Early warning of COVID-19 via wastewater-based epidemiology: potential and bottlenecks.
Early warning of COVID-19 via wastewater-based epidemiology: potential and bottlenecks.
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DOI：
10.1016/j.scitotenv.2021.145124
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发表时间：
2021-05-01
期刊：
影响因子：
--
通讯作者：
Sano D
中科院分区：
文献类型：
--
作者：
Zhu Y;Oishi W;Maruo C;Saito M;Chen R;Kitajima M;Sano D
有效的预警工具对于疫情防控具有重要的行政和社会意义。面对由 COVID-19 引发的前所未有的全球公共卫生危机，基于废水的流行病学 (WBE) 作为临床检测的一种有前景的监测补充，受到了很高的期望，而临床检测一直受到能力和周转时间有限的困扰。特别是，最近的研究强调了 WBE 在成为预警系统一部分方面可能发挥的作用。在这项研究中，我们简要讨论了这一概念的基础知识、这种应用的好处和关键点、科学界面临的挑战、迄今为止取得的进展，以及未来研究需要解决的问题，以使这一概念发挥作用。我们发现，感染个体的排毒动态，特别是数学排毒模型的形式，以及根据观察到的病毒载量反算活跃排毒者的数量，是WBE在COVID-19大流行中应用的主要瓶颈，值得更多关注，并且需要优化采样策略（位置、时间和间隔）以适应WBE项目的目的和范围。
An effective early warning tool is of great administrative and social significance to the containment and control of an epidemic. Facing the unprecedented global public health crisis caused by COVID-19, wastewater-based epidemiology (WBE) has been given high expectations as a promising surveillance complement to clinical testing which had been plagued by limited capacity and turnaround time. In particular, recent studies have highlighted the role WBE may play in being a part of the early warning system. In this study, we briefly discussed the basics of the concept, the benefits and critical points of such an application, the challenges faced by the scientific community, the progress made so far, and what awaits to be addressed by future studies to make the concept work. We identified that the shedding dynamics of infected individuals, especially in the form of a mathematical shedding model, and the back-calculation of the number of active shedders from observed viral load are the major bottlenecks of WBE application in the COVID-19 pandemic that deserve more attention, and the sampling strategy (location, timing, and interval) needs to be optimized to fit the purpose and scope of the WBE project.
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DOI：
10.1016/j.scitotenv.2020.143578
发表时间：
2021-03-01
期刊：
The Science of the total environment
影响因子：
--
作者：
Hata A;Hara-Yamamura H;Meuchi Y;Imai S;Honda R
通讯作者：
Honda R
影响因子：
9.8
作者：
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通讯作者：
Castiglioni, Sara
影响因子：
9.8
作者：
Haramoto, Eiji;Malla, Bikash;Kitajima, Masaaki
通讯作者：
Kitajima, Masaaki
影响因子：
82.9
作者：
He, Xi;Lau, Eric H. Y.;Leung, Gabriel M.
通讯作者：
Leung, Gabriel M.
影响因子：
9.8
作者：
Ahmed, Warish;Bertsch, Paul M.;Kitajima, Masaaki
通讯作者：
Kitajima, Masaaki