Evidence map›Paper›PMID 35101695›Full record

ArticleWater research2022

Metrics to relate COVID-19 wastewater data to clinical testing dynamics.

Amy Xiao, Fuqing Wu, Mary Bushman, Jianbo Zhang, Maxim Imakaev, Peter R Chai, Claire Duvallet, Noriko Endo, Timothy B Erickson, Federica Armas and 13 more

Open access · hybridAbstract read
In one paragraph

Article in Water research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 3 pooled it
11.2field-weighted citation impact, top 1% of its field
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

63 citing papers in PubMed, 3 syntheses or guidelines pooled it, 117 citations in OpenAlex.

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3 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 9 institutions in 2 countries.

Amy XiaoDepartment of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA.
Fuqing WuDepartment of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA.
Mary BushmanHarvard T.H. Chan School of Public Health, Harvard University USA.
Jianbo ZhangDepartment of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA.
Maxim ImakaevBiobot Analytics, Inc., Cambridge, MA USA.
Peter R ChaiDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; The Fenway Institute, Fenway Health, Boston, MA USA; The Koch Institute for Integrated Cancer Research, Massachusetts Institute of Technology USA; Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute USA.
Claire DuvalletBiobot Analytics, Inc., Cambridge, MA USA.
Noriko EndoBiobot Analytics, Inc., Cambridge, MA USA.
Timothy B EricksonDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School USA; Harvard Humanitarian Initiative, Harvard University USA.
Federica ArmasSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Brian ArnoldDepartment of Computer Science, Princeton University USA; Center for Statistics and Machine Learning, Princeton University USA.
Hongjie ChenSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Franciscus ChandraSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Newsha GhaeliBiobot Analytics, Inc., Cambridge, MA USA.
Xiaoqiong GuSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
William P HanageHarvard T.H. Chan School of Public Health, Harvard University USA.
Wei Lin LeeSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Mariana MatusBiobot Analytics, Inc., Cambridge, MA USA.
Kyle A McElroyBiobot Analytics, Inc., Cambridge, MA USA.
Katya MonizDepartment of Biological Engineering, Massachusetts Institute of Technology USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology USA.
Steven F RhodeMassachusetts Water Resources Authority, Boston, MA USA.
Janelle ThompsonCampus 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.
Eric J AlmDepartment 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; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore; Broad Institute of MIT and Harvard, Cambridge, MA USA. Electronic address: ejalm@mit.edu.
Massachusetts Institute of Technology · USSingapore-MIT Alliance for Research and Technology · SGHarvard University · USSingapore-HUJ Alliance for Research and Enterprise · SGDana-Farber/Harvard Cancer Center · USFenway Health · USMassachusetts Department of Environmental Protection · USNanyang Technological University · SGPrinceton University · US

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
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 DA051106
6 · The paper itself

Abstract

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Indexed as

COVID-19PandemicsHumansRNA, ViralSARS-CoV-2WastewaterWastewater-Based Epidemiological MonitoringRNA, ViralWastewaterSARS-CoV-2Temporal dynamicsWastewater-based epidemiologyWastewater surveillance

Identifiers

PMID35101695
PMCPMC8758950
OpenAlexW4225819711

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.