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Novel fractional order SIDARTHE mathematical model of COVID-19 pandemic

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  • Higazy, M.

Abstract

Nowadays, COVID-19 has put a significant responsibility on all of us around the world from its detection to its remediation. The globe suffer from lockdown due to COVID-19 pandemic. The researchers are doing their best to discover the nature of this pandemic and try to produce the possible plans to control it. One of the most effective method to understand and control the evolution of this pandemic is to model it via an efficient mathematical model. In this paper, we propose to model COVID-19 pandemic by fractional order SIDARTHE model which did not appear in the literature before. The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. The sensitivity of the fractional order COVID-19 SIDARTHE model to the fractional order and the infection rate parameters are displayed. All studies are numerically simulated using MATLAB software via fractional order differential equation solver.

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  • Higazy, M., 2020. "Novel fractional order SIDARTHE mathematical model of COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920304057
    DOI: 10.1016/j.chaos.2020.110007
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    2. Jahanshahi, Hadi & Munoz-Pacheco, Jesus M. & Bekiros, Stelios & Alotaibi, Naif D., 2021. "A fractional-order SIRD model with time-dependent memory indexes for encompassing the multi-fractional characteristics of the COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Ullah, Mohammad Sharif & Higazy, M. & Kabir, K.M. Ariful, 2022. "Dynamic analysis of mean-field and fractional-order epidemic vaccination strategies by evolutionary game approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    4. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Ping He & Yu Gao & Longfei Guo & Tongtong Huo & Yuxin Li & Xingren Zhang & Yunfeng Li & Cheng Peng & Fanyun Meng, 2021. "Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    6. Ullah, Mohammad Sharif & Higazy, M. & Ariful Kabir, K.M., 2022. "Modeling the epidemic control measures in overcoming COVID-19 outbreaks: A fractional-order derivative approach," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    7. Sabah Bushaj & Xuecheng Yin & Arjeta Beqiri & Donald Andrews & İ. Esra Büyüktahtakın, 2023. "A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization," Annals of Operations Research, Springer, vol. 328(1), pages 245-277, September.
    8. Asamoah, Joshua Kiddy K. & Owusu, Mark A. & Jin, Zhen & Oduro, F. T. & Abidemi, Afeez & Gyasi, Esther Opoku, 2020. "Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Zhou, Jiaying & Ye, Yong & Arenas, Alex & Gómez, Sergio & Zhao, Yi, 2023. "Pattern formation and bifurcation analysis of delay induced fractional-order epidemic spreading on networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    10. Aldila, Dipo, 2020. "Analyzing the impact of the media campaign and rapid testing for COVID-19 as an optimal control problem in East Java, Indonesia," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    11. Khajji, Bouchaib & Kouidere, Abdelfatah & Elhia, Mohamed & Balatif, Omar & Rachik, Mostafa, 2021. "Fractional optimal control problem for an age-structured model of COVID-19 transmission," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    12. Li, Hang & Shen, Yongjun & Han, Yanjun & Dong, Jinlu & Li, Jian, 2023. "Determining Lyapunov exponents of fractional-order systems: A general method based on memory principle," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).

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