Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

Author:

Moses Melanie E.ORCID,Hofmeyr Steven,Cannon Judy L.ORCID,Andrews AkilORCID,Gridley Rebekah,Hinga Monica,Leyba KirtusORCID,Pribisova AbigailORCID,Surjadidjaja Vanessa,Tasnim HumayraORCID,Forrest StephanieORCID

Abstract

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

Funder

National Science Foundation

Defense Advanced Research Projects Agency

Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence

Office of Science

U.S. Department of Energy

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

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