Modelling how the flu virus spreads within the respiratory tract
Historically, when flu season arrives, Canadians are urged to engage in prevention strategies such as getting the annual vaccine, frequent hand washing, and good coughing and sneezing practices. But despite the proven effectiveness of these measures in reducing transmission, there is still much to learn about how viral infections behave in the body. Christian Quirouette, a physics PhD candidate in the Complex Systems field at Ryerson University, is helping to advance our knowledge.
Together with co-authors and collaborators Dr. Catherine Beauchemin (Department of Physics, Ryerson), Nada Younis (Department of Physics, Ryerson) and Micaela Reddy (Array BioPharma Inc., Boulder, Colorado), Quirouette has developed a mathematical model that describes the course of an influenza virus infection by explicitly including how the virus moves through a person’s respiratory tract.
The human respiratory tract and the viral transport modes represented by the mathematical model.
“Normally, mathematical models like this assume the infection is spatially uniform,” says Quirouette, lead author of the study published in PLOS Computational Biology. “Uniformity means that the cells in your nose are exposed to the same amount of virus as those in your lower lungs. Our work is different. If you explicitly model how the virus moves around in the respiratory tract, cells in different parts can see very different amounts of virus. So the infection is not spatially uniform.”
The team focused its modelling on advection, which is the movement of virus particles from lower to higher regions of the respiratory tract upwards towards the nose and mouth, as the particles ride what is called the “mucus escalator.” That’s part of the defence mechanism of the respiratory tract: the mucus and its contents get pushed up the respiratory tract and out via the nose and mouth. The intermediate layer of liquid between the cells and the mucus layer, in which virus particles can be found, is then also subject to this upwards motion, and so virus particles are also driven upwards.
In their model with advection, the team found that cells need to produce more virus than in the typical, spatially uniform models in order to match the observed course of a flu infection in a patient. This suggests that the spatially uniform model likely underestimates the rate of virus production. Since producing more virus means an increased chance that a drug-resistant mutant virus can emerge, this has implications for antiviral therapy.
Infectious virus concentration as a function of depth within the human respiratory tract shown at specific days post infection (dpi).
“The model also predicts that advection prevents infection from reaching cells located below the depth at which virus first deposits,” adds Quirouette. “This means advection is an effective physiological mechanism to suppress infection, and the initial depth of virus deposition restricts what proportion of cells in the respiratory tract can be affected by the infection.”
Understanding where the infection localizes within a person’s respiratory tract has implications for the persistence of their immunity to the virus and for the optimal delivery of antiviral drugs.
“Our work is not specific to the influenza virus,” says Quirouette. “It can be applied to other respiratory viruses like SARS-CoV-2, the virus responsible for COVID-19. So when the time comes to optimize an antiviral drug or to mathematically model the localized immune response to the virus to better understand and control it, our mathematical model could become an important tool in correctly capturing these mechanisms and helping answer some important scientific questions.”
In providing greater understanding, the team’s mathematical model may contribute to more effective treatments for a range of respiratory viruses.
The research team’s work was funded in part by an NSERC Discovery Grant and an Early Researcher Award from the Ministry of Research and Innovation awarded to Dr. Beauchemin and by a Ryerson Graduate Fellowship and a Queen Elizabeth II Graduate Scholarship in Science and Technology (QEII-GSST) awarded to Quirouette.