsingular kernels, AIMS Math., 7 (2022), 7936–7963. https://doi.org/10.3934/math.2022444 doi: 10.3934/math.2022444
    [34] S. Rashid, F. Jarad, A. G. Ahmad, K. M. Abualnaja, New numerical dynamics of the heroin epidemic model using a fractional derivative with Mittag-Leffler kernel and consequences for control mechanisms, Results Phy., 35 (2022). https://doi.org/10.1016/j.rinp.2022.105304 doi: 10.1016/j.rinp.2022.105304
    [35] S. Rashid, E. I. Abouelmagd, S. Sultana, Y.-M. Chu, New developments in weighted $n$-fold type inequalities via discrete generalized ${\rm{\hat h}}$-proportional fractional operators, Fractals, 30 (2022), Article ID 2240056. https://doi.org/10.1142/S0218348X22400564 doi: 10.1142/S0218348X22400564
    [36] S. A. Iqbal, M. G. Hafez, Y.-M. Chu, C. Park, Dynamical Analysis of nonautonomous RLC circuit with the absence and presence of Atangana-Baleanu fractional derivative, J. Appl. Anal. Comput., 12 (2022), 770–789. https://doi.org/10.11948/20210324 doi: 10.11948/20210324
    [37] A. N. Shiryaev, Essentials of Stochastic Finance, Facts, Models and Theory. World Scientific, Singapore, (1999). https://doi.org/10.1142/3907
    [38] K. X. Li, Stochastic delay fractional evolution equations driven by fractional Brownian motion, Math. Methods Appl. Sci., 38 (2015), 1582–1591. https://doi.org/10.1002/mma.3169 doi: 10.1002/mma.3169
    [39] M. Kerboua, A. Debbouche, D. Baleanu, Approximate controllability of Sobolev-type nonlocal fractional stochastic dynamic systems in Hilbert spaces, Abstr. Appl. Anal., 2013 (2013), Article ID 262191. https://doi.org/10.1155/2013/262191 doi: 10.1155/2013/262191
    [40] B. Pei, Y. Xu, On the non-Lipschitz stochastic differntial equations driven by fractional Brownian motion, Adv. Differ. Equ., 2016 (2016), 194. https://doi.org/10.1186/s13662-016-0916-1 doi: 10.1186/s13662-016-0916-1
    [41] A. Atangana, S. I. Araz, Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe, Adv. Differ. Eqs., 2021 (2021), 1–107. https://doi.org/10.1186/s13662-021-03213-2 doi: 10.1186/s13662-021-03213-2
    [42] B. S. T. Alkahtani, I. Koca, Fractional stochastic SIR model, Results Phy., 24 (2021), 104124. https://doi.org/10.1016/j.rinp.2021.104124 doi: 10.1016/j.rinp.2021.104124
    [43] S. Rashid, M. K. Iqbal, A. M. Alshehri, R. Ahraf, F. Jarad, A comprehensive analysis of the stochastic fractal-fractional tuberculosis model via Mittag-Leffler kernel and white noise, Results Phy., 39 (2022), 105764. https://doi.org/10.1016/j.rinp.2022.105764 doi: 10.1016/j.rinp.2022.105764
    [44] X. Zhang, H. Peng, Stationary distribution of a stochastic cholera epidemic model with vaccination under regime switching, Appl. Math. Lett., 102 (2020). https://doi.org/10.1016/j.aml.2019.106095 doi: 10.1016/j.aml.2019.106095
    [45] F. A. Rihan, H. J. Alsakaji, C. Rajivganthi, Stochastic SIRC epidemic model with time-delay for COVID-19, Adv. Differ. Equ., 2020 (2020), 1–20. https://doi.org/10.1186/s13662-019-2438-0 doi: 10.1186/s13662-019-2438-0
    [46] Q. Liu, D. Jiang, Stationary distribution and extinction of a stochastic SIR model with nonlinear perturbation, Appl. Math. Lett., 73 (2017), 8–15. https://doi.org/10.1016/j.aml.2017.04.021 doi: 10.1016/j.aml.2017.04.021
    [47] O. Diekmann, J. A. P. Heesterbeek, M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface., 7 (2010), 873–885. https://doi.org/10.1098/rsif.2009.0386 doi: 10.1098/rsif.2009.0386
    [48] A. Atangana, Mathematical model of survival of fractional calculus, critics and their impact: How singular is our world? Adv. Diff. Equ., 2021 (2021), 403. https://doi.org/10.1186/s13662-021-03494-7 doi: 10.1186/s13662-021-03494-7
    [49] P. 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
    [50] P. Baldi, L. Mazliak, P. Priouret, Pierre, Martingales and Markov Chains, Chapman and Hall., ISBN 978-1-584-88329-6, (1991).
    [51] D. Wodarz, J. P. Christensen, A. R. Thomsen, The importance of lytic and nonlytic immune responses in viral infections, Trends Immunol., 23 (2002), 194–200. https://doi.org/10.1016/S1471-4906(02)02189-0 doi: 10.1016/S1471-4906(02)02189-0
    [52] B. Berrhazi, M. E. Fatini, T. G. Caraballo, R. Pettersson, A stochastic SIRI epidemic model with levy noise, Discret. Contin. Dyn. Syst. Ser. B., 23 (2018), 3645–3661.
    [53] K. Hattaf, A new generalized definition of fractional derivative with non-singular kernel, Computation, 8 (2020), 4. https://doi.org/10.3390/computation8020049 doi: 10.3390/computation8020049
    [54] J. M. Heffernan, R. J. Smith, L. M. Wahl, Perspectives on the basic reproductive ratio, J. R. Soc. Interf., 2 (2005), 281–293. https://doi.org/10.1098/rsif.2005.0042 doi: 10.1098/rsif.2005.0042
    [55] H. Dahari, A. Lo, R. M. Ribeiro, A. S. Perelson, Modeling hepatitis C virus dynamics: Liver regeneration and critical drug efficacy, J. Theor. Biol., 247 (2007), 371–381. https://doi.org/10.1016/j.jtbi.2007.03.006 doi: 10.1016/j.jtbi.2007.03.006
    [56] J. Reyes-Silveyra, A. R. Mikler, Modeling immune response and its effect on infectious disease outbreak dynamics, Theor. Biol. Med. Model., 13 (2016), 1–21. https://doi.org/10.1186/s12976-016-0033-6 doi: 10.1186/s12976-016-0033-6
    [57] D. Wodarz, Hepatitis C virus dynamics and pathology: the role of CTL and antibody responses, J. Gen. Virol., 84 (2003), 1743–1750. https://doi.org/10.1099/vir.0.19118-0 doi: 10.1099/vir.0.19118-0
  • Reader Comments
  • © 2022 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(1870) PDF downloads(79) Cited by(10)

Article outline

Figures and Tables

Figures(13)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog