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

Developing computational models to predict the immune response to B. pertussis booster vaccination
开发计算模型来预测百日咳博德特氏菌加强疫苗接种的免疫反应
批准号:
10570832
负责人:
Bjoern Peters
金额:
$135.01万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-03-10 至 2025-02-28

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英文摘要
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.
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THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE
THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE
THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE
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