Using Detrending to Assess SARS-CoV-2 Wastewater Loads as a Leading Indicator of Fluctuations in COVID-19 Cases at Fine Temporal Scales: Correlations Across Twenty Sewersheds in North Carolina

Kelly Hoffman, David Holcomb , Stacie Reckling, Thomas Clerkin, Denene Blackwood, Rachelle Beattie , Francis de los Reyes , Angela Harris, Helena Mitasova, Nadine Kotlarz , Jill Stewart, Jacob Kazenelson, Lawrence Cahoon, Arthur Frampton, Mariya Munir , Allison Lee , Steven Berkowitz , Rachel Noble, Virginia Guidry , Lawrence Engel , Marc Serre, Ariel Christensen 

Wastewater surveillance emerged during the COVID-19 pandemic as a novel strategy for tracking the burden of illness in communities. Previous work has shown that trends in wastewater SARS-CoV-2 viral loads correlate well with reported COVID-19 case trends over longer time periods (i.e., months). We used detrending time series to reveal shorter sub-trend patterns (i.e., weeks) to identify leads or lags in the temporal alignment of the wastewater/case relationship. Daily incident COVID-19 cases and twice-weekly wastewater SARS-CoV-2 viral loads measured at 20 North Carolina sewersheds in 2021 were detrended using smoothing ranges of ∞, 16, 8, 4 and 2 weeks, to produce detrended cases and wastewater viral loads at progressively finer time scales. For each sewershed and smoothing range, we calculated the Spearman correlation between the cases and the wastewater viral loads with offsets of -7 to +7 days. We identified a conclusive lead/lag relationship at 15 of 20 sewersheds, with detrended wastewater loads temporally leading detrended COVID-19 cases at 11 of these sites. For the 11 leading sites, the correlation between wastewater loads and cases was greatest for wastewater loads sampled at a median lead time of 6 days before the cases were reported. Distinct lead/lag relationships were the most pronounced after detrending with smoothing ranges of 4–8 weeks, suggesting that SARS-CoV-2 wastewater viral loads can track fluctuations in COVID-19 case incidence rates at fine time scales and may serve as a leading indicator in many settings. These results could help public health officials identify, and deploy timely responses in, areas where cases are increasing faster than the overall pandemic trend.