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Rapid and extensive SARS-CoV-2 Omicron variant infection wave revealed by wastewater surveillance in Shenzhen following the lifting of a strict COVID-19 strategy.
Li, Yinghui; Du, Chen; Lv, Ziquan; Wang, Fuxiang; Zhou, Liping; Peng, Yuejing; Li, Wending; Fu, Yulin; Song, Jiangteng; Jia, Chunyan; Zhang, Xin; Liu, Mujun; Wang, Zimiao; Liu, Bin; Yan, Shulan; Yang, Yuxiang; Li, Xueyun; Zhang, Yong; Yuan, Jianhui; Xu, Shikuan; Chen, Miaoling; Shi, Xiaolu; Peng, Bo; Chen, Qiongcheng; Qiu, Yaqun; Wu, Shuang; Jiang, Min; Chen, Miaomei; Tang, Jinzhen; Wang, Lei; Hu, Lulu; Wei, Bincai; Xia, Yu; Ji, John S; Wan, Chengsong; Lu, Hongzhou; Zhang, Tong; Zou, Xuan; Fu, Songzhe; Hu, Qinghua.
Affiliation
  • Li Y; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Du C; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Lv Z; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Wang F; Department of Infectious Diseases, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
  • Zhou L; Peking University Shenzhen Hospital, Shenzhen, China.
  • Peng Y; BSL-3 Laboratory (Guangdong), Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.
  • Li W; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Fu Y; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Song J; Water Ecology and Environment Division, Shenzhen Ecology and Environment Bureau, Shenzhen, China.
  • Jia C; Water Ecology and Environment Division, Shenzhen Ecology and Environment Bureau, Shenzhen, China.
  • Zhang X; Water Ecology and Environment Division, Shenzhen Ecology and Environment Bureau, Shenzhen, China.
  • Liu M; Futian District Water Authority, Shenzhen, China.
  • Wang Z; Futian District Water Authority, Shenzhen, China.
  • Liu B; Futian District Water Authority, Shenzhen, China.
  • Yan S; Nanshan District Water Authority, Shenzhen, China.
  • Yang Y; Nanshan District Water Authority, Shenzhen, China.
  • Li X; Futian District Center for Disease Control and Prevention, Shenzhen, China.
  • Zhang Y; Futian District Center for Disease Control and Prevention, Shenzhen, China.
  • Yuan J; Nanshan District Center for Disease Control and Prevention, Shenzhen, China.
  • Xu S; Nanshan District Center for Disease Control and Prevention, Shenzhen, China.
  • Chen M; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Shi X; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Peng B; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Chen Q; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Qiu Y; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Wu S; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Jiang M; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Chen M; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Tang J; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Wang L; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Hu L; Shenzhen Center for Disease Control and Prevention, Shenzhen, China.
  • Wei B; School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
  • Xia Y; School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China.
  • Ji JS; Vanke School of Public Health, Tsinghua University, Beijing, China.
  • Wan C; BSL-3 Laboratory (Guangdong), Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.
  • Lu H; Department of Infectious Diseases, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China. Electronic address: luhongzhou@fudan.edu.cn.
  • Zhang T; Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China. Electronic address: zhangt@hku.hk.
  • Zou X; Shenzhen Center for Disease Control and Prevention, Shenzhen, China. Electronic address: zoux@szcdc.net.
  • Fu S; Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi'an, China. Electronic address: fusongzhe@126.com.
  • Hu Q; Shenzhen Center for Disease Control and Prevention, Shenzhen, China. Electronic address: huqinghua03@163.com.
Sci Total Environ ; 949: 175235, 2024 Nov 01.
Article in En | MEDLINE | ID: mdl-39102947
ABSTRACT
Wastewater-based epidemiology (WBE) has emerged as a promising tool for monitoring the spread of COVID-19, as SARS-CoV-2 can be shed in the faeces of infected individuals, even in the absence of symptoms. This study aimed to optimize a prediction model for estimating COVID-19 infection rates based on SARS-CoV-2 RNA concentrations in wastewater, and reveal the infection trends and variant diversification in Shenzhen, China following the lifting of a strict COVID-19 strategy. Faecal samples (n = 4337) from 1204 SARS-CoV-2 infected individuals hospitalized in a designated hospital were analysed to obtain Omicron variant-specific faecal shedding dynamics. Wastewater samples from 6 wastewater treatment plants (WWTPs) and 9 pump stations, covering 3.55 million people, were monitored for SARS-CoV-2 RNA concentrations and variant abundance. We found that the viral load in wastewater increased rapidly in December 2022 in the two districts, demonstrating a sharp peak in COVID-19 infections in late-December 2022, mainly caused by Omicron subvariants BA.5.2.48 and BF.7.14. The prediction model, based on the mass balance between total viral load in wastewater and individual faecal viral shedding, revealed a surge in the cumulative infection rate from <0.1 % to over 70 % within three weeks after the strict COVID-19 strategy was lifted. Additionally, 39 cryptic SARS-CoV-2 variants were identified in wastewater, in addition to those detected through clinical surveillance. These findings demonstrate the effectiveness of WBE in providing comprehensive and efficient assessments of COVID-19 infection rates and identifying cryptic variants, highlighting its potential for monitoring emerging pathogens with faecal shedding.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wastewater / SARS-CoV-2 / COVID-19 Limits: Humans Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Wastewater / SARS-CoV-2 / COVID-19 Limits: Humans Country/Region as subject: Asia Language: En Journal: Sci Total Environ Year: 2024 Document type: Article