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Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments.

Abstract
The role of aerosolized SARS-CoV-2 viruses in airborne transmission of COVID-19 has been debated. The aerosols are transmitted through breathing and vocalization by infectious subjects. Some authors state that this represents the dominant route of spreading, while others dismiss the option. Here we present an adjustable algorithm to estimate the infection risk for different indoor environments, constrained by published data of human aerosol emissions, SARS-CoV-2 viral loads, infective dose and other parameters. We evaluate typical indoor settings such as an office, a classroom, choir practice, and a reception/party. Our results suggest that aerosols from highly infective subjects can effectively transmit COVID-19 in indoor environments. This "highly infective" category represents approximately 20% of the patients who tested positive for SARS-CoV-2. We find that "super infective" subjects, representing the top 5-10% of subjects with a positive test, plus an unknown fraction of less-but still highly infective, high aerosol-emitting subjects-may cause COVID-19 clusters (>10 infections). In general, active room ventilation and the ubiquitous wearing of face masks (i.e., by all subjects) may reduce the individual infection risk by a factor of five to ten, similar to high-volume, high-efficiency particulate air (HEPA) filtering. A particularly effective mitigation measure is the use of high-quality masks, which can drastically reduce the indoor infection risk through aerosols.
AuthorsJos Lelieveld, Frank Helleis, Stephan Borrmann, Yafang Cheng, Frank Drewnick, Gerald Haug, Thomas Klimach, Jean Sciare, Hang Su, Ulrich Pöschl
JournalInternational journal of environmental research and public health (Int J Environ Res Public Health) Vol. 17 Issue 21 (Nov 03 2020) ISSN: 1660-4601 [Electronic] Switzerland
PMID33153155 (Publication Type: Journal Article)
Chemical References
  • Aerosols
Topics
  • Aerosols
  • Air Microbiology
  • Algorithms
  • Betacoronavirus
  • COVID-19
  • Coronavirus Infections (prevention & control, transmission)
  • Filtration
  • Humans
  • Masks
  • Models, Theoretical
  • Pandemics (prevention & control)
  • Pneumonia, Viral (prevention & control, transmission)
  • SARS-CoV-2
  • Ventilation

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