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.

中文翻译：

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数据驱动的 SARS-CoV-2 感染多尺度数学模型揭示了 COVID-19 患者之间的异质性。

2019 年冠状病毒病 (COVID-19) 患者通常表现出与各种感染能力、症状和临床治疗相关的多种疾病进展。为了系统和彻底地了解 COVID-19 的异质性进展，我们开发了一个多尺度计算模型来定量了解感染了严重急性呼吸系统综合症 (SARS) 样冠状病毒 (SARS-CoV-2) 的 COVID-19 患者的异质性进展）。该模型由细胞内病毒动力学、多细胞感染过程和免疫反应组成，并使用微分方程和随机建模的组合来制定。通过将多源临床数据与模型分析相结合，我们使用两个指标来量化个体异质性，即感染细胞的比例和潜伏期。具体来说，我们的模拟表明，增加宿主抗病毒状态或病毒诱导的 I 型干扰素 (IFN) 产生率可以延长潜伏期并推迟从无症状到有症状的转变。我们进一步确定了在轻度-中度和重度症状之间的过渡中 T 细胞耗竭的阈值动态，并且具有严重症状的患者在晚期表现出缺乏幼稚 T 细胞。此外，我们量化了治疗 COVID-19 患者的疗效，并研究了各种治疗策略的效果。模拟结果表明，单次抗病毒治疗对中度患者就足够了，而对重度患者则需要联合治疗和预防 T 细胞耗竭。这些结果突出了 IFN 和 T 细胞反应在调节 COVID-19 进展过程中的阶段转变中的关键作用。我们的研究揭示了支持 COVID-19 进展过程中过渡阶段异质性的定量关系，可以为 COVID-19 患者的个性化治疗提供潜在指导。