Quantitative risk assessment of COVID-19 aerosol transmission indoors: a mechanistic stochastic web application.

Journal Article (Journal Article)

An increasing body of literature suggests that aerosol inhalation plays a primary role in COVID-19 transmission, particularly in indoor settings. Mechanistic stochastic models can help public health professionals, engineers, and space planners understand the risk of aerosol transmission of COVID-19 to mitigate it. We developed such model and a user-friendly web application to meet the need of accessible risk assessment tools during the COVID-19 pandemic. We built our model based on the Wells-Riley model of respiratory disease transmission, using quanta emission rates obtained from COVID-19 outbreak investigations. In this report, three modelled scenarios were evaluated and compared to epidemiological studies looking at similar settings: classrooms, weddings, and heavy exercise sessions. We found that the risk of long-range aerosol transmission increased 309-332% when people were not wearing masks, and 424-488% when the room was poorly ventilated in addition to no masks being worn across the scenarios. Also, the risk of transmission could be reduced by ∼40-60% with ventilation rates of 5 ACH for 1-4 h exposure events, and ∼70% with ventilation rates of 10 ACH for 4 h exposure events. Relative humidity reduced the risk of infection (inducing viral inactivation) by a maximum of ∼40% in a 4 h exposure event at 70% RH compared to a dryer indoor environment with 25% RH. Our web application has been used by more than 1000 people in 52 countries as of September 1st, 2021. Future work is needed to obtain SARS-CoV-2 dose-response functions for more accurate risk estimates.

Full Text

Duke Authors

Cited Authors

  • Rocha-Melogno, L; Crank, K; Bergin, MH; Gray, GC; Bibby, K; Deshusses, MA

Published Date

  • November 13, 2021

Published In

Start / End Page

  • 1 - 12

PubMed ID

  • 34726128

Electronic International Standard Serial Number (EISSN)

  • 1479-487X

International Standard Serial Number (ISSN)

  • 0959-3330

Digital Object Identifier (DOI)

  • 10.1080/09593330.2021.1998228

Language

  • eng