ArticleFrontiers in immunology2021
Divergent COVID-19 Disease Trajectories Predicted by a DAMP-Centered Immune Network Model.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Using In silico models to predict lymphocyte activation and development in a data rich era.Immunoinformatics (Amsterdam, Netherlands) · 2024Article
- A multiscale inflammatory map: linking individual stress to societal dysfunction.Frontiers in science · 2024Article
- Article
- A mathematical model of SARS-CoV-2 immunity predicts paxlovid rebound.Journal of medical virology · 2023Article
- Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins.Trends in immunology · 2023Review
- Predicting severity in COVID-19 disease using sepsis blood gene expression signatures.Scientific reports · 2023Article
- Early alveolar epithelial cell necrosis is a potential driver of COVID-19-induced acute respiratory distress syndrome.iScience · 2023Article
- Pathophysiology and clinical management of coronavirus disease (COVID-19): a mini-review.Frontiers in immunology · 2023Review
- What's next for computational systems biology?Frontiers in systems biology · 2023Review
- Considering innate immune responses in SARS-CoV-2 infection and COVID-19.Nature reviews. Immunology · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed "patient archetypes", whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.
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