A sensitive, simple, and low-cost method for COVID-19 wastewater surveillance at an institutional level

• DOI: 10.1016/j.scitotenv.2021.151047
• Publication Date: 2021-10-19T13:22:30Z
• Authors: Pengbo Liu; Makoto Ibaraki; Jamie VanTassell; Kelly Geith; Matthew Cavallo; Rebecca Kann; Lizheng Guo; Christine L. Moe
• Keywords: Wastewater-Based Epidemiological Monitoring; 0303 health sciences; 03 medical and health sciences; SARS-CoV-2; COVID-19; Humans; RNA, Viral; Wastewater; Article; 6. Clean water; 3. Good health

SARS-CoV-2 is a respiratory virus, but it is also detected in a significant proportion of fecal samples from COVID-19 cases. Recent studies have shown that wastewater surveillance can be a low-cost tool compared to massive diagnostic testing for tracking COVID-19 outbreaks in communities, but most studies have focused on sampling from wastewater treatment plants. Institutional level wastewater surveillance may serve well for early warning purposes because specific geographic areas/populations with emerging cases can be tracked and immediate action can be executed in the event of a positive wastewater signal. In this study, a novel Moore swab method was developed and used for wastewater surveillance of COVID-19 at an institutional level. Of the 442 swab samples tested, 148 (33.5%) swabs collected from the three campuses and two buildings were positive for SARS-CoV-2 RNA. Further study of the quarantine building with a known number of cases indicated that this method was sensitive enough to detect few cases in the building. In addition, comparison between grab samples and Moore swab samples from the hospital sewage line indicated that Moore swabs were more sensitive than grab samples and offer a simple, inexpensive method for obtaining a composite sample of virus in wastewater over a 24-48 h period. These results suggest that collection and analyses of Moore swabs for SARS-CoV-2 RNA detection is a sensitive, low-cost, and easy to use tool for COVID-19 surveillance that is useful for institutional settings and could be deployed in low-resource settings to identify emerging COVID-19 clusters in communities.