Developing computational models to predict the immune response to B. pertussis booster vaccination

项目摘要/摘要： 百日咳杆菌(PT)是百日咳的病原体。可预防的高效疫苗 PT感染自20世纪40年代初以来一直在使用。然而，在过去的15年里，我们见证了 百日咳病例增加，其原因尚不完全清楚。这 强调需要更好地了解针对PT的免疫反应在个体之间的差异，我们 可以通过测量对PT加强疫苗的免疫反应来确定作为免疫反应的代理 具有感染力的挑战。 我们将使用这些数据来开发计算模型，这些模型将有助于建立量化和预测性 了解影响PT强化疫苗免疫反应的因素。具体来说， 我们将研究人口统计变量的影响，如年龄和性别，接种疫苗的类型 婴儿期和接种疫苗前的免疫状态。了解这些变量如何影响疫苗 应答不仅对PT很重要，而且对一般的疫苗研究也很重要。 为了实现这一点，我们提出了一个迭代过程，包括模型建立、模型评估、实验数据 生成和模型改进。我们将通过主办一次 年度预测大赛。我们特别强调使这一进程透明和公开，以 确保模型的价值和评估结果被社区广泛接受。 具体来说，我们会： 1)建立和建立一个开放的平台，构建和评估PT加强免疫的计算模型。 2)生成交错发布日期的新实验数据以测试并迭代改进计算 模特们。 3)让更广泛的科学界参与到建模工作中来。

Project Summary/Abstract: Bordetella pertussis (PT) is the causative agent of whooping cough. Highly effective vaccines to protect from PT infection have been in use since the early 1940s. However, the last fifteen years have witnessed a consistent increase in the number of whooping cough cases, and the reasons for this are not completely understood. This highlights the need to better understand how immune responses against PT vary between individuals, which we can determine by measuring immune responses to PT booster vaccination as a proxy for immune responses to infectious challenge. We will use these data to develop computational models that will help establish a quantitative and predictive understanding of the factors that impact immune responses resulting from PT booster vaccination. Specifically, we will examine the impact of demographic variables such as age and sex, the type of vaccine received in infancy, and the immune state prior to vaccination. Understanding how these variables can impact vaccine responses is of importance not only for PT, but for vaccine research in general. To achieve this, we are proposing an iterative process of model building, model evaluation, experimental data generation, and model refinement. We will engage the wider scientific community in this process by hosting an annual prediction contest. We have placed particular emphasis on making this process transparent and open, to ensure that the value of the models and the results from the evaluation are accepted by the community at large. Specifically, we will: 1) Establish and seed an open platform to build and evaluate computational models of PT booster vaccination. 2) Generate new experimental data with staggered release dates to test and iteratively improve computational models. 3) Engage the broader scientific community in this modeling effort.