Longitudinal dynamics of protection after influenza infection and vaccination

流感感染和疫苗接种后保护的纵向动态

• 批准号: 10442728
• 负责人: Sarah Cobey
• 金额: $ 46.53万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10442728
• 关键词: Address; Advanced Development; Affect; Age; Antibody Response; Antibody titer measurement; Antigens; Avian Influenza; Biological; Birth; Cessation of life; Characteristics; Child; Childhood; Cohort Studies; Complex; Complication; Computer software; Data; Dependence; Development; Development Plans; Effectiveness; Epidemiology; Evolution; Exposure to; Formulation; Future; Household; Immune; Immune response; Immunity; Immunologic Memory; Individual; Infection; Influenza; Influenza A virus; Influenza vaccination; Knowledge; Life; Mathematics; Measures; Methods; Modeling; Nicaragua; Pattern; Pediatric cohort; Persons; Predisposition; Primary Infection; Public Health; Recording of previous events; Research; Risk; Role; Sampling; Seasons; Serology; Severities; Shapes; Strategic Planning; Symptoms; Techniques; Time; United States National Institutes of Health; Vaccination; Vaccines; Variant; Viral; Virus Diseases; Work; adaptive immune response; associated symptom; cohort; cross immunity; diverse data; flexibility; imprint; improved; infection risk; influenza infection; influenza virus strain; influenza virus vaccine; longitudinal dataset; mathematical model; next generation; pathogen; respiratory; response; seasonal influenza; sex; success; tool; transmission process; universal influenza vaccine

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).

项目总结 个体的适应性免疫反应是流感流行病学和进化的核心 流感疫苗的有效性。因此，令人惊讶的是，尽管研究了近70年，少校 有关流感的免疫反应的问题仍未得到回答。特别是，目前还不清楚 自然感染防止相同或相关类型和亚型的再次感染，疫苗接种的影响 随着时间的推移，对有症状和无症状感染的保护，以及保护如何随免疫而变化 病史、年龄、个人、性别等因素。取得进展的两个主要障碍是缺乏 随着时间的推移，来自相同个人的观察结果，以及缺乏能够适应 感染和免疫反应的复杂、随机动态在个体之间复制。建议数 研究利用非同寻常的流感队列和新的纵向建模方法来 了解如何产生对不同严重程度的流感感染的保护，特别是它是如何形成的 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)随后 自2011年以来，数千名儿童接受了调查，并记录了他们的抗体效价、感染、症状和疫苗接种情况 流感病史。我们将使用这些数据来拟合和评估一大组随机的、个人级别的、 用于估计防护持续时间及其对暴露历史的依赖的机械、动力学模型 以及其他因素。首先，我们将估计针对相同类型或 根据早期暴露的顺序以及宿主和病毒特征对其进行分型并评估其依赖性。下一首, 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，我们会 比较自然感染和接种疫苗后的保护力度，包括重复 接种疫苗。我们灵活的建模方法利用了不同的数据类型和推理技术 同时允许在数学上精确地表述生物学假说。它最近在类似的产品中取得了成功 经聚合酶链式反应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，接触史对异型感染风险有一定作用。这部作品有望成为 先进的流感基础知识和免疫记忆的发展，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 针对一种重要且不断增长的数据类型的推理技术和软件的开发 扩大一个重要的寄主-病原体动态的知识。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，特别是识别 与流感严重程度有关的因素(目标1.2)和增进对如何以及何时发生流感的了解 暴露于流感抗原影响对感染和疫苗接种的反应(目标2.1)。