Paper Info
Title
Y Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection
Abstract
Background: COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8(+) T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing. Objectives: In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease. Methods: Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8(+) T cells, NK cells, and also CD4(+) T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection. Results: The mathematical model that represents the data more appropriately is considering CD8(+) T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4(+) T cells did not contribute enough to explain the dynamics of the immune responses. Conclusions: Our computational results highlight that a low viral clearance rate by CD8(+) T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients. (C) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.cmpb.2021.106412
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Keywords
DocType
Volume
COVID-19, Disease severity, SARS-CoV-2, Immune response, ODEs, Mathematical modeling
Journal
211
ISSN
Citations 
PageRank 
0169-2607
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Rodolfo Blanco-Rodríguez100.34
Xin Du212726.78
E. A. Hernandez-Vargas34810.74