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Behavior of cough droplets emitted from Covid-19 patient in hospital isolation room with different ventilation configurations.
Dao, Huyen Thi; Kim, Kyo-Seon.
  • Dao HT; Department of Chemical Engineering, Kangwon National University, Chuncheon, Kangwon-do, 200-701, South Korea.
  • Kim KS; Department of Chemical Engineering, Kangwon National University, Chuncheon, Kangwon-do, 200-701, South Korea.
Build Environ ; 209: 108649, 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1568536
ABSTRACT
The world is now facing the Covid-19 pandemic and the control of Covid-19 spread in health care facilities is a serious concern. The ventilation system in hospital isolation rooms with infectious patients plays a significant role in minimizing the spread of viruses and the risk of infection in hospital. In this study, computational fluid dynamics (CFD) simulation is applied to investigate the important factors on transport and evaporation of multi-component cough droplets in the isolation room with different ventilation configurations. We analyzed the effects of various air outlet positions on the removal efficiency of infectious droplets in isolation room and proposed the optimum location of exhaust vent in hospital isolation room to maximize the droplet removal efficiencies. We found that the evaporation rate of droplets is strongly dependent on the relative humidity (RH) and, at low RH, the large-sized droplets with Covid-19 virus can evaporate quickly and become small-sized aerosols to stay in air for a long time and the Covid-19 can propagate more easily through the respiratory organs during breathing. It also explains why the Covid-19 can propagate faster in winter with low humidity than in summer with high humidity.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Long Covid Language: English Journal: Build Environ Year: 2022 Document Type: Article Affiliation country: J.buildenv.2021.108649

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Long Covid Language: English Journal: Build Environ Year: 2022 Document Type: Article Affiliation country: J.buildenv.2021.108649