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| LEADER |
01733nam a2200181Ia 4500 |
| 001 |
10.1038-s41598-022-14862-y |
| 008 |
220718s2022 CNT 000 0 und d |
| 020 |
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|a 20452322 (ISSN)
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| 245 |
1 |
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|a Quantifying the COVID19 infection risk due to droplet/aerosol inhalation
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| 260 |
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|b Nature Research
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.1038/s41598-022-14862-y
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| 520 |
3 |
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|a 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. © 2022, The Author(s).
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| 700 |
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|a Bale, R.
|e author
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1 |
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|a Iida, A.
|e author
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| 700 |
1 |
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|a Li, C.G.
|e author
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| 700 |
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|a Tsubokura, M.
|e author
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| 700 |
1 |
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|a Yamakawa, M.
|e author
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| 773 |
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|t Scientific Reports
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