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A Validated Mathematical Model of the Cytokine Release Syndrome in Severe COVID-19
Ruy Freitas Reis1,2, Alexandre Bittencourt Pigozzo3, Carla Rezende Barbosa Bonin4
1Institute of Exact Sciences, Department of Computing, Federal University of Juiz de Fora, Juiz de Fora, Brazil.
Abstract:
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.
Insights
This study models the immune response to SARS-CoV-2, the virus causing COVID-19. Mathematical simulations suggest the virus infects immune cells, leading to high cytokine production in non-survivors.
Area of Science:
- Immunology
- Computational Biology
- Epidemiology
Background:
- The COVID-19 pandemic, caused by SARS-CoV-2, has led to millions of deaths globally.
- Complex interactions between the SARS-CoV-2 pathogen and the human immune system remain poorly understood.
- Mathematical and computational approaches offer potential solutions for investigating these complexities.
Purpose of the Study:
- To develop a mathematical model simulating the immune response to SARS-CoV-2 infection.
- To investigate the hypothesis that SARS-CoV-2 infects immune cells, triggering excessive inflammatory cytokine production.
- To elucidate differences in immune response between COVID-19 survivors and non-survivors.
Main Methods:
- Development of a system comprising fifteen ordinary differential equations (ODEs).
- Utilizing the ODE system to simulate and analyze immune system dynamics during SARS-CoV-2 infection.
- Comparing simulation results to understand cytokine level variations.
Main Results:
- Simulation results support the hypothesis that SARS-CoV-2 infects immune cells.
- The model demonstrates a link between viral infection of immune cells and high-level production of inflammatory cytokines.
- The findings help explain the observed lower cytokine levels in COVID-19 survivors compared to non-survivors.
Conclusions:
- The mathematical model provides insights into the immune response to SARS-CoV-2.
- Infection of immune cells by SARS-CoV-2 is a plausible mechanism for severe COVID-19 symptoms.
- Computational modeling is a valuable tool for understanding complex diseases like COVID-19.
