Longitudinal dynamics of protection after influenza infection and vaccination

项目摘要 个体的适应性免疫应答对于流感的流行病学和演变至关重要， 流感疫苗的有效性。因此，令人惊讶的是，尽管近70年的研究，主要 关于对流感的免疫反应的问题仍然没有答案。特别是，目前还不清楚 自然感染可以防止相同或相关类型和亚型的再感染，疫苗接种如何影响 随着时间的推移，对有症状和无症状感染的保护，以及保护如何随着免疫的变化而变化， 病史、年龄、个体、性别和其他因素。取得进展的两个主要障碍是缺乏 随着时间的推移，来自同一个人的观察，以及缺乏可以适应 感染和免疫反应在个体间复制的复杂随机动态。拟议 研究利用了一个非凡的流感队列和纵向建模的新方法， 了解如何保护不同严重程度的流感感染，特别是如何形成的， 感染和疫苗接种史。正在进行的尼加拉瓜儿童流感队列研究（NPICS） 自2011年以来，成千上万的儿童，并记录了他们的抗体滴度，感染，症状和疫苗接种 流感的历史我们将使用这些数据来拟合和评估一组随机的，个人水平的， 估计保护持续时间及其对暴露历史的依赖性的机械动力学模型 等因素首先，我们将估计对同一类型或 亚型并评估其对早期暴露顺序以及宿主和病毒特征的依赖性。下一个是 我们将测量类型和亚型之间交叉保护的强度和持续时间。最后我们将 比较自然感染后和接种疫苗后的保护动态，包括重复接种 接种疫苗我们灵活的建模方法利用了各种数据类型和推理技术 同时允许生物学假设的精确公式化。它最近的成功与类似的 PCR确认的病毒感染和流感血清学的纵向数据集证明了可行性。 初步结果表明，暴露史对异亚型感染风险的作用。这项工作准备 提高对流感的基本认识和免疫记忆的发展，它将提供一套新的 纵向数据的动态建模工具。因此，该项目将通过推进实现NIH MIDAS目标 为一种重要的、不断增长的数据类型开发推理技术和软件， 扩大了对一个重要的宿主-病原体动态的了解。这项工作还直接涉及优先事项 由NIH战略计划建立，用于开发通用流感疫苗，特别是确定 与流感严重程度相关的因素（目标1.2），并进一步了解如何以及何时 暴露于流感抗原影响对感染和疫苗接种的反应（目标2.1）。

PROJECT SUMMARY Individuals’ adaptive immune responses are central to the epidemiology and evolution of influenza and the effectiveness of influenza vaccines. It is therefore surprising that despite nearly 70 years of study, major questions about the immune response to influenza remain unanswered. In particular, it is unclear how well natural infection protects from reinfection with the same or related types and subtypes, how vaccination affects protection against symptomatic and asymptomatic infections over time, and how protection varies with immune history, age, individual, sex, and other factors. The two main obstacles to progress have been a shortage of observations from the same individuals over time and a lack of modeling approaches that can accommodate the complex, stochastic dynamics of infection and immune response replicated across individuals. The proposed research takes advantage of an extraordinary influenza cohort and new methods for longitudinal modeling to understand how protection to influenza infections of varying severity arises, and especially how it is shaped by infection and vaccination history. The ongoing Nicaragua Pediatric Influenza Cohort Study (NPICS) has followed thousands of children since 2011 and recorded their antibody titers, infections, symptoms, and vaccination history to influenza. We will use these data to fit and evaluate a large set of stochastic, individual-level, mechanistic, dynamical models to estimate the duration of protection and its dependence on exposure history and other factors. First, we will estimate the duration of protection against reinfection with the same type or subtype and evaluate its dependence on the order of early exposures and host and viral characteristics. Next, we will measure the strength and duration of cross-protection between type and subtypes. Finally, we will compare the dynamics of protection after natural infection to those after vaccination, including repeat vaccinations. Our flexible modeling approach takes advantage of diverse data types and inference techniques while allowing precise formulation of biological hypotheses mathematically. Its recent success with similar longitudinal datasets of PCR-confirmed viral infections and influenza serology demonstrates feasibility. Preliminary results suggest a role of exposure history on heterosubtypic infection risk. This work is poised to advance basic knowledge on influenza and the development of immune memory, and it will provide a new set of dynamical modeling tools for longitudinal data. This project will thus achieve NIH MIDAS objectives by advancing the development of inference techniques and software for an important and growing type of data and by expanding knowledge of an important host-pathogen dynamic. This work also directly addresses priorities established by the NIH Strategic Plan for the development of a universal influenza vaccine, especially identifying factors associated with the severity of influenza (objective 1.2) and improving understanding of how and when exposure to influenza antigens shapes the response to infection and vaccination (objective 2.1).