Developing and Applying Analytical Models of Influenza Transmission

我们目前的科学知识表明，通过呼出的病毒生物气雾剂传播流感的重要性 适用于室内环境中的短距离和长距离。室内气流的湍流性与 呼出的生物气溶胶源的动力学性质和对基本源项的较差理解 (包括按气雾剂大小和每个气雾剂中的病毒颗粒数量分列的感染性病毒分布) 预测和跟踪气溶胶驱动的传播的困难。为了应对这一挑战，新的采样， 先进生物气溶胶技术核心开发的收集和感染病毒检测技术 (ABTC)将允许临床和生物统计核心(CBC)和研究项目1(RP1)设计和 实施一项队列研究，能够从两组人类受试者呼气中收集关键数据集 呼吸和进行队列研究的临床设施的受控环境。基于 这些关键数据集，研究项目2(RP2)将开发混合良好和高保真的分析模型 用于其他队列研究中的部署和从队列捐赠者到 收件人。这种直接的物理生物气溶胶联系对于跟踪接受者对病毒的实际暴露很重要 基于数据和高保真模型的生物气溶胶。此外，此链接将支持两种语言的翻译 队列数据和高保真模型导致能够计算数量的混合良好的分析模型 (剂量)用于流感传播风险的建模。队列研究实验设置的精心设计 临床设施是RP2项目的第一个目标(目标1)。设置设计将包括现场数据收集 关于通风率和安装环境控制与紫外线空气消毒。此外， 计算流体动力学(CFD)模型将允许定义面罩形状，该形状将阻止喷雾 对气溶胶影响最小的暴露。RP1中的队列研究将使用识别的面罩形状和 以及干预措施的环境控制。来自干预队列研究的数据将支持 报告2的第二个目标侧重于对气溶胶驱动的流感传播进行分析建模(目标2)。在……里面 RP2的这个最重要的目标，我们将描述导致受者病例的数量(剂量) 流感，使我们能够将剂量与呼气测量的生物气溶胶流失率和 环境气溶胶的命运。气溶胶浓度和最终的气溶胶命运将通过以下途径获得 验证了生物气溶胶时空分布的高保真模型。严格的验证过程 我们的高保真模型将使用持续监测的环境数据(二氧化碳、温度、湿度) 和病毒生物气溶胶数据。这些独特的数据集将使团队能够创造出不同类型的流感 用于区分短程和长程生物气溶胶的传播模型。最后一步是延长我们的 对其他环境的分析模型，如家庭队列和雪貂流感研究。RP2将提供 这两个分析模型和一个基于网络的工具，方便外地用户访问。

Our current scientific knowledge indicates the importance of influenza transmission via exhaled viral bioaerosols over short and long ranges in indoor environments. The turbulent nature of indoor airflows coupled with the dynamic nature of exhaled bioaerosol sources and the poor understanding of fundamental source terms (including distribution of infective virus by aerosol size and number of viral particles per aerosol) creates difficulties in predicting and tracking aerosol-driven transmission. To address this challenge, novel sampling, collection, and infective virus assay technologies developed in the Advanced Bioaerosol Technology Core (ABTC) will allow the Clinical and Biostatistics Core (CBC) and Research Project 1 (RP1) to design and implement a cohort study capable of collecting critical data sets from both the cohort human subject exhaled breath and the controlled environment of the clinical facility where the cohort study will take place. Based on these critical datasets, Research Project 2 (RP2) will develop both well-mixed and high-fidelity analytical models for deployment in other cohort studies and physical tracking of viral bioaerosol from cohort donors to the recipients. This direct physical bioaerosol link is important to track actual exposure of recipients to viral bioaerosols based on both data and high -fidelity models. Furthermore, this link will enable translation of both cohort data and high-fidelity model results into well-mixed analytical models capable of accounting of quanta (dose) for modeling of risk of influenza transmission. A careful design of the cohort study experiment setup in the clinical facility is the first aim in this RP2 project (Aim 1). The setup design will include on-site data collection on ventilation rates and installation of environmental controls with UV air disinfection. Furthermore, Computational Fluid Dynamics (CFD) models will allow to define a face shield shape that will block sprayborne exposure with minimal impact on aerosols. The cohort study in RP1 will use both the identified face shield shape and the environmental controls for the interventions. The data from the intervention cohort studies will support the second aim in RP2 focused on analytical modeling of aerosol-driven transmission of influenza (Aim 2). In this most important aim of RP2, we will characterize the quanta (dose) that resulted in recipient cases of influenza, allowing us to link the dose to both bioaerosol shedding rate from exhaled breath measurements and environment aerosol fate. The aerosol concentration and ultimately aerosol fate will be available through validated high-fidelity modeling of temporal and spatial distributions of bioaerosols. A rigorous validation process of our high-fidelity models will use both continuously monitored environmental data (CO2, temperature, humidity) and viral bioaerosol data. These unique data sets will allow the team to create different type of influenza transmission models to distinguish between the short and long range bioaerosols. The final step is to extend our analytical models to other environments such as household cohort and ferret influenza studies. RP2 will provide both analytical models and a web-based tool for user-friendly access in the field.