Rapid, Large-Scale Wastewater Surveillance and Automated Reporting System Enable Early Detection of Nearly 85% of COVID-19 Cases on a University Campus
Abstract
Wastewater-based surveillance has gained prominence and come to the forefront as a leading indicator of forecasting COVID-19 (coronavirus disease 2019) infection dynamics owing to its cost-effectiveness and its ability to inform early public health interventions. A university campus could especially benefit from wastewater surveillance, as universities are characterized by largely asymptomatic populations and are potential hot spots for transmission that necessitate frequent diagnostic testing. In this study, we employed a large-scale GIS (geographic information systems)-enabled building-level wastewater monitoring system associated with the on-campus residences of 7,614 individuals. Sixty-eight automated wastewater samplers were deployed to monitor 239 campus buildings with a focus on residential buildings. Time-weighted composite samples were collected on a daily basis and analyzed on the same day. Sample processing was streamlined significantly through automation, reducing the turnaround time by 20-fold and exceeding the scale of similar surveillance programs by 10- to 100-fold, thereby overcoming one of the biggest bottlenecks in wastewater surveillance. An automated wastewater notification system was developed to alert residents to a positive wastewater sample associated with their residence and to encourage uptake of campus-provided asymptomatic testing at no charge. This system, integrated with the rest of the "Return to Learn" program at the University of California (UC) San Diego-led to the early diagnosis of nearly 85% of all COVID-19 cases on campus. COVID-19 testing rates increased by 1.9 to 13× following wastewater notifications. Our study shows the potential for a robust, efficient wastewater surveillance system to greatly reduce infection risk as college campuses and other high-risk environments reopen.
Additional Information
We thank UC San Diego's Return to Learn (RTL) program for funding the campus-wide wastewater surveillance efforts. We also thank Robert M. Neuhard and the rest of the RTL leadership team and Bradley Sollenberger of the Operational Strategic Initiatives (OSI) team for ensuring the smooth functioning of the program along with the EXCITE (EXpedited COVID-19 IdenTification Environment) CLIA lab at UCSD and the SEARCH (San Diego Epidemiology and Research for COVID Health) Alliance for processing all campus diagnostic tests. We also thank Jason Kayne, Rich Cota, Jesus Ortiz, and the Facilities management team (FM) at UCSD, Joseph Mayer from the Center for Aerosol Impacts on Chemistry of the Environment (CAICE) and Luke Arnold of the Campus Research Machine Shop (CRMS) for assistance with the installation and operation of the autosamplers; Robbie Jacobs, Shawn Knepple and their team at UCSD Logistics for assisting with our daily sampling efforts; Christopher Longhurst and the UC San Diego Health Information Services team; Brett Pollak and the UCSD Information Technology Services team for assisting with the daily notifications; Alysson M. Satterlund, Angela Song, Angela Scioscia, and Elizabeth H. Simmons of Academic Affairs for contact tracing and targeted campus messaging assistance; Jana Severson, Patrick Hochstein, Hemlata Jhaveri, the UCSD HDH team, and the UCSD Environmental Health and Safety (EHS) personnel; Jack Gilbert and the Microbiome Sample Processing Core at UC San Diego for access to qPCR equipment.Additional details
- PMCID
- PMC8409724
- Eprint ID
- 118356
- Resolver ID
- CaltechAUTHORS:20221215-540495000.23
- University of California, San Diego
- Created
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2022-12-16Created from EPrint's datestamp field
- Updated
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2022-12-16Created from EPrint's last_modified field
- Caltech groups
- COVID-19