(A-10) MATHEMATICAL MODELING OF IMMUNE RESPONSE TO SARS-CoV-2.

Introduction:: The COVID-19 virus has had a tremendous impact on our society. While there are vaccines that have been developed and are in development, our knowledge of the disease and its interaction with the human immune system is still limited. With the evolution of new strains of SARS-CoV-2 our existing treatment methods will become invalidated. To advance our understanding of the SARS-CoV-2, we developed a mathematical model that represents the human immune response to SARS-CoV-2 virus, and the necessary cytokines that play a role in the quality of the immune response.

Materials and Methods:: We developed a mathematical model that explicitly represents the interactions between different immune cells, cytokines, and SARS-CoV-2. We conducted a Local and Global Sensitivity analysis in MATLAB and Python, respectively, to investigate each parameter's effect in our model. Extensive literature review was conducted to develop the full model to include more immune cells and cytokines. We then estimated the remaining parameters by sub-modeling against datasets from literature in MATLAB.


Results, Conclusions, and Discussions:: The sensitivity analysis shows that Smax (maximum number of lung cells) and β (viral infection rate) are the two most important parameters of the model in terms of the overall effect on the lung epithelial cells. Secondary drivers of the model can be attributed to λS (growth of new cells), dI (death of infected cells), and dV (death of virus particles). We are currently analyzing the cytokine storm effect and the impact of neutralizing the TNF and IFN2 as a possible treatment methods. We are also currently developing an agent based model to capture the spate-temporal aspect of the immune response.

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