Rapid Implementation of High-Frequency Wastewater Surveillance of SARS-CoV-2

Abstract

There have been over 507 million cases of COVID-19, the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in 6 million deaths globally. Wastewater surveillance has emerged as a valuable tool in understanding SARS-CoV-2 burden in communities. The National Wastewater Surveillance System (NWSS) partnered with the United States Geological Survey (USGS) to implement a high-frequency sampling program. This report describes basic surveillance and sampling statistics as well as a comparison of SARS-CoV-2 trends between high-frequency sampling 3–5 times per week, referred to as USGS samples, and routine sampling 1–2 times per week, referred to as NWSS samples. USGS samples provided a more nuanced impression of the changes in wastewater trends, which could be important in emergency response situations. Despite the rapid implementation time frame, USGS samples had similar data quality and testing turnaround times as NWSS samples. Ensuring there is a reliable sample collection and testing plan before an emergency arises will aid in the rapid implementation of a high-frequency sampling approach. High-frequency sampling requires a constant flow of information and supplies throughout sample collection, testing, analysis, and data sharing. High-frequency sampling may be a useful approach for increased resolution of disease trends in emergency response.

Identifying trends in SARS-CoV-2 RNA in wastewater to infer changing COVID-19 incidence: Effect of sampling frequency

“…Alternatively, health departments could report trends based on the duration over which a trend occurs instead of the likelihood of a trend. A study by Holst et al classified trends in wastewater SARS-CoV-2 RNA concentrations as sustained increase, increase, plateau, decrease, or sustained decrease [25].…”

“…When analyzing time-series data for trends, a large number of observations provides greater statistical power [24], and high frequency (e.g., daily) data has previously been identified as ideal for WBE to most accurately identify trends [25]. However, daily sampling and processing of wastewater can be challenging to implement [18,20], and many facilities across the United States only collect samples once per week [26,27].…”

Identifying trends in SARS-CoV-2 RNA in wastewater to infer changing COVID-19 incidence: Effect of sampling frequency

“…Alternatively, health departments could report trends based on the duration over which a trend occurs instead of the likelihood of a trend. A study by Holst et al classified trends in wastewater SARS-CoV-2 RNA concentrations as sustained increase, increase, plateau, decrease, or sustained decrease [25]. Sustained versus unsustained trends were differentiated based on the number of samples over which a statistically significant slope occurred (five samples for sustained trends and three samples for unsustained trends) [25].…”

“…A study by Holst et al classified trends in wastewater SARS-CoV-2 RNA concentrations as sustained increase, increase, plateau, decrease, or sustained decrease [25]. Sustained versus unsustained trends were differentiated based on the number of samples over which a statistically significant slope occurred (five samples for sustained trends and three samples for unsustained trends) [25]. Although beyond the scope of this study, the criteria used to classify trends could be amended to consider the duration over which trends occur.…”

“…When analyzing time-series data for trends, a large number of observations provides greater statistical power [24], and high frequency (e.g., daily) data has previously been identified as ideal for WBE to most accurately identify trends [25]. However, daily sampling and processing of wastewater can be challenging to implement [18,20], and many facilities across the United States only collect samples once per week [26,27].…”