Award Abstract # 2038087
EAGER: RCN: Wastewater Surveillance of SARS-CoV-2

NSF Org: CBET
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Recipient: UNIVERSITY OF NOTRE DAME DU LAC
Initial Amendment Date: July 15, 2020
Latest Amendment Date: July 15, 2020
Award Number: 2038087
Award Instrument: Standard Grant
Program Manager: Mamadou Diallo
CBET
 Division of Chemical, Bioengineering, Environmental, and Transport Systems
ENG
 Directorate for Engineering
Start Date: August 1, 2020
End Date: December 31, 2021 (Estimated)
Total Intended Award Amount: $299,995.00
Total Awarded Amount to Date: $299,995.00
Funds Obligated to Date: FY 2020 = $299,995.00
History of Investigator:
  • Kyle Bibby (Principal Investigator)
    kbibby@nd.edu
  • Alexandria Boehm (Co-Principal Investigator)
  • Rolf Halden (Co-Principal Investigator)
  • Jeseth Delgado Vela (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Notre Dame
940 GRACE HALL
NOTRE DAME
IN  US  46556-5708
(574)631-7432
Sponsor Congressional District: 02
Primary Place of Performance: University of Notre Dame
IN  US  46556-5708
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): FPU6XGFXMBE9
Parent UEI: FPU6XGFXMBE9
NSF Program(s): EnvE-Environmental Engineering
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 096Z, 7916
Program Element Code(s): 144000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

The COVID-19 pandemic has infected >10 million people and disrupted the global economy. Tracking the spread of the disease is critical to protecting public health and assessing the success of pandemic response. Wastewater surveillance for COVID-19 is based on measuring genes from SARS-CoV-2 (the virus that causes COVID-19) in municipal wastewater. This information tells us whether COVID-19 is present and how it is spreading in a community. While this technique holds promise, major knowledge gaps must be addressed to allow widespread application of this technique. The goal of this Research Coordination Network (RCN) project is to connect researchers from across the Nation who are studying this problem to maximize the discovery potential. This RCN will be completely virtual to enable widespread participation during the pandemic and allow for ?open-door? participation of any interested research groups or individuals. Specific activities include virtual conferences, workshops, training videos, and seminars for dissemination of knowledge. Data will be maintained in a centralized data repository to facilitate sample exchange and archiving. Exchange and knowledge transfer will be facilitated on a global scale by connecting with other international wastewater surveillance networks and efforts. Beyond aiding in the COVID-19 pandemic response, the proposed RCN will contribute to the scientific literacy of the Nation through student and postdoc training, data sharing, and broad dissemination of research findings.

The infectious agent SARS-CoV-2, a member of the Coronavirus family, is the causative agent of COVID-19, a respiratory illness first detected in Wuhan, China in December 2019. COVID-19 has since grown into a global pandemic, causing >10M illnesses and >500K deaths globally. The outbreak has led to severe economic impacts resulting from interventions such as social distancing and stay at home ordinances to slow the spread of the disease. A significant challenge in responding to COVID-19 is the difficulty of measuring the prevalence of COVID-19 in a given community. SARS-CoV-2 RNA is shed in the stool of infected individuals, thereby affording the opportunity to rapidly monitor for coronavirus presence in centralized wastewater collection systems. Wastewater surveillance for SARS-CoV-2 RNA has thus emerged as an alternative for rapid assessment of COVID-19 presence within a community. However, despite its potential for efficient surveillance, there are numerous critical research questions that remain to be addressed before widespread adoption of this technique can occur. The team of investigators from the University of Notre Dame, Howard University, Stanford University, and Arizona State University will convene a one-year Research Coordination Network (RCN) effort with the goal of connecting teams from across the country studying SARS-CoV-2 in wastewater to address these knowledge gaps. Network activities will be held virtually to maximize participation during the pandemic. Specific activities include virtual conferences facilitated by the Water Research Foundation, workshops, training videos, and seminars for dissemination of knowledge. Exchange and knowledge transfer will be facilitated on a global scale by connecting with other international wastewater surveillance networks and efforts. Successful completion of this RCN will accelerate the transfer of knowledge leading to more rapid adoption of best practices that can facilitate the development of wastewater surveillance.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Delgado Vela, Jeseth and McClary-Gutierrez, Jill S. and Al-Faliti, Mitham and Allan, Vajra and Arts, Peter and Barbero, Roberto and Bell, Cristalyne and DSouza, Nishita and Bakker, Kevin and Kaya, Devrim and Gonzalez, Raul and Harrison, Katherine and Kan "Impact of Disaster Research on the Development of Early Career Researchers: Lessons Learned from the Wastewater Monitoring Pandemic Response Efforts" Environmental Science & Technology , v.56 , 2022 https://doi.org/10.1021/acs.est.2c01583 Citation Details
McClary-Gutierrez, Jill S. and Aanderud, Zachary T. and Al-faliti, Mitham and Duvallet, Claire and Gonzalez, Raul and Guzman, Joe and Holm, Rochelle H. and Jahne, Michael A. and Kantor, Rose S. and Katsivelis, Panagis and Kuhn, Katrin Gaardbo and Langan, "Standardizing data reporting in the research community to enhance the utility of open data for SARS-CoV-2 wastewater surveillance" Environmental Science: Water Research & Technology , 2021 https://doi.org/10.1039/D1EW00235J Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

Wastewater monitoring is an approach to track diseases within a community via monitoring for fragments of the causative pathogen in wastewater. While this approach has been applied for decades, it was recognized early in the COVID-19 pandemic that infected individuals excrete the SARS-CoV-2 genome into wastewater and thus provide a potential approach to track the disease within communities. This was and continues to be an attractive approach as it does not rely on clinical testing capacity or reporting, and thus provides an independent and anonymous reporting of community disease trends. Following the initial recognition of the potential for wastewater monitoring for COVID-19, there were significant technical developments that were necessary to make this vision a reality. Scientists globally worked collaboratively to address these challenges. This Research Coordination Network (RCN) provided a space for this collaboration through several different activities, including virtual conferences, webinars, workshops, student training videos, sample sharing, and communication networks, among others. These activities and tools were attended and used by thousands of participants throughout the course of the RCN and provided significant opportunities for data and method sharing as well as student training. The results of this RCN continue to support wastewater monitoring for public health protection, both for COVID-19 as well as other diseases.


Last Modified: 04/20/2022
Modified by: Kyle Bibby

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