Predictive values of time-dense SARS-CoV-2 wastewater analysis in university campus buildings.

Wastewater-based SARS-CoV-2 surveillance on college campuses has the ability to detect individual clinical COVID-19 cases at the building-level. High concordance of wastewater results and clinical cases has been observed when calculated over a time window of four days or longer and in settings with high incidence of infection. At Duke University, twice a week clinical surveillance of all resident undergraduates was carried out in the spring 2021 semester. We conducted simultaneous wastewater surveillance with daily frequency on selected residence halls to assess wastewater as an early warning tool during times of low transmission with the hope of scaling down clinical test frequency. We evaluated the temporal relationship of the two time-dense data sets, wastewater and clinical, and sought a strategy to achieve the highest wastewater predictive values using the shortest time window to enable timely intervention. There were 11 days with clinical cases in the residence halls (80-120 occupants) under wastewater surveillance with 5 instances of a single clinical case and 3 instances of two clinical cases which also corresponded to a positive wastewater SARS-CoV-2 signal. While the majority (71%) of our wastewater samples were negative for SARS-CoV-2, 29% resulted in at least one positive PCR signal, some of which did not correlate with an identified clinical case. Using a criteria of two consecutive days of positive wastewater signals, we obtained a positive predictive value (PPV) of 75% and a negative predictive value of 87% using a short 2 day time window for agreement. A conventional concordance over a much longer 4 day time window resulted in PPV of only 60%. Our data indicated that daily wastewater collection and using a criteria of two consecutive days of positive wastewater signals was the most predictive approach to timely early warning of COVID-19 cases at the building level.

Duke Scholars

Author David Singleton Civil and Environmental Engineering

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Author Brian R. Stoner Electrical and Computer Engineering

Author Claudia K. Gunsch Civil and Environmental Engineering

Author Sonia Grego Electrical and Computer Engineering