Predicting the infection probability distribution of airborne and droplet transmissions

Miguel Yamamoto*, Akihiro Kawamura, Shin Ichi Tanabe, Satoshi Hori

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Herein, a method is proposed to predict the infection probability distribution rather than the room-averaged value. The infection probability is predicted by considering both airborne and droplet transmissions based on CO2 concentration and the position of the occupants in a room. The proposed method was used in an actual office setting, and the results confirmed that it could provide a quantitative prediction of the infection probability by integrating the ventilation efficiency and the distance between occupants (i.e. social distancing). We verified the ability of the method to analyse the relative effectiveness of countermeasures for airborne and droplet transmissions. The proposed strategies can be implemented by a facility manager and can enable facility users to check the infection probability distribution in real-time to select a seat with the minimum risk of infection.

Original languageEnglish
JournalIndoor and Built Environment
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • CO concentration
  • COVID-19
  • Wells–Riley model
  • local positioning system
  • social distancing
  • ventilation

ASJC Scopus subject areas

  • Building and Construction
  • Public Health, Environmental and Occupational Health

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