Virtual Patient Cohorts to Illuminate Immunologic Drivers of Influenza Severity

项目摘要 流感病毒每年导致大量的疾病和死亡，突出了其健康和 经济负担。这种疾病的管理是困难的，而且很少有人知道不同的宿主因素 导致异质性结果。为了推进理解不同免疫反应的目标， 流感和预测风险，至关重要的是开发新的工具，可以定义个性化的免疫轨迹， 同时解释异质性的多种来源，并准确预测驱动疾病的动力学 进展该项目旨在解决在确定宿主因素对疾病结果的影响方面存在的差距， 在开发准确预测炎症的呼吸道感染计算方法方面存在差距， 疾病严重程度。这些研究将开发和利用新的预测性系统免疫模型， 使用虚拟患者队列的人群旨在区分临床结果并识别下游 不同水平的基础免疫力。

Project Summary Influenza viruses result in a significant number of illnesses and deaths each year highlighting its health and economic burden. Management of this disease is difficult, and little is known about how different host factor contribute to heterogenous outcomes. To advance the goals of understanding the diverse immune responses to influenza and predict risk, it is essential to develop new tools that can define individualized immune trajectories, simultaneously account for multiple sources of heterogeneity, and accurately predict dynamics that drive disease progression. This project addresses gaps in identifying the impact that host factors have disease outcome and gaps in developing computational methods for respiratory infections that accurately predict inflammation and disease severity. The studies will develop and exploit new predictive systemic immune models and simulate human populations using virtual patient cohorts aims at differentiating clinical outcomes and identify downstream effects of varying levels of basal immunity.