This article was originally published here
Sci Rep. 2022 May 10;12(1):7666. doi: 10.1038/s41598-022-11816-2.
Respiratory viruses, including respiratory syncytial virus, influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cause serious and sometimes fatal illness in thousands of people each year. Understanding the dynamics of virus spread in the respiratory system is critical as new knowledge will increase our understanding of virus pathogenesis and allow infection patterns to be more predictable in vivo, improving our ability to target administration of vaccines and medications. This study presents a computer model of the spread of the virus in the respiratory tract network. The model includes the branch structure of the airway generation network, biophysical and infectivity properties of the virus, and airflow patterns that facilitate the circulation of virus particles. As a proof of principle, the model was applied to SARS-CoV-2 by integrating data on its replication cycle, as well as the density of cells expressing angiotensin converting enzyme along the airway network. By using real physiological data associated with factors such as respiratory rate, immune response and inhaled viral load, the model can improve our understanding of virus concentration and spatio-temporal dynamics. We collected experimental data from a number of studies and incorporated it into the model to show in silico how viral load propagates along the branches of the respiratory network.
PMID:35538182 | DOI:10.1038/s41598-022-11816-2