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

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.