Modelling of human exhaled sprays and aerosols to enable real-time estimation of spatially-resolved infection risk in indoor environments

TL;DR

This paper presents a real-time computational framework combining fluid dynamics and aerosol dispersion modeling to estimate infection risks from airborne diseases in indoor environments, aiding safety management.

Contribution

It introduces a novel, open-source, automated model that predicts aerosol dispersion and infection risk in indoor spaces in real time, integrating ventilation patterns.

Findings

Predicts viral aerosol dispersion and concentration.

Provides spatially-resolved infection risk estimates.

Supports indoor safety management decisions.

Abstract

A numerical framework for the 'real-time' estimation of the infection risk from airborne diseases (e.g., SARS-CoV-2) in indoor spaces such as hospitals, restaurants, cinemas or teaching rooms is proposed. The developed model is based on the use of computational fluid dynamics as a pre-processor to obtain the time-averaged ventilation pattern inside a room, and a post-processing tool for the computation of the dispersion of sprays and aerosols emitted by its occupants in 'real time'. The model can predict the dispersion and concentration of droplets carrying viable viral copies in the air, the contamination of surfaces, and the related spatially-resolved infection risk. It may therefore provide useful information for the management of indoor environments in terms of, e.g., maximum occupancy, air changes per hour and cleaning of surfaces. This work describes the fundamentals of the model and its main characteristics. The model was developed using open-source software and is conceived to be simple, user-friendly and highly automated to enable any potential user to perform estimations of the local infection risk.