WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China.
Transbound Emerg Dis. 2022 Nov;69(6):3964-3971. doi: 10.1111/tbed.14673. Epub 2022 Aug 12.
Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml] day ) and 0.92 (95% CI:-0.09, 1.93) day , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.
病毒动力学参数指定的宿主内模型是理解感染患者体内 SARS-CoV-2 复制周期的主流工具。参数不确定性进一步影响模型的输出,例如潜在抗病毒药物的疗效。然而,收集这些参数的经验数据具有挑战性。在这里,我们旨在通过将模型校准到从呼吸道标本测量的病毒载量数据,对宿主内模型中使用的病毒动力学参数进行系统评价。我们在 PubMed、Embase 和 Web of Science 数据库中(2019 年 12 月 1 日至 2022 年 2 月 10 日)搜索了宿主内建模研究。我们从上述 9 项研究中确定了 7 项独立的宿主内模型,包括 I 型干扰素、先天反应、体液免疫反应或细胞介导的免疫反应。从这些模型中,我们提取并分析了 7 个广泛使用的病毒动力学参数,包括感染或症状发作时的病毒载量、感染易感细胞的病毒粒子率、感染细胞释放病毒的速率、病毒粒子清除率、感染细胞清除率和处于潜伏期的细胞成为有生产力感染的速率。我们从 9 项合格研究中确定了 7 项独立的宿主内模型。症状发作时的病毒载量为 4.78(95%CI:2.93,6.62)log(拷贝/ml),感染时的病毒载量为-1.00(95%CI:-1.94,-0.05)log(拷贝/ml)。感染易感细胞的病毒粒子率和感染细胞清除率的合并估计值分别为-6.96(95%CI:-7.66,-6.25)log([拷贝/ml]天)和 0.92(95%CI:-0.09,1.93)天。我们发现,当将模型类型作为分类变量包含在荟萃分析中时,感染细胞清除率与报告的模型相关(p<.01)。当对宿主内模型进行参数化时,已发表了联合病毒动力学参数估计值。审查后的病毒动力学参数可用于同一宿主内模型,以了解感染患者体内 SARS-CoV-2 的复制周期,并评估药物干预的影响。