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Duration of SARS-CoV-2 viral shedding in faeces as a parameter for wastewater-based epidemiology: Re-analysis of patient data using a shedding dynamics model

机译:粪便中SARS-COV-2病毒脱落的持续时间作为废水的流行病学参数:使用脱落动力学模型重新分析患者数据

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Wastewater-based epidemiology (WBE) is one of the most promising approaches to effectively monitor the spread of COVID-19. The virus concentration in faeces and its temporal variations are essential information for WBE. While some clinical studies have reported SARS-CoV-2 concentrations in faeces, the value varies amongst patients and changes over time. The present study aimed to examine how the temporal variations in the concentration of virus in faeces affect the monitoring of disease incidence. We reanalysed the experimental findings of clinical studies to estimate the duration of virus shedding and the faecal virus concentration. Available experimental data as of 23 October 2020 were collected. The viral shedding kinetics was modelled, and the dynamic model was fitted to the collected data by a Bayesian framework. Using posterior distributions, the duration of viral shedding and the concentration of virus copies in faeces over time were computed. We estimated the median concentration of SARS-CoV-2 in faeces as 3.4 (95% CrI: 0.24-6.5) log copies per gram-faeces over the shedding period, and our model implied that the duration of viral shedding was 26.0 days (95% CrI: 21.7-34.9), given the current standard quantification limit (Ct = 40). With simulated incidences, our results also indicated that a one-week delay between symptom onset and wastewater sampling increased the estimation of incidence by a factor of 17.2 (i.e., 10~(1.24) times higher). Our results demonstrated that the temporal variation in virus concentration in faeces affects microbial monitoring systems such as WBE. The present study also implied the need for adjusting the estimates of virus concentration in faeces by incorporating the kinetics of unobserved concentrations. The method used in this study is easily implemented in further simulations; therefore, the results of this study might contribute to enhancing disease surveillance and risk assessments that require quantities of virus to be excreted into the environment.
机译:废水的流行病学(WBE)是有效监测Covid-19传播的最有希望的方法之一。粪便中的病毒浓度及其时间变化是WBE的基本信息。虽然一些临床研究报告了粪便中的SARS-COV-2浓度,但该价值在患者之间变化并随着时间的变化而变化。本研究旨在探讨粪便中病毒浓度的时间变化如何影响疾病发病率的监测。我们重新安排了临床研究的实验结果,以估计病毒脱落的持续时间和粪便病毒浓度。收集了2020年10月23日的可用实验数据。虚拟病毒脱落动力学是模型的,动态模型由贝叶斯框架安装到收集的数据上。计算使用后分布,计算病毒脱落的持续时间和随时间越随粪便中的病毒拷贝的浓度。我们估计粪便中SARS-COV-2中的中值浓度为3.4(95%CRI:0.24-6.5)对脱落期的每克粪便副本,我们的模型暗示了病毒脱落的持续时间为26.0天(95鉴于当前标准量化极限(CT = 40),%CRI:21.7-34.9)。通过模拟发病率,我们的结果还表明症状发作和废水取样之间的一周延迟将估计增加17.2(即10〜(1.24)倍)。我们的研究结果表明,粪便中病毒浓度的时间变化会影响诸如WBE等微生物监测系统。本研究还暗示了需要通过纳入不可观察的浓度的动力学来调节粪便中病毒浓度的估计。本研究中使用的方法在进一步的模拟中容易实现;因此,该研究的结果可能有助于提高疾病监测和风险评估,要求在环境中排泄量的病毒。

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