Data-driven multi-scale mathematical modeling of SARS-CoV-2 infection reveals heterogeneity among COVID-19 patients

Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational...

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Bibliographic Details
Published in:PLoS computational biology 2021-11, Vol.17 (11), p.e1009587-e1009587
Main Authors: Wang, Shun, Hao, Mengqian, Pan, Zishu, Lei, Jinzhi, Zou, Xiufen
Format: Article
Language:English
Subjects:
Antiviral agents
Antiviral Agents - therapeutic use
Antiviral state
Biology and Life Sciences
Computational Biology
Computer applications
Computer Simulation
Coronaviruses
COVID-19
COVID-19 - etiology
COVID-19 - immunology
COVID-19 - therapy
Datasets
Differential equations
Disease Progression
Heterogeneity
Host Microbial Interactions - immunology
Host-virus relationships
Humans
Indexes (ratios)
Infections
Interferon
Interferon Type I - biosynthesis
Lymphocyte Activation
Lymphocytes
Lymphocytes T
Mathematical models
Medical treatment
Medicine and Health Sciences
Models, Immunological
Models, Statistical
Pandemics - statistics & numerical data
Patients
Prognosis
Progressions
Respiratory diseases
SARS-CoV-2 - immunology
SARS-CoV-2 - pathogenicity
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Severity of Illness Index
Signs and symptoms
Stochastic models
Stochasticity
T-Lymphocytes - immunology
Treatment Outcome
Viral diseases
Viral infections
Viruses
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Summary:Patients with coronavirus disease 2019 (COVID-19) often exhibit diverse disease progressions associated with various infectious ability, symptoms, and clinical treatments. To systematically and thoroughly understand the heterogeneous progression of COVID-19, we developed a multi-scale computational model to quantitatively understand the heterogeneous progression of COVID-19 patients infected with severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2). The model consists of intracellular viral dynamics, multicellular infection process, and immune responses, and was formulated using a combination of differential equations and stochastic modeling. By integrating multi-source clinical data with model analysis, we quantified individual heterogeneity using two indexes, i.e., the ratio of infected cells and incubation period. Specifically, our simulations revealed that increasing the host antiviral state or virus induced type I interferon (IFN) production rate can prolong the incubation period and postpone the transition from asymptomatic to symptomatic outcomes. We further identified the threshold dynamics of T cell exhaustion in the transition between mild-moderate and severe symptoms, and that patients with severe symptoms exhibited a lack of naïve T cells at a late stage. In addition, we quantified the efficacy of treating COVID-19 patients and investigated the effects of various therapeutic strategies. Simulations results suggested that single antiviral therapy is sufficient for moderate patients, while combination therapies and prevention of T cell exhaustion are needed for severe patients. These results highlight the critical roles of IFN and T cell responses in regulating the stage transition during COVID-19 progression. Our study reveals a quantitative relationship underpinning the heterogeneity of transition stage during COVID-19 progression and can provide a potential guidance for personalized therapy in COVID-19 patients.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009587