RAPID COVID-19 DCL response: Wastewater Pathogen Tracking Dashboard
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$197,375Funder
National Science Foundation (NSF)Principal Investigator
Rachel SpurbeckResearch Location
United States of AmericaLead Research Institution
Battelle Memorial InstituteResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Environmental stability of pathogen
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Monitoring the spread of COVID-19 within communities is essential to enable outbreak control measures such as social distancing or contact tracing to be effective. The goal of this project is to build a system to detect and quantify COVID-19 from city wastewater to identify neighborhoods that are at highest risk as the virus spreads. Low or undetectable COVID-19 counts are expected to be observed in wastewater from neighborhoods where the outbreak is under control, whereas they will be higher in regions where social distancing or contact tracing is needed to stop viral spread. This tracking system is adaptable to other pathogens that cause outbreaks of public health concern as well, and it will help ensure public safety as the economy is re-opening and afterwards by detecting second-wave outbreaks of which the public should be aware. This tracking system will provide real time insight into community spread and prevalence of COVID-19 by building risk models from wastewater data and comparing those to models built from other public health data. A broader impact from this research will be the development of a publicly accessible, web-based Wastewater Pathogen Tracking Dashboard (WPTD).
Several studies have demonstrated that the virus that causes COVID-19, SARS-CoV-2, is detectable in human waste and in the influent of wastewater treatment plants using diagnostic techniques such as qPCR. Compared to traditional public health risk estimation models, sampling of wastewater offers a more immediate and passive approach to population surveillance that can be tied to source tracing and socioeconomic impacts without depending on an already overburdened healthcare system. This work will go beyond the state of the art to include virome sequencing to determine prevalence of SARS-CoV-2 and other viral pathogens and long read sequencing from four locations to quantify and detect viral mutations, that may correlate with differential disease severity. The project will produce a predictive risk model to identify neighborhoods where contact tracing should be implemented due to high abundance of SARS-CoV-2 in the wastewater relative to the number of confirmed cases. This model will be developed and compared to models built from public health data to enable prediction of neighborhoods that have cleared the virus or are having new outbreaks. Virome analysis will enable extension to other pathogens of public concern and development of a dashboard for data presentation to public officials to enable informed policy decisions regarding pandemic response.
This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act
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.
Several studies have demonstrated that the virus that causes COVID-19, SARS-CoV-2, is detectable in human waste and in the influent of wastewater treatment plants using diagnostic techniques such as qPCR. Compared to traditional public health risk estimation models, sampling of wastewater offers a more immediate and passive approach to population surveillance that can be tied to source tracing and socioeconomic impacts without depending on an already overburdened healthcare system. This work will go beyond the state of the art to include virome sequencing to determine prevalence of SARS-CoV-2 and other viral pathogens and long read sequencing from four locations to quantify and detect viral mutations, that may correlate with differential disease severity. The project will produce a predictive risk model to identify neighborhoods where contact tracing should be implemented due to high abundance of SARS-CoV-2 in the wastewater relative to the number of confirmed cases. This model will be developed and compared to models built from public health data to enable prediction of neighborhoods that have cleared the virus or are having new outbreaks. Virome analysis will enable extension to other pathogens of public concern and development of a dashboard for data presentation to public officials to enable informed policy decisions regarding pandemic response.
This RAPID award is made by the Ecology and Evolution of Infectious Diseases Program in the Division of Environmental Biology, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act
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.