Fractional Dynamics of Infectious Disease Transmission with Optimal Control

Document Type : Original Scientific Paper

Authors

1 Department of Mathematics, Payame Noor University, (PNU), Tehran, I. R. Iran

2 Department of Statistics, Payame Noor University, (PNU), Tehran, I. R. Iran

Abstract

This article investigates and studies the dynamics of infectious disease transmission using a fractional mathematical model based on Caputo fractional derivatives‎. ‎Consequently‎, ‎the population studied has been divided into four categories‎: ‎susceptible‎, ‎exposed‎, ‎infected‎, ‎and recovered. The basic reproduction rate‎, ‎existence‎, ‎and uniqueness of disease-free as well as infected steady-state‎ equilibrium points of the mathematical model have been investigated in this study‎. ‎The local and global stability of both equilibrium points has‎ been investigated and proven by Lyapunov functions‎. ‎Vaccination and drug therapy are two controllers that may be used to control the spread of diseases in society‎, ‎and the conditions for the optimal use of these two controllers have been prescribed by the principle of Pontryagin's maximum. The stated theoretical results have been investigated using numerical simulation‎. ‎The‎ numerical simulation of the fractional optimal control problem indicates that vaccination of the susceptible subjects in the community reduces‎
‎horizontal transmission while applying drug control to the infected subjects reduces vertical transmission‎. ‎Furthermore‎, ‎the simultaneous use of‎ both controllers is much more effective and leads to a rapid increase in the cured population and it prevents the disease from spreading and‎ turning into an epidemic in the community‎.

Keywords

Main Subjects


[1] F. Brauer, Mathematical epidemiology: past, present, and future, Infect. Dis. Model. 2 (2017) 113- 127, https://doi.org/10.1016/j.idm.2017.02.001.
[2] D. J. Daley and J. Gani, Epidemic modelling: an introduction, Cambridge University Press, (2001).
[3] C. Vargas-De-León, Volterra-type Lyapunov functions for fractional-order epidemic systems, Commun. Nonlinear Sci. Numer. Simul. 24 (2015) 75 - 85, https://doi.org/10.1016/j.cnsns.2014.12.013.
[4] A. Elazzouzi, A. L. Alaoui, M. Tilioua and D. F. M. Torres, Analysis of a SIRI epidemic model with distributed delay and relapse, Stat. Optim. Inf. Comput. 7 (2019) 545 - 557, https://doi.org/10.19139/soic-2310-5070-831.
[5] B. Ghanbari, H. Günerhan and H. M. Srivastava, An application of the Atangana- Baleanu fractional derivative in mathematical biology: a threespecies predator-prey model, Chaos Solit. Fractals 138 (2020) p. 109910, https://doi.org/10.1016/j.chaos.2020.109910.
[6] A. Jajarmi, A. Yusuf, D. Baleanu and M. Inc, A new fractional HRSV model and its optimal control: a non-singular operator approach, Phys. A 547 (2020) p. 123860, https://doi.org/10.1016/j.physa.2019.123860.
[7] A. Mahata, S. Paul, S. Mukherjee and B. Roy, Stability analysis and Hopf bifurcation in fractional order SEIRV epidemic model with a time delay in infected individuals, Partial Differ. Equ. Appl. Math. 5 (2022) p. 100282, https://doi.org/10.1016/j.padiff.2022.100282.
[8] K. M. Owolabi, High-dimensional spatial patterns in fractional reactiondiffusion system arising in biology, Chaos Solit. Fractals 134 (2020), p. 109723, https://doi.org/10.1016/j.chaos.2020.109723.
[9] S. Paul, A. Mahata, U. Ghosh and B. Roy, Study of SEIR epidemic model and scenario analysis of COVID-19 pandemic, Ecol. Genet. Genom. 19 (2021) p. 100087, https://doi.org/10.1016/j.egg.2021.100087.
[10] S. Paul, A. Mahata, S. Mukherjee, P. C. Mali and B. Roy, Fractional order SEIQRD epidemic model of Covid-19: a case study of Italy, PLoS One 18 (2023) p. e0278880, https://doi.org/10.1371/journal.pone.0278880
[11] S. Paul, A. Mahata, S. Mukherjee and B. Roy, Dynamics of SIQR epidemic model with fractional order derivative, Partial Differ. Equ. Appl. Math. 5 (2022) p. 100216, https://doi.org/10.1016/j.padiff.2021.100216.
[12] A. V. Kamyad, R. Akbari, A. A. Heydari and A. Heydari, Mathematical modeling of transmission dynamics and optimal control of vaccination and treatment for hepatitis B virus, Comput. Math. Methods Med. (2014) Article ID 475451, https://doi.org/10.1155/2014/475451.
[13] A. S. Ahmad, S. Owyed, A. H. Abdel-Aty, E. E. Mahmoud, K. Shah and H. Alrabaiah, Mathematical analysis of COVID-19 via new mathematical model, Chaos Solit. Fractals 143 (2021) p. 110585, https://doi.org/10.1016/j.chaos.2020.110585.
[14] R. Almeida, Analysis of a fractional SEIR model with treatment, Appl. Math. Lett. 84 (2018) 56 - 62, https://doi.org/10.1016/j.aml.2018.04.015.
[15] S. Bhattacharyya and S. Ghosh, Optimal control of vertically transmitted disease, Comput. Math. Methods Med. 11 (2010) 369 - 387, https://doi.org/10.1155/2010/520830.
[16] W. Lin, Global existence theory and chaos control of fractional differential equations, J. Math. Anal. Appl. 332 (2007) 709 - 726, https://doi.org/10.1016/j.jmaa.2006.10.040.
[17] L. Boujallal, Stability analysis of a fractional order mathematical model of Leukemia, Int. J. math. model. comput. 11 (2021) 15 - 27.
[18] P. V. D. Driessche and J. Watmough, Reproduction numbers and subthreshold endemic equilibria for compartmental systems of disease transmission, Math. Biosci. 180 (2002) 29 - 48, https://doi.org/10.1016/S0025-5564(02)00108-6.
[19] C. Connell McCluskey and P. van den Driessche, Global analysis of two tuberculosis models, J. Dynam. Differential Equations 16 (2004) 139 - 166, https://doi.org/10.1023/B:JODY.0000041283.66784.3e.
[20] J. S. Muldowney, Compound matrices and ordinary differential equations, Rocky Mountain J. Math. 20 (1990) 857 - 872, https://doi.org/10.1216/rmjm/1181073047.
[21] L. C. Cardoso, R. F. Camargo, F. L. P. Dos Santos and J. P. C. Dos Santos, Global stability analysis of a fractional differential system in hepatitis B, Chaos Solit. Fractals 143 (2021) p. 110619, https://doi.org/10.1016/j.chaos.2020.110619.
[22] S. L. Khalaf, M. S. Kadhim and A. R. Khudair, Studying of COVID-19 fractional model: stability analysis, Partial Differ. Equ. Appl. Math. 7 (2023) p. 100470, https://doi.org/10.1016/j.padiff.2022.100470.
[23] M. Y. Li and J. S. Muldowney, Global stability for the SEIR model in epidemiology, Math. Biosci. 125 (1995) 155-164, https://doi.org/10.1016/0025-5564(95)92756-5.