Fair and effective vaccine allocation during pandemics: A system dynamics model for prioritization of socioeconomically vulnerable populations

Health inequality emerged as a central ethical concern during the COVID-19 pandemic. Vaccine prioritization guidelines have been criticized for overlooking socioeconomic disparities, leading to perceived unfairness. This study presents a system dynamics model that integrates age-stratified medical considerations with socioeconomic factors to propose a more equitable vaccine allocation strategy, using England as a case study. The model prioritizes socioeconomically vulnerable groups within the same age cohort until a defined vaccination threshold is reached. Applying an extended Susceptible-Exposed-Infected-Recovered (SEIR) framework and using the Gini coefficient to assess health inequality, the model demonstrates that this approach can simultaneously reduce mortality and improve both input and outcome fairness. Demographic analysis shows that age structure and the size of the susceptible population are critical determinants of threshold-based policy effectiveness. Such policies are less effective in predominantly young populations than in populations with a more balanced age distribution. However, enhancing living conditions for vulnerable groups remains essential to further reducing health inequality. Results also illustrate the pandemic's disproportionate impact on vulnerable groups and emphasize the need for targeted interventions. These findings offer actionable insights for policymakers aiming to enhance fairness and reduce mortality rates in future pandemics.