SARS-CoV-2 RNA wastewater settled solids surveillance frequency and impact on predicted COVID-19 incidence using a distributed lag model

Abstract

AbstractSARS-CoV-2 RNA concentrations in wastewater settled solids correlate well with COVID-19 incidence rates (IRs). Here, we develop distributed lag models (DLMs) to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and PMMoV RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced datasets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-Nov 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root mean square error (RMSE) changed less than 7 cases/100,000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small error, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases.