Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published August 1, 2021 | Supplemental Material + Submitted + Published
Journal Article Open

Tools for interpretation of wastewater SARS-CoV-2 temporal and spatial trends demonstrated with data collected in the San Francisco Bay Area

Abstract

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA can be integrated with COVID-19 case data to inform timely pandemic response. However, more research is needed to apply and develop systematic methods to interpret the true SARS-CoV-2 signal from noise introduced in wastewater samples (e.g., from sewer conditions, sampling and extraction methods, etc.). In this study, raw wastewater was collected weekly from five sewersheds and one residential facility. The concentrations of SARS-CoV-2 in wastewater samples were compared to geocoded COVID-19 clinical testing data. SARS-CoV-2 was reliably detected (95% positivity) in frozen wastewater samples when reported daily new COVID-19 cases were 2.4 or more per 100,000 people. To adjust for variation in sample fecal content, four normalization biomarkers were evaluated: crAssphage, pepper mild mottle virus, Bacteroides ribosomal RNA (rRNA), and human 18S rRNA. Of these, crAssphage displayed the least spatial and temporal variability. Both unnormalized SARS-CoV-2 RNA signal and signal normalized to crAssphage had positive and significant correlation with clinical testing data (Kendall's Tau-b (τ)=0.43 and 0.38, respectively), but no normalization biomarker strengthened the correlation with clinical testing data. Locational dependencies and the date associated with testing data impacted the lead time of wastewater for clinical trends, and no lead time was observed when the sample collection date (versus the result date) was used for both wastewater and clinical testing data. This study supports that trends in wastewater surveillance data reflect trends in COVID-19 disease occurrence and presents tools that could be applied to make wastewater signal more interpretable and comparable across studies.

Additional Information

© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. Received 30 April 2021, Revised 30 June 2021, Accepted 25 July 2021, Available online 31 July 2021. We thank Matt Metzger, Melissa Thornton, and the whole COVID-WEB team for support. We also thank our wastewater utility partners for facilitating and assisting with wastewater sampling and physicochemical measurements, including from East Bay Municipal Utility District (Florencio Gonzalez, Bill Chan, Gabriela Esparza, Paula Hansen, Kiley Kinnon, Nick Klumpp, Debra Mapp, Christine Pagtakhan, Daniel Siu, Dave Williams, Zach Wu, and Cheryl Yee), Central Contra Costa Sanitary District (Lori Schectel, Mary Lou Esparza, Blake Brown, Amanda Cauble), San Jose-Santa Clara Regional Wastewater Facility (RWF) Operations and Laboratory staff, and Central Marin Sanitation Agency. We are grateful to the wastewater treatment agencies as well as the San Francisco Estuary Institute for providing us with sewershed shape files. We thank the COVID-19 WBE Collaborative (https://www.covid19wbec.org/) community for discussions of methods and approaches. Additionally, we thank Robert Tjian, Sarah Stanley, Erik Van Dis, Thomas Graham, and Mira Chaplin. We gratefully acknowledge funding from The Catena Foundation as well as rapid response grants from the Center for Information Technology Research in the Interest of Society and the Innovative Genomics Institute at UC Berkeley to K.L.N.. H.D.G. and L.C.K were supported by the National Science Foundation (NSF) Graduate Research Fellowship [grant number DGE-1752814]. In addition, H.D.G was supported by the Berkeley Fellowship, and L.C.K. was supported by NSF INTERN through Re-Inventing the Nation's Urban Water Infrastructure [grant number: 28139880-50542-C]. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Attached Files

Published - 1-s2.0-S2589914721000244-main.pdf

Published - main.pdf

Submitted - 2021.05.04.21256418v1.full.pdf

Supplemental Material - 1-s2.0-S2589914721000244-mmc1.pdf

Files

main.pdf
Files (25.5 MB)
Name Size Download all
md5:8cbccdd57b507c9052fc98077e5e548c
11.0 MB Preview Download
md5:e3a71d9d246b8feabac918ecd88101eb
963.1 kB Preview Download
md5:26791057125225e4d3cd83f096675c16
2.6 MB Preview Download
md5:1f8763f5e71022d37c388867862864f2
11.0 MB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023