Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools.
COVID-19 in schools
SARS-CoV-2
Wastewater surveillance
Journal
Lancet regional health. Americas
ISSN: 2667-193X
Titre abrégé: Lancet Reg Health Am
Pays: England
ID NLM: 9918232503006676
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
received:
14
10
2022
revised:
30
01
2023
accepted:
01
02
2023
entrez:
27
2
2023
pubmed:
28
2
2023
medline:
28
2
2023
Statut:
epublish
Résumé
Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021. In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.
Sections du résumé
Background
UNASSIGNED
Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown.
Methods
UNASSIGNED
The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021.
Findings
UNASSIGNED
In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples.
Interpretation
UNASSIGNED
Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy.
Funding
UNASSIGNED
County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.
Identifiants
pubmed: 36844610
doi: 10.1016/j.lana.2023.100449
pii: S2667-193X(23)00023-6
pmc: PMC9939935
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100449Subventions
Organisme : NIMH NIH HHS
ID : K01 MH112436
Pays : United States
Organisme : NIMHD NIH HHS
ID : L70 MD011114
Pays : United States
Organisme : NICHD NIH HHS
ID : U01 HD108787
Pays : United States
Investigateurs
Nathan A Baer
(NA)
Tom Barber
(T)
Anelizze Castro-Martinez
(A)
Marisol Chacón
(M)
Willi Cheung
(W)
Evelyn S Crescini
(ES)
Emily R Eisner
(ER)
Lizbeth Franco Vargas
(L)
Abbas Hakim
(A)
Charlotte Hobbs
(C)
Alma L Lastrella
(AL)
Elijah S Lawrence
(ES)
Nathaniel L Matteson
(NL)
Karthik Gangavarapu
(K)
Toan T Ngo
(TT)
Phoebe Seaver
(P)
Elizabeth W Smoot
(EW)
Rebecca Tsai
(R)
Bing Xia
(B)
Stefan Aigner
(S)
Catelyn Anderson
(C)
Pedro Belda-Ferre
(P)
Shashank Sathe
(S)
Mark Zeller
(M)
Kristian G Andersen
(KG)
Gene W Yeo
(GW)
Ezra Kurzban
(E)
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
Authors declare they have no competing interests.
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