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 communities. 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, and data prewhitening to remove trends helped to reveal distinct cross-correlation patterns between daily clinical case numbers and daily wastewater SARS-CoV-2 concentration during a lockdown period in 2020 in Honolulu, Hawaii. Normalization of wastewater SARS-CoV-2 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 daily wastewater SARS-CoV-2 RNA abundance and 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. Water impactWastewater surveillance represents an emerging water technology with significant human health benefits. The study demonstrated that non-stationary time-series data could lead to spurious correlation, highlighting the need for prewhitening. Normalization strategies could alleviate variations in sample collection and analyses, which is useful for detecting actual underlying relationships between wastewater surveillance data and clinical data.