COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease.

Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection.

A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.

COVID-19 大流行凸显了新出现的病原体对人类健康的主要威胁。为了应对这种严重的疾病，迫切需要开发定量方法来加深对当前疫情的理解。

考虑到感染开始时间的不同，提出了数学模型来代表感染患者中的 SARS-CoV-2 动态。基于靶细胞有限模型，SARS-CoV-2的宿主内繁殖数与人类流感感染的广泛值一致。拟合数据的最佳模型包括免疫细胞反应，这表明免疫反应缓慢，在症状出现后 5 至 10 天内达到峰值。不支持具有蚀相阶段（即成为有效感染细胞之前的潜伏阶段）的模型。有趣的是，模型模拟预测 SARS-CoV-2 在感染后的最初几天内复制可能非常缓慢，并且在感染后的前 4 天内病毒载量可能低于检测水平。

对 SARS-CoV-2 动力学的定量理解和病毒感染标准参数的估计是这项开创性工作的关键贡献。这些模型可用于未来评估控制理论方法，以定制针对 COVID-19 的新药。