Abstract: Computer simulation is the only method available for evaluating vaccination policy for rare diseases or emergency use of new vaccines. The most realistic simulation of vaccination policy is agent-based simulation (ABS) in which agents have similar socio-demographic characteristics to a population of interest. Currently, analysts use published information about vaccine efficacy (VE) as the probability that a vaccinated agent develops immunity; however, VE trials typically report only a single overall VE, or VE conditioned on one covariate (e.g., age). Thus, ABS's potential to realistically simulate the effects of co-existing diseases, gender, and other characteristics of a population is under-used. We developed a Bayesian network (BN) model as a compact representation of a VE trial dataset for use in ABS of vaccination policy. We compared BN-based VEs to the VEs estimated directly from the dataset. Our evaluation results suggest that VE trials should release statistical models of their datasets for use in ABS of vaccination policy.
Learning Objective 1: Consider the advantage of Bayesian Network models of vaccine-efficacy (VE) datasets over the information that VE trials report as tables in publications, for use in agent-based simulations of vaccination.
Mohammadamin Tajgardoon (Presenter)
University of Pittsburgh
Michael Wagner, University of Pittsburgh
Shyam Visweswaran, University of Pittsburgh
Richard Zimmerman, University of Pittsburgh