Evidence map›Paper›PMID 38562450›Full record

ArticleArXiv2024

Wastewater-based Epidemiology for COVID-19 Surveillance and Beyond: A Survey.

Chen Chen, Yunfan Wang, Gursharn Kaur, Aniruddha Adiga, Baltazar Espinoza, Srinivasan Venkatramanan, Andrew Warren, Bryan Lewis, Justin Crow, Rekha Singh and 3 more

Abstract readPreprint
In one paragraph

Article in ArXiv, 2024. 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

13 authors.

Chen ChenDepartment of Computer Science, University of Virginia, Charlottesville, 22904, United States.ORCID 0000-0002-7423-0090
Yunfan WangDepartment of Computer Science, University of Virginia, Charlottesville, 22904, United States.
Gursharn KaurBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Aniruddha AdigaBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Baltazar EspinozaBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Srinivasan VenkatramananBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Andrew WarrenBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Bryan LewisBiocomplexity Institute and Initiative, University of Virginia, Charlottesville, 22904, United States.
Justin CrowVirginia Department of Health, Richmond, 23219, United States.
Rekha SinghVirginia Department of Health, Richmond, 23219, United States.
Alexandra LorentzDivision of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States.
Denise ToneyDivision of Consolidated Laboratory Services, Department of General Services, Richmond, 23219, United States.
Madhav MaratheDepartment of Computer Science, University of Virginia, Charlottesville, 22904, United States.

Funding

Systems Analysis of Social Pathways of Epidemics to Reduce Health DisparitiesR01GM109718 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI MARATHE, ACHLA, VULLIKANTI, ANIL · 2014 to 2022
$3.6M
NIGMS NIH HHS R01 GM109718
6 · The paper itself

Abstract

The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.

Indexed as

COVID-19EpidemiologyInfectious diseaseSurveyWastewater

Identifiers

PMID38562450
PMCPMC10984000

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.