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溶胶传播流感的重要性 在室内环境中的短距离和长距离上。室内气流的湍流性质与 呼出生物气溶胶源的动态性质和对基本源项的认识不足 （包括通过气溶胶大小和每个气溶胶的病毒颗粒数量分布感染性病毒） 在预测和跟踪气溶胶驱动的传播方面存在困难。为了应对这一挑战，新的采样， 先进生物气溶胶技术核心中开发的病毒收集和感染性病毒检测技术 （ABTC）将允许临床和生物统计学核心（CBC）和研究项目1（RP 1）设计和 实施能够从人类受试者呼出的群组和人类受试者呼出的群组收集关键数据集的群组研究， 呼吸和将进行队列研究的临床机构的受控环境。基于 针对这些关键数据集，研究项目2（RP2）将开发混合良好的高保真分析模型 用于部署在其他队列研究中，并对从队列供体到 受惠人士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是重要的 生物气溶胶基于数据和高保真模型。此外，此链接将使翻译两者 队列数据和高保真度模型的结果，成为良好的混合分析模型，能够核算的量子 （剂量）用于流感传播风险建模。一个精心设计的队列研究实验设置在 临床设施是本RP 2项目的首要目标（目标1）。设置设计将包括现场数据收集 通风率和安装紫外线空气消毒的环境控制。此外，委员会认为， 计算流体动力学（CFD）模型将允许定义一个面罩形状， 对气溶胶影响最小的暴露。RP 1中的队列研究将使用已识别的面罩形状 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 RP 2的第二个目标侧重于气溶胶驱动的流感传播的分析建模（目标2）。在 RP2最重要的目标，我们将描述导致受体病例的量子（剂量）， 流感，使我们能够将剂量与呼出气测量的生物气溶胶脱落率和 环境气溶胶归宿气溶胶浓度和最终的气溶胶命运将通过 生物气溶胶时间和空间分布的验证高保真建模。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该团队能够创建不同类型的流感 传输模型，以区分短期和长期的生物气溶胶。最后一步是扩展我们的 分析模型，以其他环境，如家庭队列和雪貂流感研究。RP2将提供 分析模型和方便实地用户使用的网上工具。

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.