Center for Marine Environmental Studies (CMES), Ehime University, 3 Bunkyo, Matsuyama, Ehime 790-8577, Japan.
Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, North 13 West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.
Sci Total Environ. 2021 May 15;769:144549. doi: 10.1016/j.scitotenv.2020.144549. Epub 2021 Jan 4.
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 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.
基于污水的流行病学(WBE)是有效监测 COVID-19 传播的最有前途的方法之一。粪便中的病毒浓度及其时间变化是 WBE 的重要信息。虽然一些临床研究报告了粪便中的 SARS-CoV-2 浓度,但患者之间的数值有所不同,并且随时间而变化。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。我们重新分析了临床研究的实验结果,以估算病毒脱落的持续时间和粪便中的病毒浓度。截至 2020 年 10 月 23 日,收集了可用的实验数据。对病毒脱落动力学进行建模,并通过贝叶斯框架将动态模型拟合到收集的数据中。使用后验分布,计算了粪便中病毒脱落持续时间和病毒拷贝数随时间的变化。我们估计 SARS-CoV-2 在粪便中的中位数浓度为 3.4(95%CrI:0.24-6.5)log 拷贝/克粪便,在当前标准定量限(Ct=40)下,我们的模型暗示病毒脱落的持续时间为 26.0 天(95%CrI:21.7-34.9)。基于模拟的发病率,我们的结果还表明,在症状出现和污水采样之间延迟一周会使发病率的估计增加 17.2 倍(即增加 10 倍)。我们的结果表明,粪便中病毒浓度的时间变化会影响微生物监测系统,如 WBE。本研究还暗示需要通过纳入未观察到的浓度动力学来调整粪便中病毒浓度的估计值。本研究中使用的方法可以很容易地在进一步的模拟中实施;因此,本研究的结果可能有助于加强需要将病毒排出到环境中的数量的疾病监测和风险评估。