Prewhitening and normalization help detect a strong cross-correlation between daily wastewater SARS-CoV-2 RNA abundance and COVID-19 cases in a community

Wastewater surveillance is a promising technology for real-time tracking and even early detection of COVID-19 infections in a community. Although correlation analysis between wastewater surveillance data and the daily clinical COVID-19 case numbers has been frequently conducted, the importance of stationarity of the time series data has not been well addressed. In this study, we demonstrated that strong yet spurious correlation could arise from non-stationary time series data in wastewater surveillance. Data prewhitening to remove trends by the first differences of values between two consecutive times helped to reveal distinct cross-correlation patterns between daily clinical case numbers and daily wastewater SARS-CoV-2 RNA abundance during a lockdown period in 2020 in Honolulu, Hawaii. Normalization of wastewater SARS-CoV-2 RNA concentration by the endogenous fecal viral markers in the same samples significantly improved the cross-correlation, and the best correlation was detected at a two-day lag of the daily clinical case numbers. The detection of a significant correlation between the daily wastewater SARS-CoV-2 RNA abundance and the clinical case numbers also suggests that disease burden fluctuation in the community should not be excluded as a contributor to the often observed weekly cyclic patterns of clinical cases.

Automated summary

Wastewater surveillance is a promising technology for real-time tracking and early detection of COVID-19 infections. However, the importance of stationarity in time series data has been overlooked. This study demonstrates that non-stationary time series data in wastewater surveillance can lead to strong yet spurious correlations. The use of data prewhitening and normalization improved the cross-correlation between daily clinical case numbers and wastewater SARS-CoV-2 RNA abundance.