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An age-gender-structured mathematical model to study the optimization of COVID-19 vaccination programs
Gilberto González-Parra1,2, Giulia Luebben2, Rafael J Villanueva1
1Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain.
Mathematics and Computers in Simulation
|January 7, 2026
Summary
Optimizing COVID-19 vaccination strategies is complex. The study found prioritizing middle-aged individuals without comorbidities, then males with comorbidities, best reduced fatalities and years of life lost.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- COVID-19 pandemic necessitated rapid vaccine deployment.
- Vaccination program efficacy is influenced by age, gender, comorbidity, and vaccine hesitancy.
- Optimizing vaccination strategies is crucial for minimizing disease impact.
Purpose of the Study:
- To develop and analyze an age-structured mathematical model for evaluating COVID-19 vaccination program efficacy.
- To identify optimal vaccination prioritization strategies using real-world US vaccine availability data.
- To minimize fatalities and years of life lost (YLL) through informed vaccine recommendations.
Main Methods:
- Constructed a large non-autonomous system of nonlinear differential equations for an age-structured epidemiological model.
- Incorporated factors like comorbidity status, vaccine hesitancy, gender, and social contacts.
- Developed and applied three novel optimization algorithms to identify best vaccination programs.
Main Results:
- Optimal vaccination strategies prioritize middle-aged males and females without comorbidities.
- A secondary optimal strategy targets middle-aged males with comorbidities.
- These findings consider male-higher case fatality rates and comorbidity behavior.
Conclusions:
- Mathematical modeling is vital for designing effective vaccination programs.
- Prioritizing specific demographic groups can significantly reduce COVID-19 mortality and YLL.
- The study provides a framework for rational vaccine recommendations for future pandemics.
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