Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

Alasdair Cohen; Ayella Maile-Moskowitz; Christopher Grubb; Raul A Gonzalez; Alessandro Ceci; Amanda Darling; Laura Hungerford; Ronald D Fricker Jr; Carla V Finkielstein; Amy Pruden; Peter J Vikesland

doi:10.1021/acsestwater.2c00059

Subsewershed SARS-CoV-2 Wastewater Surveillance and COVID-19 Epidemiology Using Building-Specific Occupancy and Case Data

ACS ES T Water. 2022 May 13;2(11):2047-2059. doi: 10.1021/acsestwater.2c00059. eCollection 2022 Nov 11.

Authors

Affiliations

¹ Department of Population Health Sciences, Virginia Tech, Blacksburg, Virginia 24061, United States.
² Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States.
³ Department of Statistics, Virginia Tech, Blacksburg, Virginia 24061, United States.
⁴ Hampton Roads Sanitation District, Virginia Beach, Virginia 23455, United States.
⁵ Molecular Diagnostics Laboratory, Fralin Biomedical Research Institute, Virginia Tech, Roanoke, Virginia 24016, United States.
⁶ Integrated Cellular Responses Laboratory, Fralin Biomedical Research Institute at VTC, Roanoke, Virginia 24016, United States.
⁷ Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia 24061, United States.

PMID: 37552724
DOI: 10.1021/acsestwater.2c00059

Abstract

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020-2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech's main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident-rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.