Predicting and monitoring variations in the effects of vaccines against RSV

项目摘要/摘要 呼吸道合胞病毒(RSV)对婴儿和老年人都造成了很大的感染负担。而当 目前还没有疫苗，几个候选疫苗正在接受临床测试，预计将成为 在接下来的几年里可以使用。目前正在考虑各种提供疫苗的方法，包括 为母亲接种疫苗以保护婴儿，并直接为儿童和高危成人接种疫苗。有一件急事 需要确定最大限度地提高RSV疫苗的直接和间接效益的投放策略 并根据现有的监测数据监测影响的变化。整体而言 该项目的目标是使用数学模型来优化新型疫苗的疫苗投放策略。 针对RSV，使用模型来指导未来临床试验的设计，并开发统计模型 可以使用常规收集的医疗保健数据在地方一级监测疫苗影响的变化。疾病 疫苗的动态和影响往往是国家或国家一级的特征。然而， 这种聚集忽略了由于疫苗接种率和/或疫苗接种量的变化而导致的地方一级的重要变异性 空间结构的联系网络。这种类型的变异性可能会导致疫苗效果的差异 在不同地区之间。通过量化和了解RSV动态中的异质性驱动因素，我们 可以评估不同的投放策略，以最大限度地提高针对RSV的疫苗在整个 整个人口，并设计监测，以监测地方一级的影响。开发和验证这些 尖端建模方法，我们将使用监控数据和常规收集的管理 来自美国多个地点的住院数据代表了广泛的流行病学 设置。我们将测试关于病原体传播发生的空间尺度的特定假设， 这种空间变异的决定因素，以及这种变异对疫苗影响的影响。在目标1中，我们将 使用传播的统计模型和动态模型来量化动态中的空间变异性 RSV流行，并测试关于驱动这些模式的机制的假设，包括局部 以及不同年龄段之间的区域接触模式。了解这些机制将有助于了解 预测疫苗对RSV的影响。在目标2中，我们将使用传输模型来测试替代方案 关于疫苗交付战略的假设将使跨年龄组和跨年龄段的利益最大化 太空。从这些模型中获得的信息可以用来指导RSV临床试验的设计。 最后，在目标3中，我们将开发一个分析框架，使我们能够设计监视和监视 使用常规收集的住院数据，疫苗一旦引入，其影响的局部变化 同时控制时空变化的混杂因素。

Project Summary/Abstract Respiratory syncytial virus (RSV) causes a large burden of infections in both infants and the elderly. While there is currently no vaccine, several candidates are undergoing clinical testing and are expected to become available in the coming years. A variety of approaches for vaccine delivery are being considered, including immunizing mothers to protect infants and directly immunizing children and high-risk adults. There is an urgent need to determine the delivery strategy that will maximize the direct and indirect benefits of RSV vaccines across the entire population and to monitor variations in impact from available surveillance data. The overall goals for this project are to use mathematical models to optimize vaccine delivery strategies for novel vaccines against RSV, to use models to guide the design of future clinical trials, and to develop statistical models that can monitor variations in vaccine impact at local-levels using routinely-collected healthcare data. Disease dynamics and the impact of vaccines are often characterized at aggregated state or national levels. However, this aggregation ignores important variability at the local level due to variations in vaccine uptake and/or spatially-structured contact networks. This type of variability can result in disparities in the impact of a vaccine between locales. By quantifying and understanding the drivers of heterogeneity in the dynamics of RSV, we can evaluate different delivery strategies that would maximize the impact of a vaccine against RSV across the entire population and design surveillance to monitor impact at the local level. To develop and validate these cutting-edge modeling approaches, we will use surveillance data and routinely-collected administrative hospitalization data from several locations in the United States that represent a broad range of epidemiological settings. We will test specific hypotheses about the spatial scale at which pathogen transmission occurs, the determinants of this spatial variation, and the implications of this variation for vaccine impact. In Aim 1, we will use statistical models and dynamic models of transmission to quantify spatial variability in the dynamics of RSV epidemics and to test hypotheses about the mechanisms driving these patterns, including the role of local and regional contact patterns among different age groups. Understanding these mechanisms will help to inform predictions of the impact of vaccines against RSV. In Aim 2, we will use transmission models to test alternative hypotheses about the strategy for vaccine delivery that would maximize benefits across age groups and across space. The information derived from these models can be used to guide the design of clinical trials for RSV. Finally, in Aim 3, we will develop an analysis framework that will allow us to design surveillance and monitor local variations in the impact of a vaccine once it is introduced using routinely-collected data on hospitalizations while controlling for spatiotemporally-varying confounders.