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Abstract:
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This talk focuses on Bayesian inference for survival probabilities in a prime-boost vaccination regime in the development of Ebola vaccine. We are interested in the heterologous prime-boost regimen due to its demonstrated durable immunity, well-tolerated safety profile, and suitability as a population vaccination strategy. Our research is motivated by the need to estimate the survival probability given the administrated dosage. To do so, we establish two key relationships. First, we model the connection between the designed dose concentration and the induced antibody count from them using a response surface model. Second, we model the association between the anti-body count and the probability of survival when experimental subjects are exposed to the Ebola virus in a controlled setting using a survival model. Finally, we employ a combination of the two models with dose concentration as the predictor of the survival probability for a future vaccinated population. We implement our two-level Bayesian model in Stan and illustrate its use with simulated and real-world data. Performance of this model is evaluated via simulation.
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