Poster M8 - Mathematical Modeling of Immune Response to SARS-CoV-2

Introduction: In response to the profound impact the COVID-19 pandemic has had on society, the mathematics and broader scientific community has focused considerable research efforts to understand the spread of the virus. Despite a tremendous volume of research in this area, how the human immune system responds to SARS-CoV-2 has not been yet fully understood due to limited analysis of the experimental or clinical information to date. Mathematical models that account for the interaction between SARS-CoV-2 and the human immune system will improve the scientific community's ability to analyze the vast amount of data available. We develop a mathematical model for the immune response to SARS-CoV-2 to investigate the role of various molecular pathways in successful viral clearance and the key mechanisms responsible for disease severity exhibited by some patients. Specifically, our in-host model explicitly represents the interactions between the virus, innate immune cells, and selected cytokines. These interactions are formulated in a system of coupled ordinary and delayed differential equations. We conduct parameter estimation based on experimental data from literature and investigate model behaviors via computational simulations. Using this model, we use sensitivity analysis to determine the implications of variation of parameters. Our model demonstrates key aspects of the immune response to SARS-CoV-2, specifically its sensitive pathways, which might be responsible for disease severity exhibited by COVID-19 patients. This could be used to identify several therapeutic targets that would provide hypotheses to be tested clinically, thus, serving as a foundation for the development of evidence-based therapeutic strategies.

Materials and

Methods: A delayed ordinary differential equations model that explicitly represents the interactions between different immune cells, cytokines, and SARS-CoV-2 has been used for this research. The development of smaller sub-models using fewer cytokines and immune cells was also completed to ensure the accuracy of the full model. The model has been developed in MATLAB using experimental datasets to estimate the parameters and accurately depict interactions between cells and the virus. Mathematical techniques such as homeostasis equations, production post-infection and different time stamps to estimate final parameters are methodologies used to develop the model. Local and global sensitivity analyses were conducted to assess the validity of the parameters and general model.

Results, Conclusions, and Discussions: The effect of cytokine storm by TNF-Alpha and IFN Gamma is captured with multiple scenarios of slight decrease within their levels being the next step to simulate over the next months to provide possible therapeutic blueprints for further experimental research. We aim to delay certain IFN dynamics to also capture their effect on the immune response along with the enhancement of the IFN gamma pathway in the activation of Natural Killer cells. This will allow for a more robust understanding of the disease along with powerful future steps that experimental research is able to take.

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