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Modelling the direct virus exposure risk associated with respiratory events.
Wang, Jietuo; Dalla Barba, Federico; Roccon, Alessio; Sardina, Gaetano; Soldati, Alfredo; Picano, Francesco.
  • Wang J; Centro di Ateneo di Studi e Attività Spaziali - CISAS, University of Padova, Padova 35131, Italy.
  • Dalla Barba F; Department of Industrial Engineering, University of Padova, Padova 35131, Italy.
  • Roccon A; Institute of Fluid Mechanics and Heat Transfer, TU Wien, Vienna 1060, Austria.
  • Sardina G; Polytechnic Department, University of Udine, 33100 Udine, Italy.
  • Soldati A; Department of Mechanics and Maritime Sciences, Chalmers University of Technology, 41296 Gothenburg, Sweden.
  • Picano F; Institute of Fluid Mechanics and Heat Transfer, TU Wien, Vienna 1060, Austria.
J R Soc Interface ; 19(186): 20210819, 2022 01.
Article in English | MEDLINE | ID: covidwho-1621730
ABSTRACT
The outbreak of the COVID-19 pandemic highlighted the importance of accurately modelling the pathogen transmission via droplets and aerosols emitted while speaking, coughing and sneezing. In this work, we present an effective model for assessing the direct contagion risk associated with these pathogen-laden droplets. In particular, using the most recent studies on multi-phase flow physics, we develop an effective yet simple framework capable of predicting the infection risk associated with different respiratory activities in different ambient conditions. We start by describing the mathematical framework and benchmarking the model predictions against well-assessed literature results. Then, we provide a systematic assessment of the effects of physical distancing and face coverings on the direct infection risk. The present results indicate that the risk of infection is vastly impacted by the ambient conditions and the type of respiratory activity, suggesting the non-existence of a universal safe distance. Meanwhile, wearing face masks provides excellent protection, effectively limiting the transmission of pathogens even at short physical distances, i.e. 1 m.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Prognostic study / Systematic review/Meta Analysis Limits: Humans Language: English Journal: J R Soc Interface Year: 2022 Document Type: Article Affiliation country: RSIF.2021.0819

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Prognostic study / Systematic review/Meta Analysis Limits: Humans Language: English Journal: J R Soc Interface Year: 2022 Document Type: Article Affiliation country: RSIF.2021.0819