MODELING VACCINATION STRATEGIES AND INTERVENTION IMPACTS ON INFECTIOUS DISEASE DYNAMICS

Abstract

This dissertation explores how mathematical and statistical modeling can inform vaccination strategies and evaluate the impact of interventions during infectious disease outbreaks. Motivated by the challenges observed during the COVID-19 pandemic, the research integrates compartmental modeling, Bayesian inference, and agent-based simulation to assess policy effectiveness and disease control across varying contexts. The first study develops a SEIRDV model that incorporates government interventions and vaccination rollout to examine COVID-19 dynamics in Qatar. A Bayesian approach is used to estimate parameters and compute time-varying transmission rates. The model captures shifts in transmission following policy changes and quantifies the reduction in deaths attributable to vaccination. Building on this, the second study introduces two compartmental models to account for reinfection dynamics and varying vaccine efficacy. Using a Bayesian framework with the Metropolis-Hastings algorithm, the study compares models through scenario analysis and formal metrics such as Bayes factors and Hellinger distance. Results demonstrate the critical importance of early vaccination in minimizing reinfections and hospital burden. The final study employs an agent-based modeling framework, SAFE-ABM, to simulate structured interactions across families, workplaces, and schools. The model evaluates targeted interventions for essential workers, including mobility restrictions, school closures, and rotational workforce strategies. To support robust uncertainty assessment, we introduce a novel Bayesian uncertainty quantification framework specifically designed for agent-based simulations. This framework systematically captures variation in transmission, recovery, and mortality rates. Findings suggest that rotational workforce policies combined with quarantine measures are most effective in curbing workplace outbreaks and household transmission. These studies provide a comprehensive and methodologically innovative view of how vaccination strategies and targeted interventions can be modeled to support public health decision-making during pandemics