Predictive Modeling of Influenza-Pneumococcal Coinfection

甲型流感病毒(IAV)和继发性细菌感染(SBI)是相当多的 每年的疾病和死亡人数。这些疾病的管理很困难，部分原因是缺乏 理解宿主-病原体相互作用的复杂相互作用和无法研究肺炎 临床环境。为了推进开发有效疗法和预测IAV和SBI风险的目标， 新的微生物学工具，可以评估宿主免疫反应如何工作，以限制病毒负担和 在定量细节上增强细菌侵袭是必不可少的。这个项目解决了免疫学方面的空白 对IAV和SBI的知识以及在开发预测模型和解释感染数据方面的差距 使用串联数学-实验方法量化肺泡巨噬细胞损失(目标1) 和SBI相关的I型干扰素加重(目标2)。这些研究将利用预测模型来 在这些响应中建立复杂的反馈，确定控制参数和动态 管理不同的临床结果，改进对免疫学和临床数据的解释，并揭示新的 流感及相关细菌感染的治疗和预防目标。

Influenza A virus (IAV) and secondary bacterial infections (SBI) are responsible for a significant number of illnesses and deaths each year. Management of these diseases is difficult, in part due to a lack of understanding of complex interplay of host-pathogen interactions and inability to study pneumonia in clinical settings. To advance the goal of developing effective therapeutics and predicting IAV and SBI risk, new microbiologic tools that can assess how host immune responses work to limit viral burden and enhance bacterial invasion in quantitative detail is essential. This project addresses gaps in immunological knowledge of IAV and SBIs and gaps in developing predictive models and interpreting infection data by using a tandem mathematical-experimental approach to quantify alveolar macrophage loss (Aim 1) and SBI related type I interferon exacerbation (Aim 2). These studies will exploit the predictive models to establish the intricate feedbacks in these responses, identify controlling parameters and dynamics that govern different clinical outcomes, improve interpretation of immunological and clinical data, and reveal new targets for treatment and prevention of influenza and related bacterial infections.