Tajgardoon Mohammadamin, Wagner Michael M, Visweswara Shyam, Zimmerman Richard K
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:389-398. eCollection 2018.
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 underused. 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.
计算机模拟是评估罕见疾病疫苗接种政策或新疫苗紧急使用情况的唯一可用方法。疫苗接种政策最逼真的模拟是基于主体的模拟（ABS），其中主体具有与目标人群相似的社会人口特征。目前，分析人员将已发表的关于疫苗效力（VE）的信息用作接种疫苗的主体产生免疫力的概率；然而，VE试验通常只报告单一的总体VE，或基于一个协变量（如年龄）的VE。因此，ABS在逼真模拟人群中并存疾病、性别和其他特征影响方面的潜力未得到充分利用。我们开发了一种贝叶斯网络（BN）模型，作为VE试验数据集的紧凑表示形式，用于疫苗接种政策的ABS。我们将基于BN的VE与直接从数据集中估计的VE进行了比较。我们的评估结果表明，VE试验应发布其数据集的统计模型，以供疫苗接种政策的ABS使用。
