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Enregistrement W6944839683 · doi:10.20381/ruor-28622

Minimizing Variability of SARS-CoV-2 Wastewater Measurements and Advancing the Interpretation of Wastewater Surveillance Data

2022· other· en· W6944839683 sur OpenAlexaboutno aff

Notice bibliographique

RevueuO Research (University of Ottawa) · 2022
Typeother
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueHorticultural and Viticultural Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésContext (archaeology)Public healthWastewaterInterpretation (philosophy)OutbreakDisease surveillance

Résumé

récupéré en direct d'OpenAlex

Wastewater surveillance (WWS), included in the field of study of wastewater-based epidemiology (WBE), is the analysis of wastewaters to quantify community disease or use of chemicals by the community, such as pharmaceuticals and illicit drugs. WWS has historically been applied within the context of community public health through monitoring of pathogenic viral outbreaks such as polio and hepatitis A, as well as monitoring of illicit drug consumption. While WWS has been used for several decades, many of its contributions were largely unpopular within the public mainstream prior to the coronavirus disease in 2019 (COVID-19) pandemic. Since the onset of the pandemic, public health resources around the world were significantly afflicted by COVID-19. This elicited a prompt response by researchers to rapidly develop WWS for the application of severe acute respiratory syndrome-2 virus (SARS-CoV-2) WWS as a complementary epidemiological tool for population-wide monitoring of COVID-19 outbreaks. With the novelty of this technology, there are several challenges and gaps of knowledge that remain to be addressed in order to improve the reliability of WWS for SARS-CoV-2. Particularly, the effects of various constituents, endogenous and added, that commonly occur and are applied to wastewaters may result in the significant variability observed in WWS data sets, which in turn results in the uncertainty of the interpretation of WWS data sets of SARS-CoV-2 by various public health agencies throughout the pandemic. This study is aimed to address the critical issue of data variability by investigating the effect of enhanced primary clarification with ferric-based chemical coagulants on the measurements of SARS-CoV-2 and the pepper mild mottle virus (PMMoV) WWS normalizing biomarker. It is believed that the addition of ferric ions via common coagulation treatment of primary sludge would interfere with the quantitative polymerase chain reaction (qPCR) amplification of viral RNA and could cause false-negative results. With 18.1% of the total population in Canada receiving wastewater that undergoes primary treatment including chemical precipitation/flocculation, and with proof of enrichment of SARS-CoV-2 and PMMoV RNA in untreated wastewater and settled primary sludge, it is important to elucidate whether ferric sulfate chemical coagulant is a potential source of data variability for population-wide WWS. With ferric sulfate concentrations ranging from 0 - 60 mg/L as Fe³⁺, the PMMoV-normalized SARS-CoV-2 viral signal measurements were significantly reduced as a result of a significant elevation in the PMMoV viral signal measurements. This is possibly due to the partitioning of PMMoV viral particles from the liquid phase to the solids phase of wastewater samples influenced by ferric sulfate at 60 mg/L as Fe³⁺ compared to the samples that were not treated with ferric sulfate. This thesis also examined the evolving relation of WWS measurements to measurements of public health metrics to improve our current interpretation of SARS-CoV-2 WWS. The statistical correlations between wastewater PMMoV-normalized SARS-CoV-2 viral signal and clinical metrics indicative of disease incidence (laboratory-confirmed COVID-19 positive cases), and metrics indicative of disease burden (hospitalization, intensive care unit (ICU) admissions, and deaths) are investigated from the onset of the wildtype and the Alpha variant of concern (VOC) during limited vaccination immunization, through the onset of the Omicron BA.2 VOC in two strongly characterized sewersheds (Ottawa and Hamilton). WWS demonstrates to be a strong indicator of both disease incidence and disease burden during the period of limited vaccination immunization, and a moderate indicator of disease incidence, while remains a strong indicator of disease burden during the period of peak vaccination immunization (2-4 weeks after reception of 2 doses of the COVID-19 vaccine). Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,025
score de la tête « metaresearch » (Gemma)0,055
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,132

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0250,055
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0020,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,136
Tête enseignante GPT0,327
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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