Evidence map›Paper›PMID 34159339›Full record

ArticlemedRxiv : the preprint server for health sciences2021

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

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors.

Amy Xiao *Department of Biological Engineering, Massachusetts Institute of Technology.
Fuqing Wu *Department of Biological Engineering, Massachusetts Institute of Technology.
Mary Bushman *Harvard T.H. Chan School of Public Health, Harvard University.
Jianbo ZhangDepartment of Biological Engineering, Massachusetts Institute of Technology.
Maxim ImakaevBiobot Analytics, Inc., Cambridge, MA.
Peter R ChaiDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School.
Claire DuvalletBiobot Analytics, Inc., Cambridge, MA.
Noriko EndoBiobot Analytics, Inc., Cambridge, MA.
Timothy B EricksonDivision of Medical Toxicology, Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School.
Federica ArmasSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
Brian ArnoldDepartment of Computer Science, Princeton University.
Hongjie ChenSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
Franciscus ChandraSingapore-MIT Alliance for Research and Technology, Antimicrobial Resistance Interdisciplinary Research Group, Singapore.
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.
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.
Katya MonizDepartment of Biological Engineering, Massachusetts Institute of Technology.
Steven F RhodeMassachusetts Water Resources Authority, Boston, MA.
Janelle ThompsonCampus for Research Excellence and Technological Enterprise (CREATE), Singapore.
Eric J AlmDepartment of Biological Engineering, Massachusetts Institute of Technology.

Funding

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
NIDA 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. We collected 24-hour composite wastewater samples from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and measured SARS-CoV-2 RNA concentrations using RT-qPCR. We show that the relationship between wastewater viral titers 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 viral titers 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. We find that the WC ratio increases after key events, providing insight into the balance between disease spread and public health response. We also find 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. These three metrics could complement a framework for integrating wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Identifiers

PMID34159339
PMCPMC8219106

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LicenceCC BY-NC-ND
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Registered trials

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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.