By June 2021, a new contagious disease, the Coronavirus disease 2019 (COVID-19), has infected more than 172 million people worldwide, causing more than 3.7 million deaths. Many aspects related to the interactions of the disease’s causative agent, SAR2-CoV-2, and the immune response are not well understood: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. Mathematical and computational tools can help researchers to answer these open questions about the disease. In this work, we present a system of fifteen ordinary differential equations that models the immune response to SARS-CoV-2. The model is used to investigate the hypothesis that the SARS-CoV-2 infects immune cells and, for this reason, induces high-level productions of inflammatory cytokines. Simulation results support this hypothesis and further explain why survivors have lower levels of cytokines levels than non-survivors.

By November 2020, the Coronavirus disease 2019 has infected more than 50 million people worldwide, causing more than 1.2 million deaths. This new contagious disease is not well understood, and the scientific community is trying to comprehend better the interactions of the causative agent of the disease, SAR2-CoV-2, and the immune response to identify its weak points to develop new therapies to impair its lethal effects. Mathematical and computational tools can help in this task: the multiscale interactions among the various components of the human immune system and the pathogen are very complex. In this work, we present a simple system of five ordinary differential equations that can be used to model the immune response to SARS-CoV-2. The model parameters and initial conditions were adjusted to cohort studies that collected viremia and antibody data. The results have shown that the model was able to reproduce both viremia and antibodies dynamics successfully.

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