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Darquenne C, Borojeni AA, Colebank MJ, Forest MG, Madas BG, Tawhai M, Jiang Y. Aerosol Transport Modeling: The Key Link Between Lung Infections of Individuals and Populations. Front Physiol 2022; 13:923945. [PMID: 35795643 PMCID: PMC9251577 DOI: 10.3389/fphys.2022.923945] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 05/24/2022] [Indexed: 12/18/2022] Open
Abstract
The recent COVID-19 pandemic has propelled the field of aerosol science to the forefront, particularly the central role of virus-laden respiratory droplets and aerosols. The pandemic has also highlighted the critical need, and value for, an information bridge between epidemiological models (that inform policymakers to develop public health responses) and within-host models (that inform the public and health care providers how individuals develop respiratory infections). Here, we review existing data and models of generation of respiratory droplets and aerosols, their exhalation and inhalation, and the fate of infectious droplet transport and deposition throughout the respiratory tract. We then articulate how aerosol transport modeling can serve as a bridge between and guide calibration of within-host and epidemiological models, forming a comprehensive tool to formulate and test hypotheses about respiratory tract exposure and infection within and between individuals.
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Affiliation(s)
- Chantal Darquenne
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
- *Correspondence: Chantal Darquenne,
| | - Azadeh A.T. Borojeni
- Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Mitchel J. Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center and Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - M. Gregory Forest
- Departments of Mathematics, Applied Physical Sciences, and Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Balázs G. Madas
- Environmental Physics Department, Centre for Energy Research, Budapest, Hungary
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Yi Jiang
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, United States
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Chen A, Wessler T, Daftari K, Hinton K, Boucher RC, Pickles R, Freeman R, Lai SK, Forest MG. Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection. Biophys J 2022; 121:1619-1631. [PMID: 35378080 PMCID: PMC8975607 DOI: 10.1016/j.bpj.2022.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/27/2021] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.
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Affiliation(s)
- Alexander Chen
- Department of Mathematics, CSU Dominguez Hills, Carson, California
| | - Timothy Wessler
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina.
| | - Katherine Daftari
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Kameryn Hinton
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Richard C Boucher
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Raymond Pickles
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Ronit Freeman
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Samuel K Lai
- Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina; Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, UNC Chapel Hill, Chapel Hill, North Carolina
| | - M Gregory Forest
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina.
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