Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age.
Authors presented in alphabetical order, Eric J. Arts, R. Stephen Brown, David Bulir, Trevor C. Charles, Christopher T. DeGroot, Robert Delatolla, Jean‐Paul Desaulniers, Elizabeth A. Edwards, Meghan Fuzzen, Kimberley Gilbride, Jodi Gilchrist, Lawrence Goodridge, Tyson E. Graber, Marc Habash, Peter Jüni, Andrea E. Kirkwood, James Knockleby, Christopher J. Kyle, Chrystal Landgraff, Chand S. Mangat, Douglas G. Manuel, R. Michael L. McKay, Edgard M. Mejia, Aleksandra Mloszewska, Banu Örmeci, Claire Oswald, Sarah Jane Payne, Hui Peng, Shelley Peterson, Art F. Y. Poon, Mark R. Servos, Denina Simmons, Jianxian Sun, Minqing Ivy Yang, Gustavo Ybazeta
Abstract
Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.- Cite:
- Authors presented in alphabetical order, Eric J. Arts, R. Stephen Brown, David Bulir, Trevor C. Charles, Christopher T. DeGroot, Robert Delatolla, Jean‐Paul Desaulniers, Elizabeth A. Edwards, Meghan Fuzzen, Kimberley Gilbride, Jodi Gilchrist, Lawrence Goodridge, Tyson E. Graber, Marc Habash, Peter Jüni, Andrea E. Kirkwood, James Knockleby, Christopher J. Kyle, et al.. 2022. Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age..
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@article{order:-2022-Community,
title = "Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age.",
author = {order, Authors presented in alphabetical and
Arts, Eric J. and
Brown, R. Stephen and
Bulir, David and
Charles, Trevor C. and
DeGroot, Christopher T. and
Delatolla, Robert and
Desaulniers, Jean‐Paul and
Edwards, Elizabeth A. and
Fuzzen, Meghan and
Gilbride, Kimberley and
Gilchrist, Jodi and
Goodridge, Lawrence and
Graber, Tyson E. and
Habash, Marc and
J{\"u}ni, Peter and
Kirkwood, Andrea E. and
Knockleby, James and
Kyle, Christopher J. and
Landgraff, Chrystal and
Mangat, Chand S. and
Manuel, Douglas G. and
McKay, R. Michael L. and
Mejia, Edgard M. and
Mloszewska, Aleksandra and
{\"O}rmeci, Banu and
Oswald, Claire and
Payne, Sarah Jane and
Peng, Hui and
Peterson, Shelley and
Poon, Art F. Y. and
Servos, Mark R. and
Simmons, Denina and
Sun, Jianxian and
Yang, Minqing Ivy and
Ybazeta, Gustavo},
journal = "",
year = "2022",
publisher = "Research Square Platform LLC",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G22-18001",
doi = "10.21203/rs.3.rs-1439969/v2",
abstract = "Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.",
}
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<abstract>Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels.</abstract>
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%0 Journal Article %T Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age. %A order, Authors presented in alphabetical %A Arts, Eric J. %A Brown, R. Stephen %A Bulir, David %A Charles, Trevor C. %A DeGroot, Christopher T. %A Delatolla, Robert %A Desaulniers, Jean‐Paul %A Edwards, Elizabeth A. %A Fuzzen, Meghan %A Gilbride, Kimberley %A Gilchrist, Jodi %A Goodridge, Lawrence %A Graber, Tyson E. %A Habash, Marc %A Jüni, Peter %A Kirkwood, Andrea E. %A Knockleby, James %A Kyle, Christopher J. %A Landgraff, Chrystal %A Mangat, Chand S. %A Manuel, Douglas G. %A McKay, R. Michael L. %A Mejia, Edgard M. %A Mloszewska, Aleksandra %A Örmeci, Banu %A Oswald, Claire %A Payne, Sarah Jane %A Peng, Hui %A Peterson, Shelley %A Poon, Art F. Y. %A Servos, Mark R. %A Simmons, Denina %A Sun, Jianxian %A Yang, Minqing Ivy %A Ybazeta, Gustavo %D 2022 %I Research Square Platform LLC %F order:-2022-Community %X Abstract Wastewater-based surveillance of SARS-CoV-2 RNA has been implemented at building, neighbourhood, and city levels throughout the world. Implementation strategies and analysis methods differ, but they all aim to provide rapid and reliable information about community COVID-19 health states. A viable and sustainable SARS-CoV-2 surveillance network must not only provide reliable and timely information about COVID-19 trends, but also provide for scalability as well as accurate detection of known or unknown emerging variants. Emergence of the SARS-CoV-2 variant of concern Omicron in late Fall 2021 presented an excellent opportunity to benchmark individual and aggregated data outputs of the Ontario Wastewater Surveillance Initiative in Canada; this public health-integrated surveillance network monitors wastewaters from over 10 million people across major population centres of the province. We demonstrate that this coordinated approach provides excellent situational awareness, comparing favourably with traditional clinical surveillance measures. Thus, aggregated datasets compiled from multiple wastewater-based surveillance nodes can provide sufficient sensitivity (i.e., early indication of increasing and decreasing incidence of SARS-CoV-2) and specificity (i.e., allele frequency estimation of emerging variants) with which to make informed public health decisions at regional- and state-levels. %R 10.21203/rs.3.rs-1439969/v2 %U https://gwf-uwaterloo.github.io/gwf-publications/G22-18001 %U https://doi.org/10.21203/rs.3.rs-1439969/v2
Markdown (Informal)
[Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age.](https://gwf-uwaterloo.github.io/gwf-publications/G22-18001) (order et al., GWF 2022)
- Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age. (order et al., GWF 2022)
ACL
- Authors presented in alphabetical order, Eric J. Arts, R. Stephen Brown, David Bulir, Trevor C. Charles, Christopher T. DeGroot, Robert Delatolla, Jean‐Paul Desaulniers, Elizabeth A. Edwards, Meghan Fuzzen, Kimberley Gilbride, Jodi Gilchrist, Lawrence Goodridge, Tyson E. Graber, Marc Habash, Peter Jüni, Andrea E. Kirkwood, James Knockleby, Christopher J. Kyle, et al.. 2022. Community Surveillance of Omicron in Ontario: Wastewater-based Epidemiology Comes of Age..