A Predictive Vaccination Strategy Based on a Swarm Intelligence Technique for the Case of Saudi Arabia: A Control Engineering Approach
Received: 22 April 2023 | Revised: 13 May 2023 | Accepted: 15 May 2023 | Online: 9 August 2023
Corresponding author: Sahbi Boubaker
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.
Keywords:COVID-19, optimal control, model identification, vaccination strategy, disease mitigation
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