COVID-19 immunotherapy: a mathematical model

Author:

Tavares J N1,Gomes Emilie123

Affiliation:

1. Faculty of Sciences, University of Porto , Portugal

2. Center of Mathematics, University of Porto

3. Statistics, Modeling and Computing Applications Office

Abstract

Abstract The pandemic caused by SARS-CoV-2 is responsible for a terrible health devastation with profoundly harmful consequences for the economic, social and political activities of communities on a global scale. Extraordinary efforts have been made by the world scientific community, who, in solidarity, shared knowledge so that effective vaccines could be produced quickly. However, it is still important to study therapies that can reduce the risk, until group immunity is reached, which, globally, will take a time that is still difficult to predict. On the other hand, the immunity time guaranteed by already approved vaccines is still uncertain. The current study proposes a therapy whose foundation lies in the important role that innate immunity may have, by preventing the disease from progressing to the acute phase that may eventually lead to the patient’s death. Our focus is on natural killer (NK) cells and their relevant role. NKs are considered the primary defence lymphocytes against virus-infected cells. They play a critical role in modulating the immune system. Preliminary studies in COVID-19 patients with severe disease suggest a reduction in the number and function of NK cells, resulting in decreased clearance of infected and activated cells and unchecked elevation of inflammation markers that damage tissue. SARS-CoV-2 infection distorts the immune response towards a highly inflammatory phenotype. Restoring the effector functions of NK cells has the potential to correct the delicate immune balance needed to effectively overcome SARS-CoV-2 infection.

Publisher

Oxford University Press (OUP)

Subject

Applied Mathematics,Pharmacology,General Environmental Science,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine,General Neuroscience

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