Evidence map›Paper›PMID 34374562›Full record

ArticlemSystems2021

Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus.

Smruthi Karthikeyan, Andrew Nguyen, Daniel McDonald, Yijian Zong, Nancy Ronquillo, Junting Ren, Jingjing Zou, Sawyer Farmer, Greg Humphrey, Diana Henderson and 6 more

Abstract read
In one paragraph

Article in mSystems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.

0numbers the graph read from it
0cells of the map it votes in
86citing 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

86 citing papers in PubMed.

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  20. Wastewater Surveillance of SARS-CoV-2 in Zambia: An Early Warning Tool.International journal of molecular sciences · 2024
    Article

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

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Smruthi KarthikeyanDepartment of Pediatrics, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Andrew NguyenHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Daniel McDonaldDepartment of Pediatrics, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Yijian ZongHalicioglu Data Science Institute, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Nancy RonquilloDepartment of Electrical and Computer Engineering, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Junting RenHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Jingjing ZouHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Sawyer FarmerDepartment of Pediatrics, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Greg HumphreyDepartment of Pediatrics, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Diana HendersonCampus Planning, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Tara JavidiHalicioglu Data Science Institute, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Karen MesserHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Cheryl AndersonHerbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Robert SchooleyDivision of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Natasha K MartinDivision of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.
Rob KnightDepartment of Pediatrics, School of Medicine, University of California, San Diegogrid.266100.3, La Jolla, California, USA.

Funding

UC | University of California, San Diego (UCSD) RTL program
6 · The paper itself

Abstract

Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the "Return to Learn" program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen.

Indexed as

COVID-19high-throughputSARS-CoV-2wastewater epidemiology

Identifiers

PMID34374562
PMCPMC8409724

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.