High-Throughput Wastewater SARS-CoV-2 Detection Enables Forecasting of Community Infection Dynamics in San Diego County

Abstract

Large-scale wastewater surveillance has the ability to greatly augment the tracking of infection dynamics especially in communities where the prevalence rates far exceed the testing capacity. However, current methods for viral detection in wastewater are severely lacking in terms of scaling up for high throughput. In the present study, we employed an automated magnetic-bead-based concentration approach for viral detection in sewage that can effectively be scaled up for processing 24 samples in a single 40-min run. The method compared favorably to conventionally used methods for viral wastewater concentrations with higher recovery efficiencies from input sample volumes as low as 10 ml and can enable the processing of over 100 wastewater samples in a day. The sensitivity of the high-throughput protocol was shown to detect 1 asymptomatic individual in a building of 415 residents. Using the high-throughput pipeline, samples from the influent stream of the primary wastewater treatment plant of San Diego County (serving 2.3 million residents) were processed for a period of 13 weeks. Wastewater estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome copies in raw untreated wastewater correlated strongly with clinically reported cases by the county, and when used alongside past reported case numbers and temporal information in an autoregressive integrated moving average (ARIMA) model enabled prediction of new reported cases up to 3 weeks in advance. Taken together, the results show that the high-throughput surveillance could greatly ameliorate comprehensive community prevalence assessments by providing robust, rapid estimates.IMPORTANCE Wastewater monitoring has a lot of potential for revealing coronavirus disease 2019 (COVID-19) outbreaks before they happen because the virus is found in the wastewater before people have clinical symptoms. However, application of wastewater-based surveillance has been limited by long processing times specifically at the concentration step. Here we introduce a much faster method of processing the samples and show its robustness by demonstrating direct comparisons with existing methods and showing that we can predict cases in San Diego by a week with excellent accuracy, and 3 weeks with fair accuracy, using city sewage. The automated viral concentration method will greatly alleviate the major bottleneck in wastewater processing by reducing the turnaround time during epidemics.

Keywords: COVID-19; SARS-CoV-2; epidemiology; wastewater.

FIG 1

Tracking infection dynamics in San Diego County. (A) Map showing the San Diego sewer mains (depicted in purple) that feed into the influent stream at the primary wastewater treatment plant (WWTP) at Point Loma. Overlaid are the cumulative cases recorded from the different zip codes in the county during the course of the study. The caseload was counted by cases per zip code from areas draining into the WWTP. The sizes of the circles are proportional to the diagnostic cases reported from each zone, and the color gradient shows the number of cases per 100,000 residents. (B) Daily new cases reported by the county of San Diego. (C) SARS-CoV-2 viral gene copies detected per liter of raw sewage determined from N1 Cq values corrected for PMMoV (pepper mild mottle virus) concentration. All viral concentration estimates were derived from the processing of two sample replicates and two PCR replicates for each sample (error bars show the standard deviations [SD]).

FIG 2

(A) Daily caseload and wastewater viral concentration data shown for a period of 13 weeks, where a spline smoothing is applied to each time series to demonstrate general trends. (B) Predictive model showing the predicted data (yellow) compared to the observed caseload (blue) and the 4-week forecast (red). Data collected from 07/07/2020 to 09/28/2020 were used as the training data set to predict the caseload for the following weeks (up to 10/25/2020). Data (wastewater plus county testing data) gathered from 09/29/2020 to 10/21/2020 were used for model validation. MATLAB Systems Identification toolbox was used to estimate the model order and parameters and calculate the forecasted values.