Predicting and monitoring variations in the effects of vaccines against RSV

项目概要/摘要 呼吸道合胞病毒（RSV）在婴儿和老年人中引起很大的感染负担。而 目前还没有疫苗，几种候选疫苗正在进行临床试验，预计将成为 在未来几年内可用。目前正在考虑各种疫苗输送方法，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。目前迫切 需要确定将RSV疫苗的直接和间接效益最大化的接种策略 并根据现有监测数据监测影响的变化。整体 该项目的目标是使用数学模型来优化新型疫苗的疫苗输送策略 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， 可以使用可靠收集的医疗数据监测疫苗在地方层面的影响。疾病 疫苗的动态和影响往往是在州或国家一级的总体情况下确定的。然而，在这方面， 这种汇总忽略了地方一级由于疫苗摄取和/或 空间结构的联系网络。这种类型的变异性可能导致疫苗效果的差异 在不同地区之间。通过量化和了解RSV动力学异质性的驱动因素，我们 可以评估不同的交付策略，最大限度地提高疫苗对RSV的影响， 对整个人口进行监测和设计监测，以监测地方一级的影响。开发和验证这些 先进的建模方法，我们将使用监控数据和严格收集的行政管理数据， 来自美国几个地方的住院数据，代表了广泛的流行病学 设置.我们将检验关于病原体传播发生的空间尺度、 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 使用传播的统计模型和动态模型来量化 RSV流行病，并测试有关驱动这些模式的机制的假设，包括当地 以及不同年龄组的区域接触模式。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 关于疫苗提供战略的假设，将使各年龄组和各年龄组的效益最大化， 空间从这些模型中获得的信息可用于指导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，使我们能够设计监督和监测 一旦采用了使用可靠收集的住院数据的疫苗，疫苗影响的地方差异 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.