Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
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.
Article activity feed
-
-
-
-
SciScore for 10.1101/2021.05.19.444569: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:SIMCoV overcomes these limitations by leveraging the data shared in response to the COVID-19 pandemic and an underlying architecture that takes advantage of HPC capabilities. The SIMCoV platform could be used to simulate other spatial interactions such as predator prey dynamics between immune and infected cells or collective action …
SciScore for 10.1101/2021.05.19.444569: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:SIMCoV overcomes these limitations by leveraging the data shared in response to the COVID-19 pandemic and an underlying architecture that takes advantage of HPC capabilities. The SIMCoV platform could be used to simulate other spatial interactions such as predator prey dynamics between immune and infected cells or collective action dynamics (11), like the collective search strategies of T cells (54). To enable open and reproducible science, SIMCoV is freely available under an open-source license, and it was designed to be easily extensible. Future extensions of the model could investigate the effects of mucus and the complex dynamics of airflow in the respiratory tract (55) with more detailed topological models of the airways and alveoli and the fraction and distribution of epithelial cells with receptors necessary for infection. SIMCoV could also incorporate additional aspects of the innate and adaptive immune response. Even without these features and with limited parameter tuning, SIMCoV reflects the patchiness of tissue damage in patient CT scans (Fig. 6) and predicts the time course of patient viral load (Fig. 5). In future work, we will initialize SIMCoV simulations with locations of tissue damage from CT scans to predict viral load, and potentially disease severity, in individual patients. By predicting the time course of viral loads within individuals, SIMCoV can help to identify factors that determine windows of transmission between individuals and thereby improve und...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-