Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2
Abstract Respiratory viruses including Respiratory Syncytial Virus, influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause serious and sometimes fatal disease in thousands of people annually. Understanding virus propagation dynamics within the respiratory system is cr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-11816-2 |
| _version_ | 1811253186821881856 |
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| author | Dixon Vimalajeewa Sasitharan Balasubramaniam Donagh P. Berry Gerald Barry |
| author_facet | Dixon Vimalajeewa Sasitharan Balasubramaniam Donagh P. Berry Gerald Barry |
| author_sort | Dixon Vimalajeewa |
| collection | DOAJ |
| description | Abstract Respiratory viruses including Respiratory Syncytial Virus, influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause serious and sometimes fatal disease in thousands of people annually. Understanding virus propagation dynamics within the respiratory system is critical because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance our ability to target vaccine and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. As a proof of principle, the model was applied to SARS-CoV-2 by integrating data about its replication-cycle, as well as the density of Angiotensin Converting Enzyme expressing cells along the respiratory tract network. Using real-world physiological data associated with factors such as the respiratory rate, the immune response and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of the virus. We collected experimental data from a number of studies and integrated them with the model in order to show in silico how the virus load propagates along the respiratory network branches. |
| first_indexed | 2024-04-12T16:47:11Z |
| format | Article |
| id | doaj.art-81f9f35043ce44b4a977b2f66fb258dd |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T16:47:11Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-81f9f35043ce44b4a977b2f66fb258dd2022-12-22T03:24:33ZengNature PortfolioScientific Reports2045-23222022-05-0112111410.1038/s41598-022-11816-2Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2Dixon Vimalajeewa0Sasitharan Balasubramaniam1Donagh P. Berry2Gerald Barry3Department of Statistics, Texas A & M UniversitySchool of Computing, University of NebraskaTeagasc, Animal & Grassland Research and Innovation CenterSchool of Veterinary Medicine, University College DublinAbstract Respiratory viruses including Respiratory Syncytial Virus, influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause serious and sometimes fatal disease in thousands of people annually. Understanding virus propagation dynamics within the respiratory system is critical because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance our ability to target vaccine and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. As a proof of principle, the model was applied to SARS-CoV-2 by integrating data about its replication-cycle, as well as the density of Angiotensin Converting Enzyme expressing cells along the respiratory tract network. Using real-world physiological data associated with factors such as the respiratory rate, the immune response and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of the virus. We collected experimental data from a number of studies and integrated them with the model in order to show in silico how the virus load propagates along the respiratory network branches.https://doi.org/10.1038/s41598-022-11816-2 |
| spellingShingle | Dixon Vimalajeewa Sasitharan Balasubramaniam Donagh P. Berry Gerald Barry Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 Scientific Reports |
| title | Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 |
| title_full | Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 |
| title_fullStr | Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 |
| title_full_unstemmed | Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 |
| title_short | Virus particle propagation and infectivity along the respiratory tract and a case study for SARS-CoV-2 |
| title_sort | virus particle propagation and infectivity along the respiratory tract and a case study for sars cov 2 |
| url | https://doi.org/10.1038/s41598-022-11816-2 |
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