Other ; Objective: The objective of this study is to evaluate the direct and indirect economic impact of vaccine-based interventions in response to an influenza pandemic, for the city of Chicago, using a dynamic agent-based model. Background: Prior work on this topic has focused on understanding the direct economic impact of intervention strategies on controlling influenza pandemics. In contrast, this work applies a dynamic agent-based model to estimate the direct as well as indirect economic impact of vaccine-based interventions. Measurement of direct and spill-over effects of interventions will guide more efficient allocation of clinical resources aimed at minimizing the costs of deaths, hospitalizations, and outpatient visits during an influenza pandemic. Methods: We use a collocation based synthetic social contact network, generated for the city of Chicago. The transmission dynamics of the influenza-like-illness in the population is simulated using the susceptible-exposed-infectious-recovered (SEIR) epidemiological model. We use an agent-based model to compare the costs and benefits of vaccine-based interventions under different transmission scenarios during an influenza pandemic. Results: Vaccination of 40% of the population by the dynamic model results in 19.84% (range: 17.41%-23.84%) reduction in the attack rate of pandemic influenza, compared to the results of static model by Meltzer et al. (1999), which shows 6.77% (range: 4.7%, 9.4%) reduction in the attack rate. In summary, the results of the vaccination in the dynamic model show 46.8% (range: 26.0%, 69.2%) more net return per vaccinated individual, in comparison to the static model. Conclusion: From similar vaccine-based intervention scenarios tested in the static Monte Carlo model of Meltzer et al. (1999), we infer the dynamic agent-based model comparatively averts more cases of influenza, and the vaccine-based interventions are comparatively more cost effective for all age and risk groups.