Abstract
ObjectivesThis paper proposes a new stochastic epidemic modeling approach to estimate the effectiveness of COVID-19 vaccinations based on individual vaccination status using only observational data. MethodsTo accomplish this goal, we modified the SEIR model to categorize individuals according to their vaccination status and utilized Bayesian data augmentation techniques to assess vaccine effectiveness with partially observed data. We also implemented a dynamic time warping algorithm to compare transmission probabilities between groups with different vaccination statuses. ResultsOur findings indicated that both the fully vaccinated and boosted groups experienced lowered transmission probabilities, with average reductions of 51% and 49% respectively. Interestingly, we also observed no significant difference between the boosted group and fully vaccinated group regarding re-susceptibility. ConclusionIn conclusion, this proposed stochastic epidemic modeling approach for estimating COVID-19 vaccine effectiveness has significant implications for public health policy and decision-making. It could lead to more precise assessments of vaccine effectiveness and validity tests for clinical trial estimates. Overall, this approach has the potential to be a valuable tool in evaluating vaccine effectiveness and the population impact of the vaccination process.
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