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Quantifying the COVID19 infection risk due to droplet/aerosol inhalation.
Bale, Rahul; Iida, Akiyoshi; Yamakawa, Masashi; Li, ChungGang; Tsubokura, Makoto.
  • Bale R; RIKEN Center for Computational Science, Kobe, 6500047, Japan. rahul.bale@riken.jp.
  • Iida A; Graduate School of System Informatics, Department of Computational Science, Kobe University, Kobe, Japan. rahul.bale@riken.jp.
  • Yamakawa M; Department of Mechanical Engineering, Toyohashi Institute of Technology, Toyohashi, Japan.
  • Li C; Department of Mechanical Engineering, Kyoto Institute of Technology, Kyoto, Japan.
  • Tsubokura M; RIKEN Center for Computational Science, Kobe, 6500047, Japan.
Sci Rep ; 12(1): 11186, 2022 07 01.
Article in English | MEDLINE | ID: covidwho-1972641
ABSTRACT
The dose-response model has been widely used for quantifying the risk of infection of airborne diseases like COVID-19. The model has been used in the room-average analysis of infection risk and analysis using passive scalars as a proxy for aerosol transport. However, it has not been employed for risk estimation in numerical simulations of droplet dispersion. In this work, we develop a framework for the evaluation of the probability of infection in droplet dispersion simulations using the dose-response model. We introduce a version of the model that can incorporate the higher transmissibility of variant strains of SARS-CoV2 and the effect of vaccination in evaluating the probability of infection. Numerical simulations of droplet dispersion during speech are carried out to investigate the infection risk over space and time using the model. The advantage of droplet dispersion simulations for risk evaluation is demonstrated through the analysis of the effect of ambient wind, humidity on infection risk, and through a comparison with risk evaluation based on passive scalars as a proxy for aerosol transport.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Infections Type of study: Experimental Studies / Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-14862-y

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Infections Type of study: Experimental Studies / Prognostic study Topics: Vaccines / Variants Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-14862-y