A Case Study on Strategic Multi-Objective Optimization of Influenza Vaccine Allocation: Age-Specific Approaches in Saudi Arabia

Acute lower respiratory tract infections pose a significant health challenge, affecting over 15% of Saudi Arabia’s population annually. Research over the past three decades highlights that viruses are the primary cause of these infections. The epidemiology of respiratory viruses in Saudi Arabia is influenced by the movement of foreign workers and the mass gatherings of Muslims in Mecca during the Hajj and Umrah seasons. This study exploring the impact of different vaccination rates across various age groups during the 2019/2020 influenza season in Saudi Arabia. We used a two-phased multi-objective optimization approach with a Controlled Elitism Non-Dominated Sorting Genetic Algorithm applied to an age-structured SEIR model (Susceptible-Exposed–Infectious-Recovered) to design vaccination policies tailored to age-specific infection rates. The research divides the population into five age categories—infants and children, adolescents, young adults, middle-aged adults, and seniors—across five regions: Eastern, Central, Northern, Western, and Southern Saudi Arabia. The study compares three vaccination strategies: no vaccination, national vaccination, and a two-phased multi-objective optimization approach under various pandemic scenarios. It evaluates the outcomes regarding infection rates, mortality, and morbidity. The findings highlight how demographic factors significantly influence disease transmission and vaccine distribution. The optimized vaccination strategy outperformed the national vaccination approach, leading to more significant reductions in infection rates (by 0.01% to 0.45%), morbidity (by 0.01% to 0.04%), and mortality (by 0.001% to 0.013%).