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. 2020 Nov 1:186:116296.
doi: 10.1016/j.watres.2020.116296. Epub 2020 Aug 13.

COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology

Raul Gonzalez  1 , Kyle Curtis  2 , Aaron Bivins  3 , Kyle Bibby  3 , Mark H Weir  4 , Kathleen Yetka  2 , Hannah Thompson  2 , David Keeling  2 , Jamie Mitchell  2 , Dana Gonzalez  2
Affiliations

COVID-19 surveillance in Southeastern Virginia using wastewater-based epidemiology

Raul Gonzalez et al. Water Res. .

Abstract

Wastewater-based epidemiology (WBE) has been used to analyze markers in wastewater treatment plant (WWTP) influent to characterize emerging chemicals, drug use patterns, or disease spread within communities. This approach can be particularly helpful in understanding outbreaks of disease like the novel Coronavirus disease-19 (COVID-19) when combined with clinical datasets. In this study, three RT-ddPCR assays (N1, N2, N3) were used to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in weekly samples from nine WWTPs in southeastern Virginia. In the first several weeks of sampling, SARS-CoV-2 detections were sporadic. Frequency of detections and overall concentrations of RNA within samples increased from mid March into late July. During the twenty-one week study, SARS-CoV-2 concentrations ranged from 101 to 104 copies 100 mL-1 in samples where viral RNA was detected. Fluctuations in population normalized loading rates in several of the WWTP service areas agreed with known outbreaks during the study. Here we propose several ways that data can be presented spatially and temporally to be of greatest use to public health officials. As the COVID-19 pandemic wanes, it is likely that communities will see increased incidence of small, localized outbreaks. In these instances, WBE could be used as a pre-screening tool to better target clinical testing needs in communities with limited resources.

Keywords: COVID-19; RT-ddPCR; SARS-CoV-2; Wastewater-based epidemiology.

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Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Image, graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Documented cases of COVID-19 by city/county in southeastern Virginia for the study period. Panel ‘a’ presents total confirmed cases. Panel ‘b’ represents total cases normalized by each city's population and plotted as a percent.
Fig. 2
Fig. 2
SARS-CoV-2 detections for each assay (N1, N2, N3) by sample date. Treatment facilities are noted on the x axis of panel ‘a’. Panel ‘b’ shows total detection by date for each assay. Panel ‘c’ represents total detection of all assays for each sample date.
Fig. 3
Fig. 3
Population normalized SARS-CoV-2 loading for each facility. Filled dots indicated samples greater than the limit of detection, hollow dots indicate samples below the limit of detection.
Fig. 4
Fig. 4
Population normalized SARS-CoV-2 loading (log10 copies/person) overlaid onto the respective facility catchment. Filled polygons represent discrete catchments for each of the nine sampled treatment plants.
Fig. 5.
Fig. 5
SARS-CoV-2 loading (copies) with LOWESS smoothing for the studied region over 21 weeks.
See this image and copyright information in PMC

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