Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States

Yuehan Ai; Angela Davis; Dan Jones; Stanley Lemeshow; Huolin Tu; Fan He; Peng Ru; Xiaokang Pan; Zuzana Bohrerova; Jiyoung Lee

doi:10.1016/j.scitotenv.2021.149757

Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States

Sci Total Environ. 2021 Dec 20:801:149757. doi: 10.1016/j.scitotenv.2021.149757. Epub 2021 Aug 19.

Authors

Yuehan Ai¹, Angela Davis², Dan Jones³, Stanley Lemeshow⁴, Huolin Tu⁵, Fan He¹, Peng Ru⁵, Xiaokang Pan⁶, Zuzana Bohrerova⁷, Jiyoung Lee⁸

Affiliations

¹ Department of Food Science and Technology, The Ohio State University, Columbus, OH, USA.
² Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, USA.
³ The Ohio State University Comprehensive Cancer Center and James Cancer Center, Columbus, OH, USA; James Molecular Laboratory, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
⁴ Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.
⁵ The Ohio State University Comprehensive Cancer Center and James Cancer Center, Columbus, OH, USA.
⁶ James Molecular Laboratory, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
⁷ Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus, OH, USA.
⁸ Department of Food Science and Technology, The Ohio State University, Columbus, OH, USA; Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, USA. Electronic address: lee.3598@osu.edu.

Abstract

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.

Keywords: B.1.427/429; D614G; N501Y; PMMoV; Quadratic polynomial model; Wastewater-based epidemiology; crAssphage.

MeSH terms

COVID-19*
Humans
Ohio
SARS-CoV-2*
Wastewater
Wastewater-Based Epidemiological Monitoring

Substances

Waste Water