Duane Graves Co-Authors Article on COVID-19 Wastewater Epidemiology
Duane Graves, Ph.D. (Tennessee) coauthored an article entitled “COVID-19 Wastewater Epidemiology: A Model to Estimate Infected Populations” published as a preprint in medRxiv on November 7, 2020.
The article describes a model to provide an estimate of the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day. This overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to better inform policy decisions.
Duane’s coauthors were Christopher Steven McMahan, Stella Self, Lior Rennert, Corey Kalbaugh, David Kriebel, Jessica A. Deaver, Sudeep Popat, Tanju Karanfil, and David L. Freedman.
Duane Graves is a Senior Principal Scientist based in Tennessee with more than 30 years of experience focused on environmental biotechnology; environmental forensics; in situ groundwater, soil, and sediment remediation; evaluation of airborne biological contaminants; and remediation of groundwater in karst formations.
medRxiv is a free online archive and distribution server for complete but unpublished manuscripts (preprints) in the medical, clinical, and related health sciences. Preprints are preliminary reports of work that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
BACKGROUND: Wastewater-based epidemiology (WBE) provides an opportunity for near real-time, cost-effective monitoring of community level transmission of SARS-CoV-2, the virus that causes COVID-19. Detection of SARS-CoV-2 RNA in wastewater can identify the presence of COVID-19 in the community, but methods are lacking for estimating the numbers of infected individuals based on wastewater RNA concentrations.
METHODS: Composite wastewater samples were collected from three sewersheds and tested for SARS-CoV-2 RNA. A Susceptible-Exposed-Infectious-Removed (SEIR) model based on mass rate of SARS-CoV-2 RNA in the wastewater was developed to predict the number of infected individuals. Predictions were compared to confirmed cases identified by the South Carolina Department of Health and Environmental Control for the same time period and geographic area.
RESULTS: Model predictions for the relationship between mass rate of virus release to the sewersheds and numbers of infected individuals were validated based on estimated prevalence from individual testing. A simplified equation to estimate the number of infected individuals fell within the 95% confidence limits of the model. The unreported rate for COVID-19 estimated by the model was approximately 12 times that of confirmed cases. This aligned well with an independent estimate for the state of South Carolina.
CONCLUSIONS: The SEIR model provides a robust method to estimate the total number of infected individuals in a sewershed based on the mass rate of RNA copies released per day. This overcomes some of the limitations associated with individual testing campaigns and thereby provides an additional tool that can be used to better inform policy decisions.
Read the article: https://doi.org/10.1101/2020.11.05.20226738
About medRxiv: https://www.medrxiv.org/
Learn more about Duane: https://geosyntec.com/people/duane-graves