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Survival of SARS-CoV-2 in wastewater

2023· article· en· W4361001979 on OpenAlexaff
Samendra P. Sherchan, Ocean Thakali, Luisa A. Ikner, Charles P. Gerba

Bibliographic record

VenueThe Science of The Total Environment · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Wastewater2019-20 coronavirus outbreakVirologySars virusCoronavirusBetacoronavirusMedicineEnvironmental scienceInternal medicineEnvironmental engineeringOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

The ongoing pandemic of Coronavirus disease 2019 (COVID-19) has affected >600 million people with >6 million deaths. Although Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2), the etiologic agent of COVID-19, is transmitted via respiratory droplets or direct contact, isolation of viable SARS-CoV-2 in feces has been reported. Therefore, there is a need for understanding the persistence of SARS-CoV-2 and emerging variants in wastewater. In this study, the survival of SARS-CoV-2 isolate hCoV-19/USA-WA1/2020 was observed in three wastewater matrices - filtered and unfiltered raw wastewater, and secondary effluent. All experiments were conducted within a BSL-3 laboratory at room temperature. The time required for inactivation of 90 % (T 90 ) of SARS-CoV-2 was 10.4, 10.8, and 18.3 h for unfiltered raw, filtered raw, and secondary effluent, respectively. Progressive decline in infectivity of the virus following first order kinetics was noted in these wastewater matrices. To the best of our knowledge, this is the first study to describe the survival of SARS-CoV-2 in secondary effluent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.284
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2023
Admission routes1
Has abstractyes

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