Implementing building-level SARS-CoV-2 wastewater surveillance on a university campus

Abstract

The COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long-term care facilities, schools, and residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater-based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university's fall residential semester. In multiple cases, wastewater surveillance enabled the identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in the identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with resident populations of 150-200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.

Keywords: Epidemiology; Mitigation; SARS-CoV-2; Wastewater.

Graphical abstract

Fig. 1

Residential areas of UNC Charlotte Main Campus monitored in this study. Residential buildings are marked in yellow. Left panel: north campus residential areas. Right panel: South Village residential area.

Fig. 2

Autosampler setup for surface manholes and cleanout cap locations.

Fig. 3

Weekly sample collection and processing timeline for the UNC Charlotte wastewater surveillance project.

Fig. 4

Overview of sample collection and detection outcomes over the 8-week on-campus session.

Fig. 5

Total number of SARS-CoV-2 positive wastewater samples from UNCC dormitories (red, right axis) and daily new positive cases in Mecklenburg county (blue, left axis) from September 28 to November 23, 2020. Curves are fit to the data using Loess Smoothing. Pearson correlation coefficient between the two series is 0.769.

Fig. 6

Timeline of campus actions after the first positive wastewater event on the UNC Charlotte campus, Sept. 30, 2020.

Fig. 7

Number of positive cases detected in wastewater-triggered surge testing events vs. number of cases in other reported campus clusters during the fall semester (Sept. 1-Nov. 23, 2020). Numbers are extracted from publicly available reports (Niner Notices, (“Latest NinerNotices,” n.d.)) made available to the University community and do not include students who reported late for required surge testing or cases that evaded surge testing.

Fig. 8

Decision tree representation of the process used by UNC Charlotte administrative group when deciding whether or not to follow up positive wastewater signal with surge testing.