Hybrid control strategy for SEIARM model of COVID-19 outbreak

Delavari, Hadi; Veisi, Amir

doi:10.47974/JSMS-1484

Open Access Research Article

Hybrid control strategy for SEIARM model of COVID-19 outbreak

^*Hadi DelavariCorresponding authordelavari@hut.ac.irDepartment of Electrical EngineeringHamedan University of TechnologyHamedan, 65155, Iran0000-0002-2538-8801View full profile → , Amir VeisiAmir.veisi96@gmail.comDepartment of Electrical EngineeringBu-Ali Sina UniversityHamedan, 65178, Iran0009-0009-3770-7839View full profile →

* Corresponding author · click or hover a name for details

pp. 269–284Vol. 29Issue 3March 2026DOI: 10.47974/JSMS-1484XML

Received:: 10 Dec 2024
Published Online:: 06 Jan 2026
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1484
Pages:: 269–284

Abstract

In recent years, the global spread of COVID-19 has posed a significant challenge, impacting nearly every country. A newly identified virus variant, exhibiting significantly higher transmissibility, has recently emerged. A comprehensive understanding of the transmission dynamics and the application of focused control measures are necessary for managing and containing its spread. Non-Pharmacological Interventions (NPIs) remain a key component of the main strategies for stopping the spread. In the SEIARM model of COVID-19 transmission, this research presents a novel control strategy to limit the number of affected people. By integrating the concepts of PID and Sliding Mode Control (SMC), the suggested approach guarantees resilience to changes in model parameters brought on by the sliding mode control method. The effectiveness of this method in handling the newly mutated virus is examined, and its performance is compared against open-loop, PID, and SMC controllers.

Keywords

COVID-19 Sliding mode control PID Control Non-pharmacological interventions

Subject Classifications

34H0537N3537N25

References

[1] “https://www.who.int/emergencies/diseases/novel-coronavirus-2019.”
[2] N. Banholzer, E. Weenen, B. Kratzwald, A. Seeliger, D. Tschernutter, P. Bottrighi, A. Cenedes, J. Salles, W. Vach, S. Feuerriegel. “The estimated impact of non-pharmaceutical interventions on documented cases of COVID-19: A cross-country analysis,” MedRxiv, p. 2020.04. 16.20062141 (2020).
[3] T. Carletti, D. Fanelli, and F. Piazza, “COVID-19: The unreasonable effectiveness of simple models,” Chaos, Solitons & Fractals: X, vol. 5, p. 100034 (2020).
[4] E. Tagliazucchi, P. Balenzuela, M. Travizano, G. Mindlin, and P. D. Mininni, “Lessons from being challenged by COVID-19,” Chaos, Solitons & Fractals, vol. 137, p. 109923 (2020).
[5] B. Ivorra, M. R. Ferrández, M. Vela-Pérez, and A. M. Ramos, “Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China,” Communications in nonlinear science and numerical simulation, vol. 88, p. 105303 (2020).
[6] S. Nuñez, F. A. Inthamoussou, F. Valenciaga, H. De Battista, and F. Garelli, “Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread,” Biomedical Signal Processing and Control, vol. 67, p. 102557 (2021).
[7] M. Zamir, Z. Shah, F. Nadeem, A. Memood, H. Alrabaiah, and P. Kumam, “Non pharmaceutical interventions for optimal control of COVID-19,” Computer methods and programs in biomedicine, vol. 196, p. 105642 (2020).
[8] D. H. Morris, F. W. Rossine, J. B. Plotkin, and S. A. Levin, “Optimal, near-optimal, and robust epidemic control,” Communications Physics, vol. 4, no. 1, p. 78 (2021).
[9] S. Ullah, M. F. Khan, S. A. A. Shah, M. Farooq, M. A. Khan, and M. b. Mamat, “Optimal control analysis of vector-host model with saturated treatment,” The European Physical Journal Plus, vol. 135, no. 10, pp. 1-25 (2020).
[10] J. A. Gondim and L. Machado, “Optimal quarantine strategies for the COVID-19 pandemic in a population with a discrete age structure,” Chaos, Solitons & Fractals, vol. 140, p. 110166 (2020).
[11] F. Pazos and F. E. Felicioni, “A control approach to the Covid-19 disease using a SEIHRD dynamical model,” Medrxiv, p. 2020.05. 27.20115295 (2020).
[12] C. Tsay, F. Lejarza, M. A. Stadtherr, and M. Baldea, “Modeling, state estimation, and optimal control for the US COVID-19 outbreak,” Scientific reports, vol. 10, no. 1, p. 10711 (2020).
[13] M. M. Morato, S. B. Bastos, D. O. Cajueiro, and J. E. Normey-Rico, “An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil,” Annual reviews in control, vol. 50, pp. 417-431 (2020).
[14] T. Berger, “Feedback control of the COVID-19 pandemic with guaranteed non-exceeding ICU capacity,” Systems & Control Letters, vol. 160, p. 105111 (2022).
[15] M. T. Angulo, F. Castaños, R. Moreno-Morton, J. X. Velasco-Hernandez, and J. A. Moreno, “A simple criterion to design optimal nonpharmaceutical interventions for epidemic outbreaks (preprint),” (2020).
[16] A. Ibeas, M. De la Sen, and S. Alonso-Quesada, “Robust sliding control of SEIR epidemic models,” Mathematical Problems in Engineering, vol. 2014.
[17] Y. Xiao, X. Xu, and S. Tang, “Sliding mode control of outbreaks of emerging infectious diseases,” Bulletin of mathematical biology, vol. 74, pp. 2403-2422 (2012).
[18] A. R. Khalili, A. Heydari, and M. Zarrabi, “Analysis and control of SEIR epidemic model via sliding mode control,” Adv Model Optim, vol. 18, no. 1, pp. 153-162 (2016).
[19] G. Rohith and K. Devika, “Dynamics and control of COVID-19 pandemic with nonlinear incidence rates,” Nonlinear Dynamics, vol. 101, no. 3, pp. 2013-2026 (2020).
[20] F. Brauer, C. Castillo-Chavez, and C. Castillo-Chavez, Mathematical models in population biology and epidemiology (no. 40). Springer (2012).
[21] W. O. Kermack and A. G. McKendrick, “A contribution to the mathematical theory of epidemics,” Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, vol. 115, no. 772, pp. 700-721 (1927).
[22] R. M. Anderson and R. M. May, Infectious diseases of humans: dynamics and control. Oxford university press (1991).
[23] F. B. Hamzah, C. H. Laub, H. Nazric, D. V. Ligotd, G. Leee , Ch. L. Tan, MKBM. Shaib, U. H. B. Zaidon, A. B. Abdullah, M. H. Chung. “CoronaTracker: worldwide COVID-19 outbreak data analysis and prediction,” Bull World Health Organ, vol. 1, no. 32, pp. 1-32 (2020).
[24] S. Clifford, C. A. B. Pearson, P. Klepac, K. V. Zandvoort, B. J. Quilty, R. M. Eggo, S. Flasche. “Interventions targeting air travellers early in the pandemic may delay local outbreaks of SARS-CoV-2,” medRxiv, p. 2020.02. 12.20022426 (2020).
[25] V. Singh, R.C. Poonia, S. Kumar, P. Dass, P. Agarwal, V. Bhatnagar, L. Raja. “Prediction of COVID-19 corona virus pandemic based on time series data using Support Vector Machine,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 23, no. 8, pp. 1583-1597 (2020).
[26] V. Bhatnagar, R.C. Poonia, P. Nagar, S. Kumar, V, Singh, L. Raja, P. Dass. “Descriptive analysis of COVID-19 patients in the context of India,” Journal of Interdisciplinary Mathematics, vol. 24, no. 3, pp. 489-504 (2021).
[27] V. Goyal, M. Gupta, and V. K. Deolia, “A Chebyshev Neural Network based sliding mode controller for uncertain discrete-time-delayed nonlinear systems,” Journal of Statistics and Management Systems, vol. 22, no. 7, pp. 1251-1273 (2019).
[28] A. Veisi and H. Delavari, “Fractional-order backstepping strategy for fractional-order model of COVID-19 outbreak,” Mathematical Methods in the Applied Sciences, vol. 45, no. 7, pp. 3479-3496 (2022).
[29] A. Veisi and H. Delavari, “A novel fractional-order feedback management of COVID-19 prevalence,” Journal of Statistics and Management Systems, vol. 25, no. 6, pp. 1345-1359 (2022).
[30] A. Veisi and H. Delavari, “Analysis of fractional order SEIR model for Covid 19 and investigation of its spread management with a novel adaptive fractional order nonlinear controller,” Iranian Journal of Biomedical Engineering, vol. 15, no. 2, pp. 121-130 (2021).
[31] H. Delavari and A. Veisi, “Fuzzy fractional-order sliding mode control of COVID-19 virus variants,” Computational Intelligence in Electrical Engineering, vol. 14, no. 1, pp. 93-108 (2023).
[32] A. Veisi, H. Maleki and H. Delavari, “Non-Pharmacological Interventions for Covid-19 new Variants with Fractional Order Fuzzy Type-2 PID,” 2023 9th International Conference on Control, Instrumentation and Automation (ICCIA), Tehran, Iran, Islamic Republic of, pp. 1-5 (2023), doi: 10.1109/ICCIA61416.2023.10506400.
[33] A. Veisi, H. Maleki and H. Delavari, “Adaptive Fractional Sliding Mode Controller for Controlling Airway Pressure in an Artificial Ventilation System,” 2023 9th International Conference on Control, Instrumentation and Automation (ICCIA), Tehran, Iran, Islamic Republic of, pp. 1-5 (2023), doi: 10.1109/ICCIA61416.2023.10506394.

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