key: cord-0863537-d92ebwth
authors: Ahmed, Warish; Tscharke, Ben; Bertsch, Paul M.; Bibby, Kyle; Bivins, Aaron; Choi, Phil; Clarke, Leah; Dwyer, Jason; Edson, Janette; Nguyen, Thi Minh Hong; O’Brien, Jake W.; Simpson, Stuart L.; Sherman, Paul; Thomas, Kevin V.; Verhagen, Rory; Zaugg, Julian; Mueller, Jochen F.
title: SARS-CoV-2 RNA monitoring in wastewater as an early warning system for COVID-19 transmission in the community: a temporal case study
date: 2020-12-05
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.144216
sha: eb79bbf743ec1455c3f9080bce23568d1cdc06c9
doc_id: 863537
cord_uid: d92ebwth

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus which causes COVID-19, has spread rapidly across the globe infecting millions of people and causing significant health and economic impacts. Authorities are exploring complimentary approaches to monitor this infectious disease at the community level. Wastewater-based Epidemiological (WBE) approaches to detect SARS-CoV-2 RNA in municipal wastewater are being developed worldwide as an environmental surveillance approach to inform health authority decision-making. Owing to the extended excretion of SARS-CoV-2 RNA in stool, WBE can surveil large populated areas with a longer detection window providing unique information on the presence of pre-symptomatic and asymptomatic cases that are unlikely to be screened by clinical testing. Herein, we analysed SARS-CoV-2 RNA in 24-h composite wastewater samples (n = 63) from three wastewater treatment plants (WWTPs) in Brisbane, Queensland, Australia from 24th of February to 1st of May 2020. A total of 21 samples were positive for SARS-CoV-2, ranging from 135 to 11,992 gene copies (GC)/100 mL of wastewater. Detections were made in a Brisbane South WWTP in late February 2020, up to three weeks before the first cases were reported there. Wastewater samples were generally positive during the period with highest caseload data. The positive SARS-CoV-2 RNA detection in wastewater while there were limited clinical reported cases demonstrates the potential of WBE as an early warning system. When integrated into disease surveillance and monitoring systems, wastewater monitoring data may assist management efforts to identify hotspots and target localised public health responses, such as increased individual testing and the provision of health warnings.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus responsible for coronavirus disease 2019 . The COVID- 19 Wong et al., 2020). Consequently, SARS-CoV-2 RNA can be measured in wastewater to detect and monitor SARS-CoV-2 infection trends among the population in an approach, previously utilized for poliovirus, and now known as wastewater-based epidemiology (WBE) (Paul et al., 1940; Asghar et al., 2014) . Importantly, levels of RNA shedding in stool are independent of COVID19 severity (Zheng et al., 2020) . SARS-CoV-2 RNA has also been detected in the stool despite negative clinical test results on nasopharyngeal swabs and is present in patient stool for a longer period (9-16 days post symptom onset, compared to 6-11 days in nasopharyngeal swabs); hence, monitoring stool may be useful when nasopharyngeal swab is negative . These observations suggest that WBE may afford a symptom clinically diagnosed cases in the country. Detections of SARS-CoV-2 RNA in wastewater was also reported in Milan, Italy, within a few days of the first national clinical case (La ; in Brisbane, Australia, when the number of clinical cases in the region were in the hundreds (Ahmed et al. 2020a ); in secondary effluent in Yamanashi Prefecture, Japan, when reported cases were at their peak Interestingly, Medema and colleagues detected an RNA target associated with coronaviruses in wastewater from a city in the Netherlands six days before the first clinical cases were reported (Medema et al., 2020) , and another study from the northeastern USA reported that viral titers in wastewater indicated infections were significantly higher than those that were clinically detected . It is important to note that these studies measure fragments of viral RNA. While fecal transmission cannot be ruled out, SARS-CoV-2 in untreated wastewater were found to be not infective (Rimoldi et al., 2020) .

Despite the flood of reported SARS-CoV-2 RNA detections in wastewater, little has been documented on the performance of virus concentration and detection methods (O"Reilly et al., 2020). A modeling exercise suggested that wastewater surveillance would theoretically be able to detect 1 shedder in a catchment of 2,000,000 people in the best case scenario, but noted limitations including uncertainties around RNA signal decay in wastewater, which have been shown to be dependent on temperatures and residence times en route to sampling points (Hart and Halden, 2020; Ahmed et al., 2020c; Ahmed et al., 2020d). Furthermore, 30-90% of infected individuals have reported shedding the virus accompanied by highly variable shedding rates (0.8 to 7.5 log 10 /g of feces). Therefore, predicting the number of shedders in a catchment may be difficult. A preprint has reported a detection limit of 1 shedder in 1,000 people to 2 shedders per strategies and direct public health intervention outcomes remains tenuous with the Water Research Foundation describing outbreak detection as "very feasible", outbreak tapering as "somewhat feasible", and prevalence assessment as "may or may not be feasible" (WRF, 2020).

At present, the efficacy of WBE for SARS-CoV-2 surveillance and monitoring has yet to be fully evaluated, and is limited by the short-term and cross-sectional nature of the published studies which often report RNA titers in wastewater during the exponential growth phase of an SARS-CoV-2 RNA is stable for days in untreated wastewater at temperatures ranging from 4 to 37°C, permitting reliable detection following sample collection and processing . These observations indicate that during a SARS-CoV-2 epidemic, the RNA signal in wastewater will likely build, plateau, and taper in distinct manners that will make interpretation of the signal challenging. The aim of this study was to investigate whether SARS-CoV-2 RNA can be detected and quantified in wastewater collected from three wastewater treatment plants (WWTPs), and to compare the frequency and variability of detection with COVID-19 case number data. We report a longitudinal study of SARS-CoV-2 RNA quantities in wastewater paired with clinical diagnoses of COVID-19 from 24/02/2020 to 01/05/2020 in Brisbane, Queensland, Australia during both the growth, plateau, and tapering of an epidemic of COVID-19 and compare this to the limited available data from other major Australian capital cities, Sydney and Melbourne; a Queensland regional centre, Townsville; two Queensland tourist areas, Cannonvale-Airlie Beach area of the Whitsundays and Hervey Bay; and a popular wine tourist region, the Barossa Valley in South Australia. 

Viruses were concentrated from these wastewater samples (100-200 mL based on turbidity) using adsorption-extraction with electronegative membrane as previously described (Ahmed et al., 2020a) . RNA was directly extracted from the electronegative membrane using a RNeasy PowerMicrobiome Kit ( Qiagen, Hilden, Germany) with slight modification. A 2 mL glass bead beating tube was replaced with a 5-mL bead tube containing garnet beads. This was done to accommodate the electronegative membrane followed by adding 990 µL of buffer PM1 and 10 µL of β-mercaptoethanol (Sigma-Aldrich, Australia). A tissue homogenizer (Precellys 24, Bertin Technologies, France) was used to homogenize the samples, in which homogenization occurred for 3 × 20 s cycles at 10,000 rpm with a 10 s pause between cycles. Homogenization did not within 7 days of RNA extraction. Laboratory personnel wore personal protective equipment (PPE) such as gloves, coats, safety glasses, face masks, and face shields during wastewater sample processing. Filtered wastewater samples were treated with 10% bleach and discarded in the sink as per the activity risk assessment for this project.

Published RT-qPCR assays that target different regions of the SARS-CoV-2 genome, specifically For each RT-qPCR run, a series of three positive and no template controls were included.

All RT-qPCR data were generated using default settings for baseline and threshold. Data were only collected from instrument runs in which the positive control produced amplification, and no amplified product was observed in the no template control. All instrument runs passed these criteria. A master standard curve with 95% upper and lower confidence intervals was generated for each assay. The log 10 -linear regression of copy number and corresponding quantification cycle J o u r n a l P r e -p r o o f Journal Pre-proof (Cq) values (derived from the 6-point, assay gBlock 1:10 serial dilution series) measured in triplicate from three qPCR instrument runs were used to generate the master standard curve.

For each sample replicate, the SARS-CoV-2 RNA concentration (copies/reaction) was calculated from the master standard curve and accounts for the difference in nucleic acid type between the double-stranded oligonucleotide used to generate the standard curve and the singlestranded genome of SARS-CoV-2 (i.e., divide by 2) (Bustin et al., 2009). The assay limit of detection (ALOD) for the different assays used was defined as the minimum copy number with a 95% probability of detection as previously described (Verbyla et al., 2016) . The process limit of detection (PLOD) was calculated by dividing the ALOD by the RNA template volume added to the RT-qPCR well and then multiplying this number by the total volume of RNA extracted from each sample to yield the total RNA gene copies (GC). This number was then normalised to total sample volume processed to yield the PLOD of SARS-CoV-2 RNA GC/100 mL. Since an RNA sample still can yield RT-qPCR positive signal above the ALOD, we used a cut-off <45 Cq to determine positive samples.

An experiment was conducted to determine the presence of PCR inhibition in nucleic acid extracted from wastewater samples using a murine hepatitis virus (MHV) RT-qPCR assay Woodcroft, unpublished, https://github.com/wwood/CoverM). Low quality read mappings were removed with CoverM "filter" (minimum identity 95% and minimum aligned length of 90%). Read depth profiles for each sample were calculated using samtools (ver. 1.9) to confirm coverage of the targeted region.

Low risk approval as defined by the National Statement on Ethical Conduct in Human Research was obtained from CSIRO Ethics Committee (reference number 2020_035_LR).

All RNA samples were free from PCR inhibition as determined by the MHV RT-qPCR assay and, were therefore, used for downstream RT-qPCR analysis without dilution (Supplementary Table   ST2 South area prior to the clinical testing of individuals being available or implemented widely ( Fig.   2A and Fig. 2C ). The decay of the 7-day average detections in mid-April (9 th to 21 st April, Fig. 2B) align with the decrease of active cases (Fig. 2C) , and may align with a 2-3 week lag in daily cases (24 th to 28 th March peak in daily cases vs peak of 7-day average detections, April 12 th ). As might be expected, the 7-day average wastewater detections tracked similarly to the 7-day average GC/100 mL; however, the GC/100 mL did not appear to agree well with daily case numbers ( Supplementary Fig. SF1 ). Unfortunately, active case numbers were only available from 9 th April, after the peak in daily cases, meaning there were only three days with data overlap with the quantified SARS-CoV-2 detections in wastewater. For those days the ratio of GC/100 mL to number of active cases were 1.7, 1.8, and 2.9. 

The current study immediately followed our proof-of-concept study, where we reported the  The observed prevalence of SARS-CoV-2 RNA in WWTP influent varied between wastewater catchments perhaps due to large differences in catchment size, pipe networks, wastewater characteristics, and subsequently, hydraulic retention times.

 Early detection of SARS-CoV-2 in wastewater is particularly useful for COVID-19 management, and will aid in health messaging, warnings, setting up pop-up testing clinics of individuals to detect and minimize potential second or third waves of the pandemic. J o u r n a l P r e -p r o o f 

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We thank Amber Migus and Shane Neilson for assisting with sample collection. We thank Queensland Urban Utilities for their assistance in sampling.

The authors have declared no conflicts of interest

The authors did not receive any funding for this project. 

The authors declare no conflict of intereset J o u r n a l P r e -p r o o f