Abstract
This chapter is dedicated to what measures could have been taken to prevent COVID-19 from becoming a pandemic. Since COVID-19 shows the distribution according to the power law and exponential law in biological networks, fractional derivatives are useful to model the spread of the virus realistically. Therefore, a mathematical model, formerly originated for COVID-19, is firstly investigated with the Caputo derivative to represent the spread of the virus according to the power law. Then, a fractional optimal control problem (FOCP) is enhanced to prevent spread. Existence-proven controls representing strategies such as non-pharmaceutical and pharmaceutical interventions and plasma transfusion therapy aim to minimize the rate of infected individuals together with the cost required for treatment and prevention of the infection. After that, the necessary optimality conditions are revealed by Pontryagin’s Maximum Principle and solved numerically using Adam’s type predictor–corrector method (PCM) combined with the forward–backward sweep algorithm (FBSA). Finally, the optimal control design is studied in terms of the Atangana–Baleanu derivative in Caputo sense (ABC) to examine the spread of the virus according to the exponential law. All results are presented comparatively under various control strategies by using MATLAB.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
World Health Organization: Coronavirus. World Health Organization, https://www.who.int/health-topics/coronavirus. Accessed 5 Nov 2022
Zhou, P., et al.: A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579(7798), 270–273 (2020)
Li, Q., et al.: Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New Eng. J. Med. 382, 1199–1207 (2020)
Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223), 497–506 (2020)
Sharma, S., Samanta, G.P.: Analysis of a hand–foot–mouth disease model. Int. J. Biomath. 10(02), 1750016 (2017)
Bonyah, E., Khan, M.A., Okosun, K.O., Islam, S.: A theoretical model for Zika virus transmission. PloS one 12(10), e0185540 (2017)
Egonmwan, A.O., Okuonghae, D.: Analysis of a mathematical model for tuberculosis with diagnosis. J. Appl. Math. Comput. 59, 129–162 (2019)
Chen, T.M., Yin, L.A, et al.: Mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infect. Dis. Poverty 9(1), 1–8 (2020)
Hou, C., et al.: The effectiveness of quarantine of Wuhan City against the Corona Virus Disease 2019 (COVID-19): a well-mixed SEIR model analysis. J. Med. Virol. 92(7), 841–848 (2020)
Ndaïrou, F., Area, I., Nieto, J.J., Torres, D.F.: Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solit. Fractals 135, 109846 (2020)
Lemos-Paião, A.P., Silva, C.J., Torres, D.F.: A new compartmental epidemiological model for COVID-19 with a case study of Portugal. Ecol. Complex. 44, 100885 (2020)
Samui, P., Mondal, J., Khajanchi, S.: A mathematical model for COVID-19 transmission dynamics with a case study of India. Chaos Solit. Fractals 140, 110173 (2020)
López, L., Rodo, X.: A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics. Results Phys. 21, 103746 (2021)
Hamou, A.A., Rasul, R.R., Hammouch, Z., Ö zdemir, N.: Analysis and dynamics of a mathematical model to predict unreported cases of COVID-19 epidemic in Morocco. Comput. Appl. Math. 41(6), 1–33 (2022)
Khojasteh, H., Khanteymoori, A., Olyaee, M.H.: Comparing protein–protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features. Sci. Rep. 12(1), 1–11 (2022)
Eroğlu, B.B.İ., Avcı, D.: Separable solutions of Cattaneo-Hristov heat diffusion equation in a line segment: Cauchy and source problems. Alex. Eng. J. 60(2), 2347–2353 (2021)
Povstenko, Y., Avcı, D., Eroğlu, B.B.İ., Ö zdemir, N.: Control of thermal stresses in axisymmetric problems of fractional thermoelasticity for an infinite cylindrical domain. Therm. Sci. 21(1 Part A), 19–28 (2017)
Hristov, J.: Magnetic field diffusion in ferromagnetic materials: fractional calculus approaches. Int. J. Optim. Control: Theor. Appl. (IJOCTA) 12, 20–38 (2022)
Singh, J., Kumar, D., Hammouch, Z., Atangana, A.: A fractional epidemiological model for computer viruses pertaining to a new fractional derivative. Appl. Math. Comput. 316, 504–515 (2018)
Naik, P.A., Owolabi, K.M., Zu, J., Naik, M.U.D.: Modeling the transmission dynamics of COVID-19 pandemic in Caputo type fractional derivative. J. Multiscale Model. 12(03), 2150006 (2021)
Joshi, H., Jha, B.K., Yavuz, M.: Modelling and analysis of fractional-order vaccination model for control of COVID-19 outbreak using real data. Math. Biosci. Eng. 20(1), 213–240 (2023)
Lemos-Paião, A.P., Silva, C.J., Torres, D.F.: An epidemic model for cholera with optimal control treatment. J. Comput. Appl. Math. 318, 168–180 (2017)
Baba, I. A., Abdulkadir, R.A., Esmaili, P.: Analysis of tuberculosis model with saturated incidence rate and optimal control. Physica A. 540, 123237 (2020)
Zine, H., El Adraoui, A., Torres, D.F.: Mathematical analysis, forecasting and optimal control of HIV/AIDS spatiotemporal transmission with a reaction diffusion SICA model. AIMS Math. 7(9), 16519–16535 (2022)
Baleanu, D., Shekari, P., Torkzadeh, L., Ranjbar, H., Jajarmi, A., Nouri, K.: Stability analysis and system properties of Nipah virus transmission: a fractional calculus case study. Chaos Solit. Fractals 166, 112990 (2023)
Agrawal, O.P.: A general formulation and solution scheme for fractional optimal control problems. Nonlinear Dyn. 38, 323–337 (2004)
Agrawal, O.P.: A formulation and numerical scheme for fractional optimal control problems. J. Vib. Control 14(9–10), 1291–1299 (2008)
Eroğlu, B.B.İ., Yapışkan, D.: Local generalization of transversality conditions for optimal control problem. Math. Model. Nat. Phenom. 14(3), 310 (2019)
Yildiz, T.A., Jajarmi, A., Yildiz, B., Baleanu, D.: New aspects of time fractional optimal control problems within operators with nonsingular kernel. Discrete Contin. Dyn. Syst. -S. 13(3), 407–428 (2020)
Eroğlu, B.B.İ., Yapışkan, D.: Generalized conformable variational calculus and optimal control problems with variable terminal conditions. AIMS Math. 5(2), 1105–1126 (2020)
Avcı, D., Eroğlu, B.B.İ.: Optimal control of the Cattaneo–Hristov heat diffusion model. Acta Mech. 232(9), 3529–3538 (2021)
Tajadodi, H., Jafari, H., Ncube, M.N.: Genocchi polynomials as a tool for solving a class of fractional optimal control problems. Int. J. Optim. Control: Theor. Appl. 12(2), 160–168 (2022)
Sweilam, N.H., Assiri, T.A., Abou Hasan, M.M.: Optimal control problem of variable-order delay system of advertising procedure: numerical treatment. Discrete Contin. Dyn. Syst.-S. 15(5), 1247–1268 (2022)
Avcı, D., Soytürk, F.: Optimal control strategies for a computer network under virus threat. J. Comput. Appl. Math. 419, 114740 (2023)
Bonyah, E.: Fractional Optimal Control Model for Nutrients, Phytoplankton, and Zooplankton. In: Applications of Fractional Calculus to Modeling in Dynamics and Chaos, pp. 429–452. Chapman and Hall/CRC, New York (2023)
Panwar, V.S., Uduman, P.S., Gómez-Aguilar, J.F.: Mathematical modeling of coronavirus disease COVID-19 dynamics using CF and ABC non-singular fractional derivatives. Chaos Solit. Fractals 145, 110757 (2021)
Eroğlu, B.B.İ., Yapışkan, D.: Comparative analysis on fractional optimal control of an SLBS model. J. Comput. Appl. Math. 421, 114840 (2023)
Shen, Z.H., et al.: Mathematical modeling and optimal control of the COVID-19 dynamics. Results Phys. 31, 105028 (2021)
Eroğlu, B.B.İ., Yapışkan, D.: An optimal control strategy to prevent the spread of COVID-19. In: Conference Proceedings of Science Technology, vol. 5(1), pp. 182–186 (2022)
Podlubny, I.: Fractional Differential Equations. Academic Press, San Diego (1999)
Atangana, A., Baleanu, D.: New fractional derivatives with nonlocal and non-singular kernel: theory and application to heat transfer model. Therm. Sci. 20(2) 763–769 (2016)
Moriyama, M., Hugentobler, W.J., Iwasaki, A.: Seasonality of respiratory viral infections. Annu. Rev. Virol. 7(1), 83–101 (2020)
Focosi, D., Anderson, A.O., Tang, J.W., Tuccori, M.: Convalescent plasma therapy for COVID-19: state of the art. Clin. Microbiol. Rev. 33(4), e00072-20 (2020)
Ali, H.M., Pereira, F.L., Gama, S.M.: A new approach to the Pontryagin maximum principle for nonlinear fractional optimal control problems. Math. Methods Appl. Sci. 39(13), 3640–3649 (2016)
Birkhoff, G., Rota, G.C.C.: Ordinary Differential Equations. John Wiley & Sons, New York (1989)
Lukes, D.L.: Differential equations: classical to controlled. Math. Sci. Eng. 162, 335 (1982). Academic Press, New York
Diethelm, K., Ford, N.J., Freed, A.D.: A predictor-corrector approach for the numerical solution of fractional differential equations. Nonlinear Dyn. 29(1), 3–22 (2002)
Baleanu, D., Jajarmi, A., Sajjadi, S.S., Mozyrska, D.: A new fractional model and optimal control of a tumor-immune surveillance with non-singular derivative operator. Chaos 29(8), 083127 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Eroğlu, B.B.İ., Yapışkan, D. (2023). Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic. In: Hammouch, Z., Lahby, M., Baleanu, D. (eds) Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer Optimization and Its Applications, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-33183-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-33183-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33182-4
Online ISBN: 978-3-031-33183-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)