Virtual Patient Cohorts to Illuminate Immunologic Drivers of Influenza Severity

项目摘要 流感病毒每年导致大量疾病和死亡，突出了它的健康和 经济负担。这种疾病的管理很困难，关于不同的宿主因素是如何产生的知之甚少 会导致不同的结果。为了推进理解不同的免疫反应的目标 在流感和预测风险方面，开发新的工具来定义个性化的免疫轨迹是至关重要的， 同时解释异质性的多个来源，并准确预测驱动疾病的动态 进步。该项目解决了在确定宿主因素对疾病结局的影响以及 在开发准确预测炎症和肺炎的呼吸道感染计算方法方面存在差距 疾病的严重性。这些研究将开发和开发新的预测性系统免疫模型和模拟 使用虚拟患者队列的人类群体旨在区分临床结果并确定下游 基础免疫水平不同的影响。

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.