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Dynamic causal modelling of immune heterogeneity
Thomas Parr1, Anjali Bhat2, Peter Zeidman2
1Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, London, UK. thomas.parr.12@ucl.ac.uk.
Scientific Reports
|June 1, 2021
Summary
This study models the immune response to viruses, suggesting some individuals may be inherently resistant to infection. The model aids in understanding resistance mechanisms and developing new immunological assays for vaccines.
Area of Science:
- Immunology
- Epidemiological Modeling
- Computational Biology
Background:
- COVID-19 epidemiological models suggest inherent resistance to infection in a proportion of the population.
- Understanding the mechanisms of viral resistance is crucial for public health and vaccine development.
Purpose of the Study:
- To introduce a mathematical model of the immune response to viruses using mean-field dynamics.
- To formalize and simulate hypotheses regarding mechanisms of viral resistance.
- To demonstrate the application of the model in developing novel immunological assays.
Main Methods:
- Developed a mean-field dynamics model focusing on virus, B-lymphocytes, T-lymphocytes, and antibodies.
- Simulated key hypotheses: attenuated viral entry, pre-existing humoral immunity, and enhanced T-cell immunity.
- Applied variational inversion with simulated data for hypothesis testing.
Main Results:
- Simulations illustrated how specific immune response dynamics are altered by different resistance hypotheses.
- Demonstrated variational inversion for quantitative measurement of latent immunological responses.
- Showcased Bayes optimal classification of immunological response types.
Conclusions:
- The developed model provides a framework for understanding viral resistance mechanisms.
- The model can be adapted to create a fast, efficient immunological assay based on sequential serology.
- This approach may be particularly valuable for assessing SARS-CoV-2 vaccines and other viral threats.
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