Your browser doesn't support javascript.
SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.
Wu, Fuqing; Xiao, Amy; Zhang, Jianbo; Moniz, Katya; Endo, Noriko; Armas, Federica; Bonneau, Richard; Brown, Megan A; Bushman, Mary; Chai, Peter R; Duvallet, Claire; Erickson, Timothy B; Foppe, Katelyn; Ghaeli, Newsha; Gu, Xiaoqiong; Hanage, William P; Huang, Katherine H; Lee, Wei Lin; Matus, Mariana; McElroy, Kyle A; Nagler, Jonathan; Rhode, Steven F; Santillana, Mauricio; Tucker, Joshua A; Wuertz, Stefan; Zhao, Shijie; Thompson, Janelle; Alm, Eric J.
  • Wu F; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA.
  • Xiao A; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA.
  • Zhang J; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA.
  • Moniz K; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA.
  • Endo N; Biobot Analytics, Inc., Cambridge, MA, USA.
  • Armas F; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
  • Bonneau R; Center for Data Science NYU, Center for Social Media and Politics, New York University, USA.
  • Brown MA; Center for Data Science NYU, Center for Social Media and Politics, New York University, USA.
  • Bushman M; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Chai PR; Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; The Fenway Institute, Fenway Health, Boston, MA, USA.
  • Duvallet C; Biobot Analytics, Inc., Cambridge, MA, USA.
  • Erickson TB; Division of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, USA; Harvard Humanitarian Initiative, Harvard University, USA.
  • Foppe K; Biobot Analytics, Inc., Cambridge, MA, USA.
  • Ghaeli N; Biobot Analytics, Inc., Cambridge, MA, USA.
  • Gu X; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
  • Hanage WP; Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Huang KH; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Lee WL; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
  • Matus M; Biobot Analytics, Inc., Cambridge, MA, USA.
  • McElroy KA; Biobot Analytics, Inc., Cambridge, MA, USA.
  • Nagler J; Center for Data Science NYU, Center for Social Media and Politics, New York University, USA.
  • Rhode SF; Massachusetts Water Resources Authority, Boston, MA, USA.
  • Santillana M; Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
  • Tucker JA; Center for Data Science NYU, Center for Social Media and Politics, New York University, USA.
  • Wuertz S; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; School of Civil and Environmental Enginering, Nanyang Technological University, Singapore.
  • Zhao S; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA.
  • Thompson J; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore.
  • Alm EJ; Department of Biological Engineering, Massachusetts Institute of Technology, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, USA; Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore;
Sci Total Environ ; 805: 150121, 2022 Jan 20.
Article in English | MEDLINE | ID: covidwho-1386609
ABSTRACT
Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article Affiliation country: J.scitotenv.2021.150121

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Sci Total Environ Year: 2022 Document Type: Article Affiliation country: J.scitotenv.2021.150121