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

EAGER：基于新型多尺度数据驱动框架准确估计室内空气传播病毒传播

基本信息

批准号：
2134083
负责人：
Sivaramakrishna Balachandar
金额：
$ 29.72万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2024-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134083&HistoricalAwards=false
关键词：
EAGER Accurate Estimation Indoor Airborne

项目摘要

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.

病毒性疾病的空气传播被认为是一个重要的传播方式。已经研究了这种传播的许多方面，包括人体在室内空间呼气事件中携带病毒的飞沫的喷射、蒸发和扩散。然而，需要开发一种基于科学的框架，能够快速可靠地预测室内空间空气传染的传播。这样一个框架将有助于评估教室、餐馆、电梯、飞机机舱等的传染风险，并为决策者提供信息。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，供研究人员、决策者、管理人员和公众使用，以便就室内空间病毒传染的风险做出知情决定。有许多重要的参数影响空气传染的传播，而问题固有的非线性性质使得简单的预测是不可能的。该项目将开发一个多尺度、数据驱动的框架，用于快速、准确地预测密闭空间中空气传播的传染病。在这个框架的发展过程中有两个关键的创新。第一种方法是将整个问题分为病毒规模、源头规模（呼吸、说话、咳嗽或打喷嚏）和房间规模的关键组成部分。第二种方法是在处理传染的个别情况之前，首先生成一个粒子扩散信息的大型数据库，从而扭转问题。这两项创新允许一些高保真模拟来探索室内病毒传播的无数场景，而不需要对每个单独的场景进行单独的计算密集型预测。该框架以及快速获取室内空间流动信息的能力，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相关的不确定性。对这一框架和工具的改进应使其适用于其他空气传播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，以解决室外空间的传染风险，并可以量身定制，以解决涉及颗粒扩散的其他问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。