EAGER: Accurate Estimation of Indoor Airborne Virus Transmission based on a Novel Multiscale Data-Driven Framework

经空气传播的病毒疾病被认为是一种重要的传播方式。这种传播的许多方面，包括室内空间内人类呼气事件对携带病毒的飞沫的喷射、蒸发和扩散，已经进行了研究。然而，需要开发一个以科学为基础的框架，能够快速可靠地预测空气传播在室内空间的传播。这样的框架将有助于评估教室、餐馆、电梯、飞机机舱等的传染风险，并为政策制定者提供信息。这些知识将为建立可靠和用户友好的预测工具奠定科学基础，研究人员、政策制定者、管理人员和普通公众可以使用这些工具来对室内空间中病毒传播的风险做出明智的决策。有许多重要参数影响空气传播传播，而问题固有的非线性性质使简单的预测变得不可能。在这个项目中，将开发一个多尺度的、数据驱动的框架，用于快速准确地预测受限空间中空气传播的传染病传播。这一框架的发展有两个关键创新。第一个问题涉及将整个问题分成病毒规模、来源规模(呼吸、说话、咳嗽或打喷嚏)和房间规模的关键组成部分。第二种方法是先生成一个粒子扩散信息的大型数据库，然后再处理传染的个别情况，从而扭转问题。这两项创新使一些高保真的模拟可以探索无数的室内病毒传播场景，而不需要对每个单独的场景进行单独的、计算密集的预测。这一框架，以及快速获取室内空间内流动信息的能力，将提供前所未有的预测能力。该框架还将通过考虑喷射的随机性质和流动的湍流性质来评估与预测有关的不确定性。对这一框架和工具的改进应将其适用范围扩大到其他空气传播的传染病以及室内空气质量。数据驱动的框架可以进一步扩展，以解决户外空间的传染风险，并可以量身定做，以解决其他涉及颗粒物扩散的问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Airborne spread of viral diseases is recognized as an important mode of transmission. Many aspects of this transmission, including the ejection, evaporation, and dispersion of virus-laden droplets by human expiratory events within indoor spaces, have been studied. However, a science-based framework that can quickly and reliably predict the spread of airborne contagion in indoor spaces needs to be developed. Such a framework would help to assess the risk of contagion in classrooms, restaurants, elevators, aircraft cabins, etc. and to inform policy makers. This knowledge would form a science-based foundation for building a reliable and user-friendly prediction tools that can be used by researchers, policy makers, administrators, and the general public to make informed decisions about the risks of viral contagion in indoor spaces.There are many important parameters that influence the spread of airborne contagion, and the inherently nonlinear nature of the problem makes simple predictions impossible. A multiscale, data-driven framework for the rapid and accurate prediction of airborne contagion spread in confined spaces will be developed in this project. There are two key innovations in the development of this framework. The first involves separating the overall problem into the key components of virus-scale, source-scale (breathing, talking, coughing, or sneezing), and room-scale components. The second consists of inverting the problem by first generating a large database of particle dispersion information before addressing the individual scenarios of contagion. These two innovations allow a few high-fidelity simulations to explore countless scenarios of indoor virus transmission without the need for separate, computationally intensive predictions of each individual scenario. This framework, along with the ability to rapidly obtain flow information within indoor spaces, will offer an unprecedented predictive capability. The framework will also evaluate uncertainties associated with the prediction by accounting for the stochastic nature of the ejection and the turbulent nature of the flow. Improvements to this framework and tool should extend their applicability to other airborne infectious diseases as well as to indoor air quality. The data-driven framework can be further extended to address the risk of contagion in outdoor spaces and can be tailored to address other problems involving particulate dispersion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.