Research article Special Issues

Modeling the impact of public health education on tungiasis dynamics with saturated treatment: Insight through the Caputo fractional derivative

  • Received: 14 November 2022 Revised: 01 February 2023 Accepted: 07 February 2023 Published: 20 February 2023
  • Public health education is pivotal in the management and control of infectious and non-infectious diseases. This manuscript presents and analyses a nonlinear fractional model of tungiasis dynamics with the impact of public health education for the first time. The human population is split into five classes depending on their disease status. The infected population is split into two subgroups; infected but unaware and infected but aware. The model focuses on the impacts of public health education, contact and treatment contact on tungiasis transmission dynamics. Notably, public health education is important for containing as well as reducing disease outbreaks in communities. The Caputo fractional derivative is utilised in defining the model governing equations. Model equilibrium points existence and stability are investigated using simple matrix algebra. Model analysis shows that tungiasis is contained when the reproduction number is less than unity. Otherwise, if it is greater than unity, the disease persists and spread in the population. The generalised Adams-Bashforth-Moulton approach is utilised in solving the derived tungiasis model numerically. The impacts of public health education, treatment and contact rate on overall disease dynamics are discussed through numerical simulations. From the simulations, we see that for given fractional order, public health education and treatment increase the quality of life plus reduce equilibrium numbers of tungiasis-infected individuals. We observe that population classes converge quicker to their steady states when $ \alpha $ is increased. Thus, we can conclude that the derivative order $ \alpha $ captures the role of experience or knowledge that individuals have on the disease's history.

    Citation: Simphiwe M. Simelane, Phumlani G. Dlamini, Fadekemi J. Osaye, George Obaido, Blessing Ogbukiri, Kehinde Aruleba, Cadavious M. Jones, Chidozie W. Chukwu, Oluwaseun F. Egbelowo. Modeling the impact of public health education on tungiasis dynamics with saturated treatment: Insight through the Caputo fractional derivative[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 7696-7720. doi: 10.3934/mbe.2023332

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  • Public health education is pivotal in the management and control of infectious and non-infectious diseases. This manuscript presents and analyses a nonlinear fractional model of tungiasis dynamics with the impact of public health education for the first time. The human population is split into five classes depending on their disease status. The infected population is split into two subgroups; infected but unaware and infected but aware. The model focuses on the impacts of public health education, contact and treatment contact on tungiasis transmission dynamics. Notably, public health education is important for containing as well as reducing disease outbreaks in communities. The Caputo fractional derivative is utilised in defining the model governing equations. Model equilibrium points existence and stability are investigated using simple matrix algebra. Model analysis shows that tungiasis is contained when the reproduction number is less than unity. Otherwise, if it is greater than unity, the disease persists and spread in the population. The generalised Adams-Bashforth-Moulton approach is utilised in solving the derived tungiasis model numerically. The impacts of public health education, treatment and contact rate on overall disease dynamics are discussed through numerical simulations. From the simulations, we see that for given fractional order, public health education and treatment increase the quality of life plus reduce equilibrium numbers of tungiasis-infected individuals. We observe that population classes converge quicker to their steady states when $ \alpha $ is increased. Thus, we can conclude that the derivative order $ \alpha $ captures the role of experience or knowledge that individuals have on the disease's history.



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    [1] E. Iboi, A. Richardson, R. Ruffin, D. Ingram, J. Clark, J. Hawkins, et al., Impact of Public Health Education Program on the Novel Coronavirus Outbreak in the United States, Front. Public Health, 9 (2021), 630974. https://doi.org/10.3389/fpubh.2021.630974 doi: 10.3389/fpubh.2021.630974
    [2] I. Owusu-Mensah, L. Akinyemi, B. Oduro, O. S. Iyiola, A fractional order approach to modeling and simulations of the novel COVID-19, Adv. Differ. Equ., 2020 (2020), 683. https://doi.org/10.1186/s13662-020-03141-7 doi: 10.1186/s13662-020-03141-7
    [3] P. Veeresha, L. Akinyemi, K. Oluwasegun, M. Şenol, B. Oduro, Numerical surfaces of fractional Zika virus model with diffusion effect of mosquito-borne and sexually transmitted disease, Math. Meth. Appl. Sci., 45 (2022), 2994–3013. https://doi.org/10.1002/mma.7973 doi: 10.1002/mma.7973
    [4] S. J. Achar, C. Baishya, P. Veeresha, L. Akinyemi, Dynamics of Fractional Model of Biological Pest Control in Tea Plants with Beddington–DeAngelis Functional Response, Fractal and Fractional, 6 (2022), 1. https://doi.org/10.3390/fractalfract6010001 doi: 10.3390/fractalfract6010001
    [5] Q. Sun, Z. Wang, D. Zhao, C. Xia, M. Perc, Diffusion of resources and their impact on epidemic spreading in multilayer networks with simplicial complexes, Chaos Solitons Fract., 164 (2022), 112734. https://doi.org/10.1016/j.chaos.2022.112734 doi: 10.1016/j.chaos.2022.112734
    [6] S. Veraldi, M. Valsecchi, Imported tungiasis: A report of 19 cases and review of the literature, Int. J. Dermatol., 46 (2007), 1061–-1066. https://doi.org/10.1111/j.1365-4632.2007.03280.x doi: 10.1111/j.1365-4632.2007.03280.x
    [7] J. N. Mwangi, H. S. Ozwara, M. M. Gicheru, Epidemiology of tunga penetrans infestation in selected areas in Kiharu constituency, Murang'a County, Kenya, Trop. Dis. Travel Med. Vaccines, 1 (2015), 13. https://doi.org/10.1186/s40794-015-0015-4 doi: 10.1186/s40794-015-0015-4
    [8] World Health Organization, Tungiasis fact sheet [Fact Sheet]. Available from: https://www.who.int/news-room/fact-sheets/detail/tungiasis. Accessed April 10, 2022.
    [9] H. Feldmeier, J. Heukelbach, U. S. Ugbomoiko, E. Sentongo, P. Mbabazi, G. Von Samson-Himmelstjerna, et al., Tungiasis—A Neglected Disease with Many Challenges for Global Public Health, PLoS Negl. Trop. Dis., 8 (2014), e3133. https://doi.org/10.1371/journal.pntd.0003133 doi: 10.1371/journal.pntd.0003133
    [10] 66th World Health Assembly Resolution. Available from: https://apps.who.int/iris/bitstream/handle/10665/150163/A66_R12-en.pdf. Accessed April 21, 2022.
    [11] R. A. Nyang'inja, D. N. Angwenyi, C. M. Musyoka, T. O. Orwa, Mathematical modeling of the effects of public health education on tungiasis—a neglected disease with many challenges in endemic communities, Adv. Differ. Equ., 426 (2018). https://doi.org/10.1186/s13662-018-1875-5
    [12] J. Heukelbach, M. Eisele, A. Jackson, H. Feldmeier, Topical treatment of tungiasis: A randomized, controlled trial, Ann. Trop. Med. Parasitol., 97 (2003), 743–-749. https://doi.org/10.1179/000349803225002408 doi: 10.1179/000349803225002408
    [13] F. K. Mbuthia, I. Chepkwony, Mathematical modelling of tungiasis disease dynamics incorporating hygiene as a control strategy, J. Adv. Math. Comput. Sci., 33 (2019), 1–8. https://doi.org/10.9734/jamcs/2019/v33i53019 doi: 10.9734/jamcs/2019/v33i53019
    [14] J. Kahuru, L. S. Luboobi, Y. Nkansah-Gyekye, Optimal control techniques on a mathematical model for the dynamics of tungiasis in a community, Int. J. Math. Math. Sci., 2017 (2017), 4804897. https://doi.org/10.1155/2017/4804897 doi: 10.1155/2017/4804897
    [15] H. O. Nyaberi, C. M. Wachira, Mathematical model on the impact of protection against tungiasis transmission dynamics, J. Math. Comput. Sci., 10 (2020), 2808–2819. https://doi.org/10.28919/jmcs/4949 doi: 10.28919/jmcs/4949
    [16] M. Muehlen, H. Feldmeier, T. Wilcke, B. Winter, J. Heukelbach, Identifying risk factors for tungiasis and heavy infestation in a resource-poor community in northeast Brazil, Transact. Royal Soc. Trop. Med. Hyg., 100 (2006), 371–-380, https://doi.org/10.1016/j.trstmh.2005.06.033 doi: 10.1016/j.trstmh.2005.06.033
    [17] Y. Ding, H. Ye, A fractional-order differential equation model of HIV infection of CD4$^{+}$ T-cells, Math. Computer Model., 50 (2009), 386–392. https://doi.org/10.1016/j.mcm.2009.04.019 doi: 10.1016/j.mcm.2009.04.019
    [18] K. Diethelm, N. J. Ford, Analysis of fractional differential equations, J. Math. Anal. Appl., 265 (2002), 229–248. https://doi.org/10.1006/jmaa.2000.7194 doi: 10.1006/jmaa.2000.7194
    [19] E. Ahmed, A. M. A. El-Sayed, H. A. A. El-Saka, Equilibrium points, stability and numerical solutions of fractional-order predator-prey and rabies models, Math. Anal. Appl., 325 (2007), 542–553. https://doi.org/10.1016/j.jmaa.2006.01.087 doi: 10.1016/j.jmaa.2006.01.087
    [20] S. M. Simelane, P. G. Dlamini, A fractional order differential equation model for Hepatitis B virus with saturated incidence, Results Phys., 24 (2021), 104114. https://doi.org/10.1016/j.rinp.2021.104114 doi: 10.1016/j.rinp.2021.104114
    [21] E. Bonyah, M. L. Juga, C. W. Chukwu, A fractional order dengue fever model in the context of protected travelers, Alexandria Eng. J., 61 (2022), 927–936. https://doi.org/10.1016/j.aej.2021.04.070 doi: 10.1016/j.aej.2021.04.070
    [22] M. T. Hoang, O. F. Egbelowo, Dynamics of a fractional-order hepatitis B epidemic model and its solutions by nonstandard numerical schemes, in: K. Hattaf and H. Dutta (eds) Mathematical Modelling and Analysis of Infectious Diseases, Studies Syst. Decision Control, 302, Springer, Cham. https://doi.org/10.1007/978-3-030-49896-2_5
    [23] P. A. Naik, J. Zu, K. M. Owolabi, Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control, Chaos Solitons Fract., 138 (2020), 109826. https://doi.org/10.1016/j.chaos.2020.109826 doi: 10.1016/j.chaos.2020.109826
    [24] N. Ozalp, E. Demirci, A fractional order SEIR model with vertical transmission, Math. Comput. Model, 54 (2011), 1–-6. https://doi.org/10.1016/j.mcm.2010.12.051 doi: 10.1016/j.mcm.2010.12.051
    [25] K. M. Owolabi, A. Atangana, Numerical methods for fractional differentiation, Springer Nature Singapore Pty Ltd, 2019. https://doi.org/10.1007/978-981-15-0098-5
    [26] U. K. Nwajeri, A. Omame, C. P. Onyenegecha, Analysis of a fractional order model for HPV and CT co-infection, Results Phys., 28 (2021), 104643. https://doi.org/10.1016/j.rinp.2021.104643 doi: 10.1016/j.rinp.2021.104643
    [27] U. K. Nwajeri, A. B. Panle, A. Omame, M. C. Obi, C. P. Onyenegecha, On the fractional order model for HPV and Syphilis using non–singular kernel, Results Phys., 37 (2022), 105463. https://doi.org/10.1016/j.rinp.2022.105463 doi: 10.1016/j.rinp.2022.105463
    [28] J. Wang, J. Zhang, Z. Jin, Analysis of an SIR model with bilinear incidence rate, Nonlinear Anal. RWA, 11 (2010), 2390–-2402. https://doi.org/10.1016/j.nonrwa.2009.07.012 doi: 10.1016/j.nonrwa.2009.07.012
    [29] Y. Zhao, D. Jiang, The threshold of a stochastic SIRS epidemic model with saturated incidence, Appl. Math. Lett., 34 (2014), 90-–93. https://doi.org/10.1016/j.aml.2013.11.002 doi: 10.1016/j.aml.2013.11.002
    [30] Q. Yang, D. Jiang, N. Shi, C. Ji, The ergodicity and extinction of stochastically perturbed SIR and SEIR epidemic models with saturated incidence, J. Math. Anal. Appl., 388 (2012), 248-–271. https://doi.org/10.1016/j.jmaa.2011.11.072 doi: 10.1016/j.jmaa.2011.11.072
    [31] 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
    [32] T. Khan, Z. Ullah, N. Ali, G. Zaman, Modeling and control of the hepatitis B virus spreading using an epidemic model, Chaos Solitons Fract., 124 (2019), 1–9. https://doi.org/10.1016/j.chaos.2019.04.033 doi: 10.1016/j.chaos.2019.04.033
    [33] G. Zaman, Y. H. Kang, I. H. Jung, Stability and optimal vaccination of an SIR epidemic model. BioSystems, 93 (2008), 240–249. https://doi.org/10.1016/j.biosystems.2008.05.004
    [34] Z. M. Odibat, N. T. Shawagfeh, Generalized Taylor's formula, Appl. Math. Comput., 186 (2007), 286–293. https://doi.org/10.1016/j.amc.2006.07.102
    [35] W. Lin, Global existence theory and chaos control of fractional differential equations, Math. Anal. Appl., 332 (2007), 709–726. https://doi.org/10.1016/j.jmaa.2006.10.040 doi: 10.1016/j.jmaa.2006.10.040
    [36] MATLAB. 9.7.0.1190202 (R2019b), Natick, Massachusetts: The MathWorks Inc.; 2018.
    [37] Wolfram Research, Inc, Mathematica, Version 9.0, Champaign, IL (2012).
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