4.7 Article

Ahead of the second wave: Early warning for COVID-19 by wastewater surveillance in Hungary

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 786, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2021.147398

关键词

COVID-19; SARS-CoV-2; Wastewater-based epidemiology; Environmental surveillance; Early warning

向作者/读者索取更多资源

Wastewater based epidemiology is a potential early warning tool for the detection of COVID-19 outbreak. The sewage surveillance in Hungary showed effectiveness in predicting the second wave of the outbreak, even with relatively low frequency sampling.
Wastewater based epidemiology is a potential early warning tool for the detection of COVID-19 outbreak. Sewage surveillance for SARS-CoV-2 RNA was introduced in Hungary after the successful containment of the first wave of the pandemic to forecast the resurge of infections. Three wastewater treatment plants servicing the entire population (1.8 million) of the capital, Budapest were sampled weekly. 24 h composite (n = 44) and grab samples (n = 21) were concentrated by an in-house flat sheet membrane ultrafiltration method. The efficiency and reproducibility of the method was comparable to those previously published. SARS-CoV-2 RNA was quantified using RT-qPCR of the N gene. The first positive signal in sewage was detected 2 weeks before the rise in case numbers. Viral concentration and volume-adjusted viral load correlated to the weekly new cases from the same week and the rolling 7-day average of active cases in the subsequent week. The correlation was more pronounced in the ascending phase of the outbreak, data was divergent once case numbers plateaued. Wastewater surveillance was found to be effective in predicting the second wave of the outbreak in Hungary. Data indicated that even relatively low frequency (weekly) sampling is useful and at the same time, cost effective tool in outbreak detection. (c) 2021 Elsevier B.V. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据