TY - JOUR
T1 - Wastewater surveillance uncovers regional diversity and dynamics of SARS-CoV-2 variants across nine states in the USA
AU - Fontenele, Rafaela S.
AU - Yang, Yiyan
AU - Driver, Erin M.
AU - Magge, Arjun
AU - Kraberger, Simona
AU - Custer, Joy M.
AU - Dufault-Thompson, Keith
AU - Cox, Erin
AU - Newell, Melanie Engstrom
AU - Varsani, Arvind
AU - Halden, Rolf U.
AU - Scotch, Matthew
AU - Jiang, Xiaofang
N1 - Funding Information:
This work was supported in part by grants from the National Institutes of Health under Award Number U01LM013129 under the RADx-rad initiative for emergency response to COVID-19 to RUH, MS, and AV. RSF, YY, KD and XJ are supported by the Intramural Research Program of the NIH, National Library of Medicine. This work utilized the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). Samples were acquired with partial support from an award by the J.M. Kaplan Fund to RUH: OneWaterOneHealth nonprofit project 30009070 of the Arizona State University Foundation. The authors gratefully acknowledge the originating and submitting laboratories who contributed sequences to GISAID (www.gisaid.org). The authors would like to thank Tyler Perleberg, Allan Yanez, Izabella Block, Anumitha Aravindan, Ayesha Babbrah and Erin Clancy from the ASU Biodesign Center for Environmental Health Engineering for their support. This work would not be possible without the participation of the municipalities, and we are deeply appreciative.
Funding Information:
This work was supported in part by grants from the National Institutes of Health under Award Number U01LM013129 under the RADx-rad initiative for emergency response to COVID-19 to RUH, MS, and AV. RSF, YY, KD and XJ are supported by the Intramural Research Program of the NIH, National Library of Medicine . This work utilized the computational resources of the NIH HPC Biowulf cluster ( http://hpc.nih.gov ). Samples were acquired with partial support from an award by the J.M. Kaplan Fund to RUH: OneWaterOneHealth nonprofit project 30009070 of the Arizona State University Foundation . The authors gratefully acknowledge the originating and submitting laboratories who contributed sequences to GISAID ( www.gisaid.org ). The authors would like to thank Tyler Perleberg, Allan Yanez, Izabella Block, Anumitha Aravindan, Ayesha Babbrah and Erin Clancy from the ASU Biodesign Center for Environmental Health Engineering for their support. This work would not be possible without the participation of the municipalities, and we are deeply appreciative.
Publisher Copyright:
© 2023
PY - 2023/6/15
Y1 - 2023/6/15
N2 - Wastewater-based epidemiology (WBE) is a non-invasive and cost-effective approach for monitoring the spread of a pathogen within a community. WBE has been adopted as one of the methods to monitor the spread and population dynamics of the SARS-CoV-2 virus, but significant challenges remain in the bioinformatic analysis of WBE-derived data. Here, we have developed a new distance metric, CoVdist, and an associated analysis tool that facilitates the application of ordination analysis to WBE data and the identification of viral population changes based on nucleotide variants. We applied these new approaches to a large-scale dataset from 18 cities in nine states of the USA using wastewater collected from July 2021 to June 2022. We found that the trends in the shift between the Delta and Omicron SARS-CoV-2 lineages were largely consistent with what was seen in clinical data, but that wastewater analysis offered the added benefit of revealing significant differences in viral population dynamics at the state, city, and even neighborhood scales. We also were able to observe the early spread of variants of concern and the presence of recombinant lineages during the transitions between variants, both of which are challenging to analyze based on clinically-derived viral genomes. The methods outlined here will be beneficial for future applications of WBE to monitor SARS-CoV-2, particularly as clinical monitoring becomes less prevalent. Additionally, these approaches are generalizable, allowing them to be applied for the monitoring and analysis of future viral outbreaks.
AB - Wastewater-based epidemiology (WBE) is a non-invasive and cost-effective approach for monitoring the spread of a pathogen within a community. WBE has been adopted as one of the methods to monitor the spread and population dynamics of the SARS-CoV-2 virus, but significant challenges remain in the bioinformatic analysis of WBE-derived data. Here, we have developed a new distance metric, CoVdist, and an associated analysis tool that facilitates the application of ordination analysis to WBE data and the identification of viral population changes based on nucleotide variants. We applied these new approaches to a large-scale dataset from 18 cities in nine states of the USA using wastewater collected from July 2021 to June 2022. We found that the trends in the shift between the Delta and Omicron SARS-CoV-2 lineages were largely consistent with what was seen in clinical data, but that wastewater analysis offered the added benefit of revealing significant differences in viral population dynamics at the state, city, and even neighborhood scales. We also were able to observe the early spread of variants of concern and the presence of recombinant lineages during the transitions between variants, both of which are challenging to analyze based on clinically-derived viral genomes. The methods outlined here will be beneficial for future applications of WBE to monitor SARS-CoV-2, particularly as clinical monitoring becomes less prevalent. Additionally, these approaches are generalizable, allowing them to be applied for the monitoring and analysis of future viral outbreaks.
KW - Coronavirus infectious disease 19 (COVID-19)
KW - Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
KW - Wastewater-based epidemiology (WBE)
UR - http://www.scopus.com/inward/record.url?scp=85150373545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150373545&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2023.162862
DO - 10.1016/j.scitotenv.2023.162862
M3 - Article
C2 - 36933724
AN - SCOPUS:85150373545
SN - 0048-9697
VL - 877
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 162862
ER -