Evidence map›Paper›PMID 32607521›Full record

ArticlemedRxiv : the preprint server for health sciences2020

SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases.

Fuqing Wu, Amy Xiao, Jianbo Zhang, Katya Moniz, Noriko Endo, Federica Armas, Richard Bonneau, Megan A Brown, Mary Bushman, Peter R Chai and 18 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

28 authors.

Fuqing WuDepartment of Biological Engineering, Massachusetts Institute of Technology.
Amy XiaoDepartment of Biological Engineering, Massachusetts Institute of Technology.
Jianbo ZhangDepartment of Biological Engineering, Massachusetts Institute of Technology.
Katya MonizDepartment of Biological Engineering, Massachusetts Institute of Technology.
Noriko EndoBiobot Analytics, Inc., Cambridge, MA.
Federica ArmasSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
Richard BonneauCenter for Data Science NYU, Center for Social Media and Politics, New York University.
Megan A BrownCenter for Data Science NYU, Center for Social Media and Politics, New York University.
Mary BushmanHarvard T.H. Chan School of Public Health, Harvard University.
Peter R ChaiDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School.
Claire DuvalletBiobot Analytics, Inc., Cambridge, MA.
Timothy B EricksonDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School.
Katelyn FoppeBiobot Analytics, Inc., Cambridge, MA.
Newsha GhaeliBiobot Analytics, Inc., Cambridge, MA.
Xiaoqiong GuSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
William P HanageHarvard T.H. Chan School of Public Health, Harvard University.
Katherine H HuangBroad Institute of MIT and Harvard.
Wei Lin LeeSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
Mariana MatusBiobot Analytics, Inc., Cambridge, MA.
Kyle A McElroyBiobot Analytics, Inc., Cambridge, MA.
Jonathan NaglerCenter for Data Science NYU, Center for Social Media and Politics, New York University.
Steven F RhodeMassachusetts Water Resources Authority, Boston, MA.
Mauricio SantillanaHarvard T.H. Chan School of Public Health, Harvard University.
Joshua A TuckerCenter for Data Science NYU, Center for Social Media and Politics, New York University.
Stefan WuertzCampus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Shijie ZhaoDepartment of Biological Engineering, Massachusetts Institute of Technology.
Janelle ThompsonCampus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Eric J AlmDepartment of Biological Engineering, Massachusetts Institute of Technology.

Funding

Ecological and genetic contributions to the spread of resistance in pneumococcusR01AI106786 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM · 2013 to 2017
$3.1M
Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
A novel robotic wastewater analysis system to quantify opioid exposure and treatment in residential communitiesR44DA051106 · NIDA · BIOBOT ANALYTICS, INC. · PI CHAI, PETER R, MATUS GARCIA, MARIANA G · 2020 to 2022
$1.5M
Development of Ingestible Biosensors to Enhance PrEP Adherence in Substance UsersK23DA044874 · NIDA · BRIGHAM AND WOMEN'S HOSPITAL · PI CHAI, PETER R · 2018 to 2022
$1.0M
NIAID NIH HHS R01 AI106786NIDA NIH HHS K23 DA044874NIDA NIH HHS R44 DA051106NIGMS NIH HHS R01 GM130668
6 · The paper itself

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 use longitudinal wastewater analysis to track SARS-CoV-2 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. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers 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 titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.

Identifiers

PMID32607521
PMCPMC7325186

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.