Research article Special Issues

Construction and numerical analysis of a fuzzy non-standard computational method for the solution of an SEIQR model of COVID-19 dynamics

  • Received: 17 December 2021 Revised: 15 February 2022 Accepted: 17 February 2022 Published: 28 February 2022
  • MSC : 65L12, 34A07

  • This current work presents an SEIQR model with fuzzy parameters. The use of fuzzy theory helps us to solve the problems of quantifying uncertainty in the mathematical modeling of diseases. The fuzzy reproduction number and fuzzy equilibrium points have been derived focusing on a model in a specific group of people having a triangular membership function. Moreover, a fuzzy non-standard finite difference (FNSFD) method for the model is developed. The stability of the proposed method is discussed in a fuzzy sense. A numerical verification for the proposed model is presented. The developed FNSFD scheme is a reliable method and preserves all the essential features of a continuous dynamical system.

    Citation: Fazal Dayan, Nauman Ahmed, Muhammad Rafiq, Ali Akgül, Ali Raza, Muhammad Ozair Ahmad, Fahd Jarad. Construction and numerical analysis of a fuzzy non-standard computational method for the solution of an SEIQR model of COVID-19 dynamics[J]. AIMS Mathematics, 2022, 7(5): 8449-8470. doi: 10.3934/math.2022471

    Related Papers:

  • This current work presents an SEIQR model with fuzzy parameters. The use of fuzzy theory helps us to solve the problems of quantifying uncertainty in the mathematical modeling of diseases. The fuzzy reproduction number and fuzzy equilibrium points have been derived focusing on a model in a specific group of people having a triangular membership function. Moreover, a fuzzy non-standard finite difference (FNSFD) method for the model is developed. The stability of the proposed method is discussed in a fuzzy sense. A numerical verification for the proposed model is presented. The developed FNSFD scheme is a reliable method and preserves all the essential features of a continuous dynamical system.



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    [1] Word Health Organization, Coronavirus disease (COVID-19) pandemic, 2020. Available from: https://www.who.int/.
    [2] Y. G. Sánchez, Z. Sabir, J. L. Guirao, Design of a nonlinear SITR fractal model based on the dynamics of a novel coronavirus (COVID-19), Fractals, 28 (2020), 2040026. https://doi.org/10.1142/S0218348X20400265 doi: 10.1142/S0218348X20400265
    [3] COVID-19 coronavirus updates, Available from: https://www.worldometers.info/coronavirus/.
    [4] S. Ullah, M. A. Khan, Modeling the impact of nonpharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study, Chaos Soliton. Fract., 139 (2020), 110075. https://doi.org/10.1016/j.chaos.2020.110075 doi: 10.1016/j.chaos.2020.110075
    [5] M. Naveed, M. Rafiq, A. Raza, N. Ahmed, I. Khan, K. S. Nisar, et al., Mathematical analysis of novel coronavirus (2019-nCov) delay pandemic model, CMC-Comput. Mater. Con., 64 (2020), 1401–1414. https://doi.org/10.32604/cmc.2020.011314 doi: 10.32604/cmc.2020.011314
    [6] W. Shatanawi, A. Raza, M. S. Arif, K. Abodayeh, M. Rafiq, M. Bibi, An effective numerical method for the solution of a stochastic coronavirus (2019-nCovid) pandemic model, CMC-Comput. Mater. Con., 66 (2021), 1121–137. https://doi.org/10.32604/cmc.2020.012070 doi: 10.32604/cmc.2020.012070
    [7] N. Shahid, D. Baleanu, N. Ahmed, T. S. Shaikh, A. Raza, M. S. Iqbal, et al., Optimality of solution with numerical investigation for coronavirus epidemic model, CMC-Comput. Mater. Con., 67 (2021), 1713–1728. https://doi.org/10.32604/cmc.2021.014191 doi: 10.32604/cmc.2021.014191
    [8] M. Naveed, D. Baleanu, M. Rafiq, A. Raza, A. H. Soori, N. Ahmed, Dynamical behavior and sensitivity analysis of a delayed coronavirus epidemic model, CMC-Comput. Mater. Con., 65 (2020), 225–241. https://doi.org/10.32604/cmc.2020.011534 doi: 10.32604/cmc.2020.011534
    [9] M. A. Aba-Oud, A. Ali, H. Alrabaiah, S. Ullah, M. A. Khan, S. Islam, A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load, Adv. Differ. Equ., 106 (2021), 1–19. https://doi.org/10.1186/s13662-021-03265-4 doi: 10.1186/s13662-021-03265-4
    [10] W. Gao, P. Veeresha, D. G. Prakasha, H. M. Baskonus, Novel dynamic structures of 2019-nCOV with nonlocal operator via powerful computational technique, Biology, 9 (2020), 1–19. https://doi.org/10.3390/biology9050107 doi: 10.3390/biology9050107
    [11] M. A. Khan, A. Atangana, Modeling the dynamics of novel coronavirus (2019-nCov) with fractional derivative, Alexandria Eng. J., 59 (2020), 2379–2389. https://doi.org/10.1016/j.aej.2020.02.033 doi: 10.1016/j.aej.2020.02.033
    [12] M. Rafiq, J. Ali, M.B. Riaz, J. Awrejcewicz, Numerical analysis of a bi-modal COVID-19 SITR model, Alexandria Eng. J., 61 (2022), 227–235. https://doi.org/10.1016/j.aej.2021.04.102 doi: 10.1016/j.aej.2021.04.102
    [13] B. Mishra, A. Prajapati, Spread of malicious objects in computer network: A fuzzy approach, Appl. Appl. Math., 8 (2013), 684–700.
    [14] L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353.
    [15] L. C. Barros, M. B. Ferreira Leite, R. C. Bassanezi, The SI epidemiological models with a fuzzy transmission parameter, Comput. Math. Appl., 45 (2003), 1619–1628. https://doi.org/10.1016/S0898-1221(03)00141-X doi: 10.1016/S0898-1221(03)00141-X
    [16] P. K. Mondal, S. Jana, P. Haldar, T. K. Kar, Dynamical behavior of an epidemic model in a fuzzy transmission, Int. J. Uncertain. Fuzz., 23 (2015), 651–665. https://doi.org/10.1142/S0218488515500282 doi: 10.1142/S0218488515500282
    [17] R. Verma; S. P. Tiwari, U. Ranjit, Dynamical behaviors of fuzzy SIR epidemic model, Adv. Fuzzy Logic Technol., 2017,482–492. https://doi.org/10.1007/978-3-319-66827-7_45 doi: 10.1007/978-3-319-66827-7_45
    [18] N. R. S. Ortega, P.C. Sallum, E. Massad, Fuzzy dynamical systems in epidemic modeling, Kybernetes. 29 (2000), 201–218. https://doi.org/10.1108/03684920010312768 doi: 10.1108/03684920010312768
    [19] R. Verma, S. P. Tiwari, R. K. Upadhyay, Transmission dynamics of epidemic spread and outbreak of Ebola in West Africa: Fuzzy modeling and simulation, J. Appl. Math.Comput., 60 (2019), 637–671. https://doi.org/10.1007/s12190-018-01231-0 doi: 10.1007/s12190-018-01231-0
    [20] A. Das, M. Pal, A mathematical study of an imprecise SIR epidemic model with treatment control, J. Appl. Math.Comput., 56 (2018), 477–500. https://doi.org/10.1007/s12190-017-1083-6 doi: 10.1007/s12190-017-1083-6
    [21] D. Sadhukhan, L. N. Sahoo, B, Mondal, M. Maitri, Food chain model with optimal harvesting in fuzzy environment, J. Appl. Math.Comput., 34 (2010), 1–18. https://doi.org/10.1007/s12190-009-0301-2 doi: 10.1007/s12190-009-0301-2
    [22] R. Jafelice, L. C. Barros, R. C. Bassanezei, F. Gomide, Fuzzy modeling in symptomatic HIV virus infected population, B. Math. Biol., 66 (2004), 1597–1620. https://doi.org/10.1016/j.bulm.2004.03.002 doi: 10.1016/j.bulm.2004.03.002
    [23] A. E. Allaoui, S. Melliani, L. S. Chadli, A mathematical fuzzy model to giving up smoking, 2020 IEEE 6th ICOA, 2020, 1–6. https://doi.org/10.1109/ICOA49421.2020.9094470 doi: 10.1109/ICOA49421.2020.9094470
    [24] J. B. Diniz, F. R. Cordeiro, Automatic segmentation of melanoma in dermoscopy images using fuzzy numbers, 2017 IEEE 30th CBMS, 2017,150–155. https://doi.org/10.1109/CBMS.2017.39 doi: 10.1109/CBMS.2017.39
    [25] I. A. G. Boaventura, A. Gonzaga, Border detection in digital images: An approach by fuzzy numbers, ISDA, 2007,341–346. https://doi.org/10.1109/ISDA.2007.38 doi: 10.1109/ISDA.2007.38
    [26] A. Alamin, S. P. Mondal, S. Alam, A. Goswami, Solution and stability analysis of non-homogeneous difference equation followed by real life application in fuzzy environment, Sadhana, 45 (2020), 1–20. https://doi.org/10.1007/s12046-020-01422-1 doi: 10.1007/s12046-020-01422-1
    [27] B. Mishra, A. Prajapati, Spread of malicious objects in computer network: A fuzzy approach, Appl. Appl. Math., 8 (2013), 684–700.
    [28] N. Lefevr, A. Kanavos, V. C. Gerogiannis, L. Iliadis, P. Pintelas, Employing fuzzy logic to analyze the structure of complex biological and epidemic spreading models, Mathematics, 9 (2021). https://doi.org/10.3390/math9090977 doi: 10.3390/math9090977
    [29] P. Panja, S. K. Mondal, J. Chattopadhyay, Dynamical study in fuzzy threshold dynamics of a cholera epidemic model, Fuzzy Inf. Eng., 9 (2017), 381–401. https://doi.org/10.1016/j.fiae.2017.10.001 doi: 10.1016/j.fiae.2017.10.001
    [30] D. N. Prata, W. Rodrigues, P. H. Bermejo, Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil, Sci. Total Environ., 729 (2020), 138862. https://doi.org/10.1016/j.scitotenv.2020.138862 doi: 10.1016/j.scitotenv.2020.138862
    [31] M. Irfan, M. Ikram, M. Ahmad, H. Wu, Y. Hao, Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces, Environ. Sci. Pollut. Res., 28 (2021), 59705–59719. https://doi.org/10.1007/s11356-021-14875-6 doi: 10.1007/s11356-021-14875-6
    [32] R. E. Mickens, Advances in applications of non-standard finite difference schemes, World Scientific Publishing Company, Singapore, 2005. https://doi.org/10.1142/5884
    [33] L. C Barros, R. C. Bassanezi, W. A. Lodwick, A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics: Theory and applications, Springer, Berlin, Heidelberg, 2017. https://doi.org/10.1007/978-3-662-53324-6
    [34] B. Liu, Y. K. Liu, Expected value of fuzzy variable and fuzzy expected value models, IEEE T. Fuzzy Syst., 10 (2002), 445–450. https://doi.org/10.1109/TFUZZ.2002.800692 doi: 10.1109/TFUZZ.2002.800692
    [35] Y. Mangongo, J. Bukweli, J. Kampempe, Fuzzy global stability analysis of the dynamics of malaria with fuzzy transmission and recovery rates, American J. Oper. Res., 11 (2021), 257–282. https://doi.org/10.4236/ajor.2021.116017 doi: 10.4236/ajor.2021.116017
    [36] T. Hussain, M. Ozair, F. Ali, S. Rehman, T. A. Assiri, E. E. Mahmoud, Sensitivity analysis and optimal control of COVID-19 dynamics based on SEIQR model, Results Phys., 22 (2021). https://doi.org/10.1016/j.rinp.2021.103956 doi: 10.1016/j.rinp.2021.103956
    [37] P. V. 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
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