A Physics Modeling Study of SARS-CoV-2 Transport in Air

TL;DR

This study models SARS-CoV-2 aerosol transport using a physics analogy, revealing how puff dynamics influence infection risk distances and aerosol suspension times in the air.

Contribution

It introduces a novel physics-based puff model to analyze aerosol transport and provides quantitative insights into infection spread distances.

Findings

Puff stopping range is proportional to puff diameter and density.

Temperature variations can alter the stopping range by about 8%.

Aerosols can remain suspended for hours, affecting transmission risk.

Abstract

The health threat from SARS-CoV-2 airborne infection has become a public emergency of international concern. During the ongoing coronavirus pandemic, people have been advised by the Centers for Disease Control and Prevention to maintain social distancing of at least 2 m to limit the risk of exposure to the coronavirus. Experimental data, however, show that infected aerosols and droplets trapped inside a turbulent puff cloud can travel up to 7 to 8 m. We propose a nuclear physics analogy-based modeling of the complex gas cloud and its payload of pathogen-virions. We show that the cloud stopping range is proportional to the product of the puff's diameter and its density. We use our puff model to determine the average density of the buoyant fluid in the turbulent cloud. A fit to the experimental data yields $1.8 < \rho_P/\rho_{\rm air} < 4.0$, where $\rho_P$ and $\rho_{\rm air}$ are the average density of the puff and the air. We demonstrate that temperature variation could cause an ${\cal O}(\pm 8\%)$ effect in the puff stopping range for extreme ambient cold or warmth. We also demonstrate that aerosols and droplets can remain suspended for hours in the air. Therefore, once the puff slows down sufficiently, and its coherence is lost, the eventual spreading of the infected aerosols becomes dependent on the ambient air currents and turbulence.