A Predictive Vaccination Strategy Based on a Swarm Intelligence Technique for the Case of Saudi Arabia: A Control Engineering Approach


  • Sahbi Boubaker Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 13 | Issue: 4 | Pages: 11091-11095 | August 2023 | https://doi.org/10.48084/etasr.5987


The COVID-19 pandemic caused high damage to health, social, and economic systems globally. Saudi Arabia has conducted a relatively successful experience in mitigating the virus. Saudi authorities have started a vaccination campaign by the end of 2020 with more than 60 million doses being administered to citizens and residents by February 2, 2022. The objective of this study is to propose an optimal vaccination strategy in short and medium terms in order to help the local health authorities to first assess the vaccination campaign and to propose a predictive vaccination plan for eradicating the disease. For this purpose, a control engineering approach was used where the disease dynamics was identified and an optimal control law using the daily number of vaccines as input and the daily number of new infections as output was proposed and evaluated. The vaccination process was modeled as a discrete-time transfer function. The parameters of the transfer function were identified based on the Particle Swarm Optimization (PSO) algorithm while considering the Routh-Hurwitz stability criterion for analyzing the system stability. The final step of this study was dedicated to synthesize three controller variants (P, PI, and PID) for the case study of Saudi Arabia. The obtained results for the modeling and the controllers’ design were found to be promising. The results were found to be generic and can therefore be used to control other diseases or any other occurrence of COVID-19 or similar viruses.


COVID-19, optimal control, model identification, vaccination strategy, disease mitigation


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T. N. Vilches, K. Zhang, R. Van Exan, J. M. Langley, and S. M. Moghadas, "Projecting the impact of a two-dose COVID-19 vaccination campaign in Ontario, Canada," Vaccine, vol. 39, no. 17, pp. 2360–2365, Apr. 2021.

V. Volpert, M. Banerjee, and S. Sharma, "Epidemic progression and vaccination in a heterogeneous population. Application to the Covid-19 epidemic," Ecological Complexity, vol. 47, Sep. 2021, Art. no. 100940.

M. A. Acuña-Zegarra, S. Díaz-Infante, D. Baca-Carrasco, and D. Olmos-Liceaga, "COVID-19 optimal vaccination policies: A modeling study on efficacy, natural and vaccine-induced immunity responses," Mathematical Biosciences, vol. 337, Jul. 2021, Art. no. 108614.

C. A. Varotsos, V. F. Krapivin, Y. Xue, V. Soldatov, and T. Voronova, "COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness," Safety Science, vol. 142, Oct. Art. no. 105370, 2021.

A. Olivares and E. Staffetti, "Uncertainty quantification of a mathematical model of COVID-19 transmission dynamics with mass vaccination strategy," Chaos, Solitons & Fractals, vol. 146, May 2021, Art. no. 110895.

S. Moore, E. M. Hill, M. J. Tildesley, L. Dyson, and M. J. Keeling, "Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study," The Lancet Infectious Diseases, vol. 21, no. 6, pp. 793–802, Jun. 2021.

D. Chaturvedi and U. Chakravarty, "Predictive analysis of COVID-19 eradication with vaccination in India, Brazil, and U.S.A," Infection, Genetics and Evolution, vol. 92, Aug. 2021, Art. no. 104834.

L. S. Ferreira et al., "Modelling optimal vaccination strategies against COVID-19 in a context of Gamma variant predominance in Brazil," Vaccine, vol. 40, no. 46, pp. 6616–6624, Nov. 2022.

M. Coccia, "Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis," Environmental Research, vol. 204, Mar. 2022, Art. no. 112314.

D. Kim and Y. J. Lee, "Vaccination strategies and transmission of COVID-19: Evidence across advanced countries," Journal of Health Economics, vol. 82, Mar. 2022, Art. no. 102589.

G. B. Libotte, F. S. Lobato, G. M. Platt, and A. J. Silva Neto, "Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment," Computer Methods and Programs in Biomedicine, vol. 196, Nov. 2020, Art. no. 105664.

M. Angeli, G. Neofotistos, M. Mattheakis, and E. Kaxiras, "Modeling the effect of the vaccination campaign on the COVID-19 pandemic," Chaos, Solitons & Fractals, vol. 154, Jan. 2022, Art. no. 111621.

A. Thongtha and C. Modnak, "Optimal COVID-19 epidemic strategy with vaccination control and infection prevention measures in Thailand," Infectious Disease Modelling, vol. 7, no. 4, pp. 835–855, Dec. 2022.

H. Tiirinki, M. Viita-aho, L.-K. Tynkkynen, M. Sovala, V. Jormanainen, and I. Keskimäki, "COVID-19 in Finland: Vaccination strategy as part of the wider governing of the pandemic," Health Policy and Technology, vol. 11, no. 2, Jun. 2022, Art. no. 100631.

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022.

S. A. A. Biabani and N. A. Tayyib, "A Review on the Use of Machine Learning Against the Covid-19 Pandemic," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8039–8044, Feb. 2022.

N. K. Al-Shammari, H. B. Almansour, and M. B. Syed, "Development of an Automatic Contactless Thermometer Alert System Based on GPS and Population Density," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 7006–7010, Apr. 2021.

R. Zrieq, S. Boubaker, S. Kamel, M. Alzain, and F. D. Algahtani, "Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches," The Journal of Infection in Developing Countries, vol. 16, no. 01, pp. 90–100, Jan. 2022.

S. Bourafa, M.-S. Abdelouahab, and A. Moussaoui, "On some extended Routh–Hurwitz conditions for fractional-order autonomous systems of order α ∈ (0, 2) and their applications to some population dynamic models," Chaos, Solitons & Fractals, vol. 133, Apr. 2020, Art. no. 109623.

F. A. Hasan, L. J. Rashad, and A. T. Humod, "Integrating Particle Swarm Optimization and Routh-Hurwitz’s Theory for Controlling Grid-Connected LCL-Filter Converter," International Journal of Intelligent Engineering and Systems, vol. 13, no. 4, pp. 102–113, Aug. 2020.

R. Mahardika, Widowati, and Y. D. Sumanto, "Routh-hurwitz criterion and bifurcation method for stability analysis of tuberculosis transmission model," Journal of Physics: Conference Series, vol. 1217, no. 1, Feb. 2019, Art. no. 012056.

"owid/covid-19-data," GitHub. https://github.com/owid/covid-19-data.


How to Cite

S. Boubaker, “A Predictive Vaccination Strategy Based on a Swarm Intelligence Technique for the Case of Saudi Arabia: A Control Engineering Approach”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 4, pp. 11091–11095, Aug. 2023.


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