Modelling airborne transmission of SARS-CoV-2 at a local scale.
The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This informatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0273820 |
| _version_ | 1811260675898474496 |
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| author | Simon Rahn Marion Gödel Gerta Köster Gesine Hofinger |
| author_facet | Simon Rahn Marion Gödel Gerta Köster Gesine Hofinger |
| author_sort | Simon Rahn |
| collection | DOAJ |
| description | The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a 'high risk' benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases. |
| first_indexed | 2024-04-12T18:51:11Z |
| format | Article |
| id | doaj.art-836483c8f83c46899874570797297316 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T18:51:11Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-836483c8f83c468998745707972973162022-12-22T03:20:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027382010.1371/journal.pone.0273820Modelling airborne transmission of SARS-CoV-2 at a local scale.Simon RahnMarion GödelGerta KösterGesine HofingerThe coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a 'high risk' benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.https://doi.org/10.1371/journal.pone.0273820 |
| spellingShingle | Simon Rahn Marion Gödel Gerta Köster Gesine Hofinger Modelling airborne transmission of SARS-CoV-2 at a local scale. PLoS ONE |
| title | Modelling airborne transmission of SARS-CoV-2 at a local scale. |
| title_full | Modelling airborne transmission of SARS-CoV-2 at a local scale. |
| title_fullStr | Modelling airborne transmission of SARS-CoV-2 at a local scale. |
| title_full_unstemmed | Modelling airborne transmission of SARS-CoV-2 at a local scale. |
| title_short | Modelling airborne transmission of SARS-CoV-2 at a local scale. |
| title_sort | modelling airborne transmission of sars cov 2 at a local scale |
| url | https://doi.org/10.1371/journal.pone.0273820 |
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